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What are the main challenges that I need to solve as a company trying to apply AI to help my business? In this episode, I have a conversation with an organization that provides an AI platform to help you overcome those.   Grant Hey, everybody, welcome to another episode of ClickAI Radio. This is Grant Larsen. And today I have someone that I bumped into the not too long ago, I was at a conference and literally went to their booth and said, Oh, I want to learn more about this technology. I've been tracking your team, your organization, so it's my chance to learn more about it. And so I got to meet a Atalia Horenshtien I hope I said that right. I'm so excited to have Atalia here with me today. So first of all, "A" welcome I tell you and "B" did I say your name right? And then "C" the first question out of the box, explain the meaning behind your name. Atalia Thank you so much for having me. And you actually pronounced the name correctly. So kudos to your best. The meaning behind my name. So first of all, thanks to my parents for choosing such a unique name. It's actually a Hebrew name. I'm originally from, from Israel. And I totally I need Hebrew pronouncing it as a Talia is actually the first claim in Judaism kingdom. So it's a name from the Bible. And it's actually very unique even in Israel. Very cool. Grant Really. The queen in the Bible, I'm gonna have to go look that up. That's awesome. You should. That's very cool. Do we? Do we bow in your presence? Then? Do we do anything like that? Do we do we say Hey, this is me? Atalia No, no, no, no, not not at all. Grant Excellent. Well, thank you. Thank you for taking the time here today. Now as I understand that, I want to make sure I get this right. Your title in your organization is Global Technology Product Advocacy Lead, I actually had to write that down because I can only remember three things. And that's five words in a title, Global Technology Product Advocacy Lead for DataRobot. Did I get that? Right? Atalia Yeah, that's actually correct. Grant What do you do in that role? Atalia So we did a robot. I started as a customer facing data scientist, where I work with customers in different industries, and helping them how to solve complex AI and machine learning problems. And learning from this role, and those use cases. I shifted a bit towards to the advocacy side. So how we tell the technical story of DataRobot, how we educate the market about what's possible. Some of the use cases I implement, and some of the stuff I saw was working on collaboration with our marketing sales. And our customers as well. Grant Okay, got it. So that global part, I think, is critical, because I'm assuming that you go across multiple markets, you're not focused on any one. I gotta believe that gives you a sort of broader industry cross industry view on on AI and machine learning. Is that correct? Atalia Yeah, so I was very lucky to work with different industries in different geographical locations. And obviously, I see a lot of different trends and maturity around AI, where they are in the stage, how are they adopting? What's the process? There, technical knowledge, their technical stage? Yeah. So from United States to Europe to the Middle East. It's really, really interesting. And I'm very happy that I have the opportunity to do so. Grant So that's awesome. What know what got you into this world? What got you into AI and machine learning? Atalia Actually, it's a really interesting path because I started actually the software engineer Not not a, as a data scientist, and over time, obviously the software engineer you work with, with software development, system design, etc, some stuff that you see today in in machine learning operations. And then when I did my masters, I was mostly specific around business intelligence and machine learning. And I learned a lot, it was super interesting. So I took my software skills into a different level. And it's a funny story, because a professor of mine is actually working for DataRobot. And he's one of the main reasons I'm here. Grant Oh, really? Oh, that's interesting. So was the professor already at DataRobot when you were like, Okay, got it. Got it? Atalia Yep, he's still here. His name is Ted Kotler is a world class Person, both on the personality side, and he's a technical knowledge. So I'm very fortunate to work with great people in the company. Grant That's fascinating. I have a similar journey myself, meaning I too, came through the software engineering path, and then sort of stumbled into  through a whole range of things into the whole data science and ML/AI space. So a lot of people certainly do that. But it's sort of a shift in the thinking, isn't it the first time you come into the ML thinking, you're thinking about your data in a much different way. And algorithms and such you're like, wait, okay, I'm solving it. So much different. But I thoroughly enjoy that. All right. So I want to get to some of the things that are unique to, to DataRobot itself. I've worked with multiple AI and ML platforms. And as I mentioned in the intro, I had been watching and looking at DataRobot, watching your organization over time, take on more and more capabilities. When I when you demonstrated the latest capabilities and gave me a sense of where things are going. It started me thinking, what are the main challenges when you think about the AI and ML world? And the problems that are in this space? What are the main challenges that we face? And ultimately, I want to get to what is it that differentiates the way DataRobot does it in the market? But could you first start with what are some of the main challenges you see today, especially with your global perspective, in the AI space? Atalia Yeah. So I think the main main challenge today around the machine learning lifecycle is how you move models into productions and how you make sure your models are still accountable, and accurate with all the factors and new reality that is coming up, right. And get this today, everyone can just build psyche learn model or simple, you know, regression model. But when you work as an organization, and you have different infrastructures, different tools, different skill sets, different personas within your team, and you localize the development side of it, and every model has completely different requirements. So you're getting to inefficient lifecycle. So moving a model from development phase to production is a process that takes usually a lot of time, and it can be super complex. So something that I personally like about there rather than this is something I learned from customers, right that this is the biggest pain today is having an ability to have a platform that will be interoperable and flexible around how to support models that were created in variety of environments and languages, but also how to serve and manage and monitor models that were deployed to different endpoints necessarily to data on production server. Grant So sorry, just interrupt there. That was one of the things when you showed me I was fascinated with which was this ability to bring in models produced from a wide range of platforms and tool sets, if you will, and still bring them into the management aspect that I thought that was a critical characteristic around DataRobot itself. So when when you do that work, when you bring those models in and you manage those, what is it that that you're doing that makes that easier? A it's a it's helpful for me to have one place to bring those together but be what value then does that help me with as I tried to, to update those and refactor those moving forward? Atalia So it includes several other aspects. So first of all, as you mentioned, you have a single place where you can see all your models regardless where they've been created and regardless of the word have been deployed to. So like single pane of glass where you can see everything and, and being able to see at a glance which models are stale, which eventually preventing any risk to your business. Because it's not just about having the visibility to those models. It's also the ability to manage, monitor and govern them. So what is the service health status of this model from the the all endpoints, and what's the accuracy of the model, how it's changed over time, maybe some features that have been drifted. When you see those aspects, it's really helpful for you to understand, maybe you need to retrain your existence project, maybe you need to swipe your existence model with any challenger model, something that is performing better now with the new data that is coming up. Think about COVID That can be a really great example, right? When you train models on certain data set, but then all the reality has changed. All the products you did at that time are irrelevant today with the existence data. Grant Okay, yeah. And this this ability to see the either the drip, like you said, or the staleness of that that's such a critical capability. Is that is that a visual thing? Is it notification based? And how is it that you're being made aware of this? Atalia So it's definitely a visual thing. So you have an ability to see on a specific time when something happened, but you know, we're not expecting from people to go in consistently checking the platform, you can automate the whole process with notifications, if you want to get notified that, let's say, above a specific threshold, you have, under a specific threshold, sorry, you have a drift in accuracy, you would like to get a notification. And you can automate the whole process around retraining, what are the factors for retraining? So really looking to? Where are the areas we can automate the cycle in order to make the life of the user easier? But also, you know, with how much we are saying, Yes, we have AI and we have a donation, you you still need the man or the woman in the middle to approve this process? To be aware about this process, and there is still user intervention for some degree. Grant Okay. All right. So so let's take a scenario. You know, one of the current challenges around AI is this, you know, data bias problem. So what I want to figure out is, let's say in my organization, I've got this bias that comes in, I'm not aware of it at the time, I'm creating the model. There's no intentional harm here. But however, as we get into the production and rollout in its execution, it becomes obvious that the kinds of decisions or insights are certainly leaning a particular direction, what is it that helps me to discover or find that out? And therefore ultimately, correct that? Atalia That's a good question. I think ethical AI is one of the rising topics in AI, right. And mathematically, you can create a model that is not biased. But there are some techniques on how to make sure that the model is more fair, towards your sensitive features. And actually, there are some capabilities in the platform that really helping you not just in production, actually, but also in development, where you can manage bias litigation and tag those have the features and see in your development process already. What's the what's the bias and fairness around this specific project with those features that you chose, and then being able to seeing how the bias and fairness continues towards the production side where you have new data coming in, so you have an ability to target even before and this is helpful for you to understand how to deploy the model into production? And what are the changes that are required in order to keep this model fair. Grant So it's interesting that there's some capabilities in the platform that help you to identify potential bias factors or features along the way, be mindful of that. Let's go to the far right side of this, which is let's say we've done that work. We have the model produced, it's deployed, it's in production. How do I how do I Give feedback. Is there a way to say, A? What are my results of this model? And be? What are my end users potentially feedback on it as well? It's two different kinds of feedback. A is the model given me good insights or guidance? And then be? What about the end users themselves? And how do I get their feedback into there? Atalia Are you asking in general about the model, or specifically with the ethical side of it, Grant Either one, I'm fine with either side of it. Yeah. Atalia I'm still on the ethical side of it. So we provide those visualization tools as part of our ML ops capabilities, to track the bias and fairness on those sensitive features. So you can be certain that decisions that are made with the model are aligned with your technique that you applied during development process. That's, that's one thing. And obviously, if you see any changes over time, you can retrain your model, you can try out different things as part of your development, with our bison furnace techniques. And then overall, the insights you can get from a model, they are divided to two phases. On the development side. You know, in machine learning, we're always talking about the predictions, the predictions, but this is not really the main thing. Yes, we care about the predictions, right. But how we translate those predictions into business actions. Exactly. So having an ability to get that Explainable AI, this is something that I see all the time, especially with the business personas, decision making people, for them, it's really difficult to translate, what is it? Ai model, right? So we provide some graphs to, to present insights and explainability on a model, for example, what are the main factors on a macro level, that contributing to the model? So for example, let's take a churn use case. And you're looking to understand why customers are churning. And it's not just about oh, this customer, the likelihood that this customer will churn is 0.7. What does that mean? So we know what what's the factors, for example, how many times the customer may be called to call center a lot, and maybe the customers plan is, is very expensive. So some of the factors that really affect about if the customer is churning. So from a business perspective, we can decide, oh, maybe we should improve our customer service, maybe we should reduce our pricing. So on a macro level to see those main factors, but also being able to dive into the to the micro level, and check how a change of each one of the features is really impacting the target variable. So let's say if a customer called two times twice or three times to customer service, how that affects the likelihood to churn. So this is gonna be super helpful. And this is really where we add the expandability side to, to the predictions from from the model and the predictions themselves, you know, at the end, when you move the model to production, and now you have new data coming in, you look to score it, this is where you can integrate the predictions with your business applications. For example, you know, Tableau Power BI, all those business applications. So you can still continue to work with your existing business flows and tools as you like, and being able to generate decisions. Grant But I like as he explained, or that you shared explainability is one of those elements that helps with successful adoption and usage of an AI model, and ML model. If we step back and given your experience with customer success, how would you net that if you were to look at the patterns, both the good patterns as well as the anti patterns around customer success? What have you noticed over time, across lots of markets or industries, those success patterns for not only building the models but but actually getting the value in the outcomes from AI models? What have you seen? Atalia So I think this is the million dollar question because I always say that it's not enough just to purchase an AI machine learning tool, right? It's a it's about if you if you use it, and if really models made it to production. This is how we did Robert, manage our success and with ourselves as partners, to our customers, we have an organization that is professional services, that includes data scientists that helping customers on implementing the whole process, we have some AI success. division where you have consultant, past consultant that currently working for DataRobot can do irrigation sessions with with customers and really looking to unlock the potential that they can get, and really helping them to the whole process with our professional services. So, the process can be quite long and complex. And I think it really depends also in the maturity level of the customer, because based on their skill set based on if they already have some models in productions, or if we just started a process, those things can affect about the process, because when you need to take into consideration existing infrastructures and models versus building everything from scratch, and maybe educate the people on what they can get from AI and machine learning model. So, this is a bit different between maturity, and I think everything eventually stopped with change management, you see, a lot of times you know, there is this very motivated executive buyer that is looking to change and, and include AI in the organization. But then how do you really convince the people that working on a day to day basis that this is the tool that can really help them and sometimes we see it even with data scientists that they they still like keeping, like to keep the hands on and and code by themselves without really trust any any other tools. So this is where you provide some education with some horses on the platform, but also how you engage a model that is not like a black box, right? You have expandability, you have the process, you have documentation to really let the the employees to trust the process at the output of the product. Grant So building that that trust in that, in that process sounds like the key thing there is success stories. Is there a success story that you can share where you help the organization go through this and realize some outcomes? Atalia I can try to think about some of the success stories that are publicly covered. Yes, as you know, right. For example, we have a story from Stuart healthcare. They are the largest for profit private hospital. They're operating in the US. And they use machine learning to make big decisions about staff and patients, which eventually helping them to reduce costs and improve the patient outcomes and experience. And they already started the process and really decrease the cost as they wanted. So 1% reduction in registered nurses hours paid per patient they netted $2 million in saving per year for eight of the 38 hospitals enough to its network. I think this is a great number and also a greater effect. I'm always very happy when I see those stories around, you know, hospitals and health care, which eventually provide better outcomes for society as well. Grant If you were medium size organization looking at looking at Hey, how can I apply AI/ML to my business? What would you say to them in terms of adopting a platform like like DataRobot? What What would you say? What are the things that they should start doing? Atalia So the process, in my opinion, divided for three major stages. One is the why. look internally and think, Okay, why we need to use AI? What are those use cases that I'm looking to solve? And what will be the expected improvement or our AI from those use cases? Once we define those use cases? The next question will be the how, okay, we defined the why now how we implement it. So understand the skills that they have in house and then define what to outsource versus what to build, buy versus build right, and then choose the right tool and start the implementation. And when you start the implementation, the the end result will be the what? Once we have the model in production, so how, what are the outcome that we are getting and how we are making sure that the models are still accountable with our business and always start small start with one use case, just prove that works, we have adoption across our organization. And then it's going to be like a waterfall because many times one use case can really amplify the rest of the use cases and create motivation. And some of the outputs of one model will go to another use case, and so on. Grant Have you have you gotten a sense of the general amount of time? Or is there a pattern? In other words, let's say I get started today? Can I expect to see outcomes in three months? Six months? A year? Where's that said? Atalia I've seen it all I've seen organizations that that can do it quickly, within really, two months. And I've seen organizations that are still stuck. Because if we're going back to what I said earlier, it really depends also in in the organization itself, right? If you have a blocker from the IT, or from the scientist, or you know, maybe some requirements that you still need to, to to implement and you didn't and what is, what is it around your prioritization? So it really depends. And also, let's be realistic. It's not just that I decided today, oh, let's talk with AI and machine learning. And it can work right. I need to have specific state of my existence data so I can really produce a better outcome, right? Today with data centric AI approach. As your data is more clean and organized and relevant, you will have better outcomes on your model. So you need to be in certain of our data transformation process with your organization's data to really start. Wow, Grant Wow, that's that's awesome. I love that Atalia. Thank you so much for taking your time here today. With me any last comments before we wrap? Atalia Up, I'm just audience gonna feed the story don't afraid to, to ask questions. I always like to say at the end, Are You Smarter tomorrow than yesterday? Because as we evolving over time, you know, I was I was three years old. And then I'm trying to obviously I'm gonna be smarter over time, as I'm growing up, but in the in the small revolution. Are we smarter tomorrow than yesterday? Think about that chocolate thing, how you can be more innovative, how to make your life easier. And how to do some changes fitting your condition and with yourself to develop and achieve great outcomes. Grant That's a growth mindset. I love that Right? Which is always learning our learning isn't kept. And let's focus on what are the things we can take moving forward to improve our environment, our situation, the people we serve, and those around us. All right. Natalia, thank you so much for your time and your organization for allowing us to chat with you today, everyone. Thanks for listening to another episode of ClickAI radio. And until next time, go check out DataRobot. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook visit ClickAIRadio.com .

In this episode, I talk with the CEO and founder of an organization that has been applying AI to help them develop products. Will AI help you develop your products faster? Come and see. Grant Hey, everybody, welcome to another episode of ClickAI Radio. So today I have this opportunity to speak with one of those brains out there in the market that's being disruptive, right? They're making changes in the industry in terms of not only the problems are solving, but it's the way in which they're solving the problems using AI very fascinating. Anyway, everyone, please welcome Paul Ortchanian here to the show. Paul Hi, nice. Nice, nice of you, happy to be here on the show.  Grant Absolutely. It's very good to have you here today. When I was first introduced to you. And I started to review your material what it is that your organization has put together as fascinated with the approach because I have a product development background and in in the software world. AI was late comer to that right meaning over generations when I saw the approach that you're taking to that I'm interested to dig more into that. But before we do that big reveal, could you maybe step back and talk about the beginning your journey? What got you on this route? And this map, both in terms of product development, and technology and AI itself? Paul Yeah, absolutely. So I started out as an engineer, headed down to San Francisco in the early 2000s. And, and I was more of a thinker than an actual engineer, or just be the type of guy who would figure things out by themselves. But if you were to ask me to really do things that the real things engineers do, you know, creativity was there, but not the solutioning. So being in San Francisco was a humbling experience, I guess, Silicon Valley, you get to see some really, really good engineers. So I had to make a shift in my career. And since I had a passion for user experience, the business aspect, product management was a great fit a function I didn't really understand. And I got to learn and respect, and did that for about 10 years.  In the mid 2000s, and 10s, I basically moved back to Montreal for family reasons and cost of living, of course in San Francisco. And I started a company called Bank Biddick, which in French stands for public bath. And the idea is that most what I realized in Canada was that people here in accelerators, incubators and, and startups just didn't understand what product management was. So they didn't really understand what they do and how they do it. And I saw a lot of organizations being led by the marketing teams, or the sales team and being very service oriented and not really product LED.  So basically, it basically stands for public bath, which means every quarter, you want to basically apply some hygiene to your roadmap, you have a galaxy of ideas, why not go out there and just, you know, take the good ones and remove the old ones and get rid of the dirt. And we started with that premise. And we put we said, well, what does a product manager do on a on a quarterly basis? Because a lot of the material you'll read out there really talks about, you know what product managers should do in terms of personas and understanding the customer's data and this and that, but nobody really tells you which order you should do it. Right. If that was my initial struggle as a product manager, do you try to do it all in the same day and then you realize that there's not enough time? So the question is like in a one quarter 12 week cycle, as my first three weeks should be about understanding the market shifts the industry, the product competitors and and the users and then maybe in the next three weeks working with leadership on making sure that there is no pivots in the organization or there are some some major strategic changes and then going into analyzing the DIS parking lot of ideas and figuring out which ones are short term and re and making business cases in order to present them for, for the company to make a decision on What to do next on the roadmap.  So there is a process and we just call that process SOAP, which goes in line with our public bath theme. So the idea was like, let's let's give product managers SOAP to basically wash their roadmap on a quarterly basis. And, and that's what being public does. And we work with over 40 organizations today so far, on really implementing this product LEDs process within their organizations, we work with their leaders on identifying a product manager within the organization and making sure that marketing support sales, the CFO CEO really understand how to engage with them what to expect from them, and how product manager can add value to to the organization. And so they just doesn't become, you know, this grace towards them as many features as you can pump out, right. Grant Oh, boy, yeah. Which, which is constant problem. The other thing that I've noticed, and I'm wondering if, and I'm sure that your SOAP methodology addresses this, it's the problem of shifting an organization in terams of their funding model, right? They'll come from sort of these project centric or service centric funding styles, and then you've got to help them through that shift to a different funding model round products. You guys address that as well. Paul Yeah, we address that a lot. One of the things we always tell them is if you are a service professional services firm, and you know, I have no issues basically calling them that. If and I asked them like do you quantify staff utilization in percentages, like 70% of our engineers are being billed? Right? Do we basically look at the sales team? How many new deals do they have in terms of pipeline? Are we looking at on time delivery across those, so double use that to serve the sales team closed? And what is our time and technical staff attrition, that usually tends to be identifiers of you being a service firm? And we often ask them, well, let's let's make the shift, when we identify one little initiative that you have that you want to productize because they all these service firms, really all they want is recurring revenue, then the service is tough, right?  That you constantly have to bring in new clients. So this recurring revenue, the path to recurring revenue is, you know, being able to say, Okay, I'm going to take two engineers, one sales person, one marketing person, one support person, and a product manager. And those guys collectively will cost me a million dollars a year, and I'm going to expect them to basically bring me $3 million in recurring revenue. That means that they're, they're no longer going to be evaluated on staff utilization, they're no longer going to be evaluating the number of deals they're bringing in. And they're, they're really going to be evaluated on how are they releasing features? Are they creating value for those features? are we increasing the number of paid customers? And are we basically, you know, staying abreast in terms of competitors and market industry changes.  And so that's a complete paradigm shift. And that transition takes a while. But the first seed is really being able to say, can you create an entity within your organization where the CFO accepts that those engineers are dedicated and no longer being, you know, reviewed in terms of their utilization rate in terms of their know how much they're billing to customers? Once they do that shift in the recipe is pretty easy to do. Grant Yeah. So it's become easy. So the thing to I've seen and experienced with, with product and product development is the relationship of innovation to product development. And so I see some groups will take innovation, and they'll move that as some separate activity or function in the organization, whereas others will have that innate within the product team itself. What have you found effective? And does self addressed that? Paul Yeah, I mean, we always ask them the question of what how are you going to defend yourself against the competition with the VCs that have to call their moat, right? And that defensibility could be innovation, it could also be your global footprint, or, you know, it could be how you operationalize your supply chain make things really, really cheap, right? Every company can have a different strategy. And we really ask them from the get go. We call this playing the strategy, we'll give them like eight potential ways a company can, you know, find strategies to differentiate themselves? And the first one is first the market?  And the question is, it's not about you being first to market today. But do you want to outpace your curlier closest rivals on a regular basis? And if so, you know, you need an r&d team and innovation team who is basically going to be pumping out commercializable features or r&d work. And then we always give him the two examples, the example of Dolby Dolby being completely analog in the 70s, but really banking on their r&d team to bring him to the digital age and from the digital age to set top boxes to Hollywood and now into Netflix compression, right?  So they basically put their R&D team as the leader to basically keep them a step ahead of their competition. But it but on the other hand, we also Welcome, you know, talk about Tesla, where Tesla is basically doing the same thing, but they're not doing it for intellectual property like Dolby, they're not suing anybody are actually open sourcing it. But there's a reason behind it where that open sourcing allows them to basically create the, you know, what we call the Betamax VHS issue, which is making sure that there's compatibility across car manufacturers for Tesla parts and overproduction of parts that are Tesla just to increase their supply chain, right? So we ask them, Do you want to be that company, if you don't want to be that company, then there's other ways for you to basically create defensibility, it could be regulatory compliance, if your industry requires it, you can go global, you can go cross industry, you can basically create customer logins, how just how SAP and Salesforce love to basically just integrate workflows with like boots on the ground, professional services certified teams, right?  And or you can basically review your process and make sure just like Amazon, that you're creating robots to do human work in order to just basically do it cheaper than anybody else. So there's ways of doing it. And I would say that if you were in AI space, especially, you know, it's important to make sure that, you know, are you really trying to innovate through AI, because you can get a lot of researchers doing a lot of things, but that's not really going to help you create commercializable ideas. So from the get go, the leadership team needs to, you know, at least make a hedge a bet on, you know, expansion, innovation, or creating efficiencies and just, you know, decide and let the product management team know in which direction they're gonna go planning on going for the next six years. Please. Grant I love your last comment there, Paul about about getting the leadership team involved. It seems that many times in organizations, this challenge of making the change sticky, right, making it last making it resonate, where people truly change their operating model, right, they're going to start operating in a different way, their roles and responsibilities change, what is the order in which things get done all of those change, when they start moving both into this AI space, but you know, product driven just by itself, even without AI has its own set of challenges? So here's the question I have for you. As you move companies through this transformation, that's part of your business, right? You are transforming the way companies operate and bring about better outcomes. How do you make those changes sticky? Because this is a cultural change? What is it you guys have found it's effective? Paul Or it goes back to our name public bath and SOAP, right? Because the idea is, you take a bath on a regular basis hygiene is something you do regularly, right? So we ask these organization, if we give you a process where you know exactly what the product management team is going to do with you with the leadership team in order to prioritize your next upcoming features, then can you do it in a cyclical way, every quarter, you need the product manager do the exact same process of revisiting the competitors, the industry, the market, as well as like the problems that you have with your premature customers, bringing it back to the organization, asking if the strategy is still about expansion, innovation, efficiencies, identifying new ideas, clearing up the parking lot of bad ideas, etc, and eventually making the business case for the new features in order for them to make a commitment. So if we do this in a cyclical way, then the product role becomes the role of what I'd like to call the CRO, which is the chief repeating officer, because all the product manager is doing is repeating that strategy and questioning the CEO, are we still on? Are we pivoting or if we pivot?  What does that mean? And if you're doing it on a three month basis, what that allows your company to do is to make sure that the marketing and sales and support team are going along with what the engineering team is going to be delivering. So this is what I usually see most product organization where a decision has been made that the engineers are going to be building a particular feature, the sales and marketing team just waits for the engineers to be Code Complete. And once a code completes, done, they're like, Okay, now we're gonna promote it. But my question is that it's too late. Right? You really need so I always show the talk about Apple, how Apple would basically go out in front of millions of people and just say, here's the new iPhone 13. And we came up with a new version of Safari, and we're updating our iOS and we're doing a 40 Other changes. And the next thing you want considered an Apple store and you know, everything has changed. The marketing has changed the guys that the doing the conferences, and the lectures and the training are all talking about the new supplier, the new iPhone, and you ask yourself, How did how did Apple know and to organize the marketing support and sales team in that in such a way that the day that the announcement has been done? Everything is changed. So that means that it's not just the engineering team's responsibility to get to Code Complete.  It is a collective responsibility where marketing support and sales are also preparing for the upcoming releases. And and the only way you can get that type of alignment is If every three months these these parties, technology, product, CEO, CFO, sales, marketing and support can get together and make a clear decision on what they're going to do, and be honest enough of what they're not going to do, and then work collectively together on making sure that that those are being delivered and prepared in terms of the size of the promotion that we're going to do, and how are we going to outreach how's the sales collateral going to change? How is the support team going to support these upcoming features. And so everybody has work to do in that three months timeframes. So and then that if we can get to that cyclical elements, I think most companies can create momentum. And once that momentum has is generating small increments of value to the customers, then you base start start building, what I like to call reputational capital, with the clients, with the customers with the prospects. And eventually anything you release the love, and everything you release adds value. And eventually everybody loves everything you're doing as an organization become that, you know, big unicorn that people want to be. Grant Yeah, so the net of that is, I believe what you said as you operationalize it. Now there's it gets integrated into everyone's role and responsibility. It's this enterprise level cross functional alignment that gets on a campus. And the cadence is, in your case, you'd mentioned quarterly, quarterly sounds like that's been a real real gem for you. I've seen some organizations do that in shorter timeframes and some much longer. It sounds like yeah, at least quarterly is that a good nugget that you find there?  Paul Yeah, quarterly works, because you know, markets are set in a quarter way they operate in that way the you want results on a quarterly basis in terms of sales in terms of engagement, etc. But what's important is that which you know, a lot of engineering teams like to work agile or Kanban. And in a quarter in a 12 week timeframe, you could fit, I'd say, Let's see your Sprint's are three weeks, you could fit for sprint for three weeks variance, or you could fit six 2-week sprints. But I feel that if you were to shorten it, then the marketing team and sales teams supporting might not have enough time to prepare themselves for Code Complete, the engineers might be able to deliver but then the product manager gets overwhelmed because doing an industry research, competitor research etc. Every, say month and a half or two months just becomes overwhelming for them. Because things don't change enough in two months for them to be able to say, Oh, look, this competitor just came up with that. And now we need so so I think three months is enough time for the world to change for, you know, country to go to war for COVID to come over and just destroy everything. So pivot decisions are usually can pretty good to do on a on a quarterly basis.  Grant Yeah, that's good. That's, I think COVID follow that rule. Right. Hey, I have a question for you around AI. So how are you leveraging AI in the midst of all this? Can you talk about that? Paul Yeah, absolutely. So what we noticed is a lot of organizations who have products, so SaaS products, or any type of product, IoT products, etc, they're generating data. I mean, it's it comes hand in hand with software development. So all that data is going into these databases are and nobody knows what to do with them. And eventually, you know, they want to start creating business intelligence, and from business intelligence, AI initiatives have just come about, it's very normal to say, You know what, with all this data, if we were to train a machine learning module, we would be able to recommend the best flight price or the best time for somebody to buy a flight, because we have enough data to do it. So so we're not working with AI first organizations who are here we have, our entire product is going to be around AI, we're just trying to work with organizations that have enough data to warrant 1-2-3, or four AI initiatives and an ongoing investment into those. So the best example I like to talk about is the Google Gmail suggestive, replies, right, which is adding value to the user needs AI in the back, end a lot of data.  But ultimately, it's not that Gmail isn't AI product, it simply has AI features in it. So and when organizations start identifying AI or machine learning, predictive elements to their product, then we go from engineering being a deterministic function, which is if we were to deliver this feature, then customers will be able to do that to a probabilistic function where Let's experiment and see what the data can give us. And if this algorithm ends up really nailing it, we will achieve this result. But if it doesn't, then do we release it? Do we not release it?  What's the and then it gets a little bit hairy because product managers just lose themselves into it. Oftentimes, they'll release a feature and the sales team would just ask them to pull it out right away because it has not met the expectations of a customer or two. And ultimately, like what we ask product managers to do is work with leadership on really it Identifying a few key elements that are very, very important to just just baseline before you were to begin an AI project. And those are pretty simple. It's, it's really like, are you trying to create to have the machine learning module? Make a prediction? Are you or are you trying for it to make a prediction plus pass judgment? Are you trying to make it a prediction, a judgment and take action? Right? Decision automation, which is what you know, self driving cars do, will will see biker, they will make a prediction that it's a biker will make a judgment that it's indeed a biker, and we'll take action to avoid the biker, right?  But when you when you're creating ml projects, you can easily say, you know, we're just going to keep it to prediction, right? Like this machine is going to predict something and then a human will make judgment and the human will take action. There's nothing wrong in doing that. So just setting the expectations for from the get go in terms of are we basically going to predict judge or take action? That's number one. And then the next question is whatever that we decide if it's just prediction, is that worth guessing? And who doesn't have guessed today, if it's a human? Is that how accurate is that human? Let's quantify. So this way we can compare it against what this machine is going to do? What is the value the company gets out of that gas being the right gas? And what's the cost of getting it wrong? So oftentimes, we forget that humans to get it wrong to and if humans get it wrong, there are huge consequences to organizations that will overlook but as soon as machine learning does the same thing, we're ready to just cancel hundreds of $1,000 of investment.  Grant Yeah, that's right. Yeah, we tossed it out. So the use case, I'm assuming would be you would leverage AI to say enhance a product managers abilities to either predict outcomes of some product development activities, or releases or things like that, would that be a kind of use case where he looked apply? Paul Well, not a product managers, I would say the product manager, we'd look at it software, let's take the software of a website that tries to predict your if people qualify for a mortgage loan, for example, right? So you have enough data at that point to be able to automate, what's the underwriting process that humans do of validating whether or not somebody's eligible for loan? Well, we could take all that data and just make a prediction of that person's fit for a particular loan. Now, if we were to say, well, it's just going to be the prediction, but we're not going to give this person the loan, we're still going to ask a human being to pass judgment that that prediction was the correct one, and then take action to give or not give him a loan.  So let's say that's the machine learning module, we're going to add to our to our feature. Now, the question is how this underwriting department in the past 10 years, how often did they really screw up that, you know, and issued loans to people that were that couldn't pay their loan, right? And realize it's 40%? Were like, Wow, 40%? Could this machine learning be as accurate as damn plus one, right? And, and then we ended up realizing that yeah, this, whatever we delivered is 33% accurate, and not 40% plus one accurate now is it still worth putting out there we spent $100,000 into it, and then you know, then it's up to the product manager to basically be able to put this thing in place and say, but look, you know, underwriting is a nine to five job currently in our business, and it cost us this much money.  On the other hand, if there's this machine learning is 33% accurate, but it's actually doing it 24/7 365 days a year, and it's only going to improve from 33 to 40. And if it goes above 40, then we the savings for our organization are this much money. So it is really the product managers job to be able to not only talking about the business KPIs, but also the what the AI machine learning KPIs we need to achieve and what the impact of that would be if we get it right. And I think that the biggest issue we have as product managers in the AI space is if we were to go and do this all there everything that we need to create AI, like the day data ops, selecting the data, sourcing it, synthesizing it, cleaning it, etc. The model ops, which, you know, comes down to multiple algorithms, training those algorithms, evaluating tuning them, and then the operationalization. If you do all these steps, and you get to 80 to 20% accuracy, and your target is at 70% accuracy, right? What do you do with it?  Because you had to do all this work anyways, it cost you tons of money and time. And so how do we get the leadership team to say this AI initiative has enough value for us that we're willing to live with the consequences of it getting it wrong, or we're willing to actually have it supported by human for the next six months to a year until we basically trains itself and gets better? So it's how do you get this openness from from from a leadership team? Because what I've often find delivering AI projects is every time you deliver an AI project, and it's misunderstood in terms of its output, and everybody thinks it has to be 100% accurate, the second and goes wrong. It's the political drama that you have to go through in order to keep it alive. is just it's just overwhelming, right? So miners will set those expectations up front and tool, the product managers with the right arguments to make sure that they the expectations are set correctly. Grant Have you ever worked with or heard of the company called digital.ai? Are your familiar with them? digital.ai, maybe not. Anyway, they have been working in a similar space as you but not so much of the product management level. What they're doing, though, is they're, they're looking to apply AI to the whole delivery function. So so you can you see, the product manager is above this, and is making sort of these KPIs and other estimate activities and the planning out. But then there are all these functions under there that of course, do the delivery of the product. And so they're working on the tooling spectrum, I think they acquired I think, was five different companies like in the last nine months, that they're integrating these and then building this AI seam or layer across that data across delivery with that purpose and intent to do that predictive not not only backwards analysis activities around AI, but predictive, which is what's the probabilities, I might run into the problem, or some problem with this particular release, right, of this product, right, that we're about to send out, now might be an interesting group for you to get connected with. Paul Yeah, I know, it's funny, because we're there. There's a local company here in Montreal that does the same thing. It's really about like data scientists are really expensive, and they're really hard to find, and there's a shortage of them. So, you know, the lot of organizations are trying to find like a self serve AI solution where you can build your AI using their AI. But ultimately, what they're doing is taking your data and delivering 123 or 10 versions of the machine learning module, it's up to you basically, judge which one is going to work the best for you, but they actually operationalize it, put it out there for you, and really automate the whole thing. So this way, you're not dependent on humans, I love that I really love that I think your organization should have one of those. But that still means that there's a dependency from the for the product manager to know that it's, it's data, like end to end, be able to clean it be able to tag it and then feed it to the to these machines, right? And I think that part is also misunderstood. Because Do we have enough data? Is there bias in the data and all that needs to be understood and figure it out? Because, you know, you could say like, Hey, we put it to this big machine. And we ended up with a 20% accuracy on the best ml that it out, put it, but that's still not good enough? Because we're trying, we're aiming for 87? And what does it mean? What do we need to do to basically get it to 87? We're gonna have to review the data bringing some third party data, you know, and it's, and that's, that costs a lot as well. So, yeah, Grant Do you think AutoML solutions play a role here like, Aible, I don't know if you're familiar with that platform, you know, that the goal is to try to reduce the amount of dependency that's needed on the data science. Scientists themselves, right. And but it's, it's still doesn't remove all of the data cleansing part, but it does help take care of some of the certainly the low level data science requirements, you think you think that's a viable solution in this area?  Paul I think it is. I mean, it's, you know, we went from rule based AI, where data scientists had to do good old fashioned AI, which was a feature engineering, right? Putting the rules themselves to machine learning AI, where, you know, we had to train the data that we needed, were so dependent on these data scientists. And now we're getting to v3, where we have these tools. And you know, there's a data dependency, but there, they also don't have such a high dependency on data scientists are and you know, figuring our algorithms and etc, we could just basically have these prepackaged algorithms that could basically output us any types of solution. What I tend to like, I've seen this a lot in a lot of companies. There's some companies that are very, very industry specific, right? So they're providing AI for E-commerce to be able to provide better search with predictive elements based on the person's browsing history. I mean, I, I'm not sure, but the ones that are providing every ML imaginable, so you could use it for supply chain, or you could use it for something else. I know it's dependent on data. But again, these algorithms, you can't have all the algorithms for all scenarios.  Even if it's supply chain, some person has perishables and there's ordering bananas and the other person is ordering, I don't know water coolers, and those, those don't have the same rules, right. You know, so it's, it's important to just, I think that maybe in the coming years, we'll have a lot of companies that are really going cross industry, just like we're in E-commerce, the other ones that are med tech, the other ones are, etcetera, the tools are the same. I mean, more or less the same, the customers are gonna get used to basically having these UI is that I'll give you your input the data in and then these emails come out, and then you choose which one and they give you probability you can retrain them and all that stuff. And I think that it's just going to get to a point where we're going to have these product managers who are now responsible of kind of training the Machine Learning Module themselves, you know if it's going to be the product manager, or if it's going to be some other function, where I think it does definitely fit inside the product managers? Grant Well I do is, I think it's because they still need to have what we would call the domain knowledge and in this domain of building products, yeah, AI, at least at least in this phase of the life of AI, where we are today for the foreseeable future. I think the product manager needs to be involved with that. Sure. So. Paul It comes down to intuition, right, somebody has to have like to build that intuition about what a model is relying on when making a judgment. And I think that, you know, with product managers, the closest one really, maybe in bigger organizations, it's the person who's managing analytics and data, but in smaller startup organization, I can definitely see the product manager putting that  Grant Yeah, absolutely. Paul, I really appreciate you taking the time. Here today on this been fascinating conversation. Any last comments you want to share? Paul We have tons of articles that talk about so we're very open source as an organization. So if you want to learn more about this, we have about 70 articles on our website. Just go to BainPublic.com and just click on "Articles" and you could just, you know, self serve and basically improve as a product manager in the AI space. Grant Excellent, fascinating, love, love the conversation, your insight and the vision where you guys are taking this I think you're gonna continue to disrupt everyone. Thanks for joining another episode of ClickAI Radio and until next time, check out BainPublic.com. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook visit ClickAIRadio.com now.  

In this episode I talk with Dr. Pranay Parikh, where we look at the question, how can you do your full time job while being an entrepreneur to fill the gaps that are missing in your life? Grant Hey, everybody, welcome to another episode of Financial Investing Radio. So today I have this opportunity. It's taken us a few times for me to chase him down. He's very busy. Dr. Pranay Parikh. I'm excited to talk with him when I when I first saw the profile on him and realize the journey that he both has done and the one that he's on and his vision of that I got to talk to this gentleman. I'm gonna read his the little clip that I got before even let him say a word. I just want you to hear a little bit about him. Alright, here we go. Okay, hold on. Here we go. It's Dr. Pranay. Parikh is a physician co founder and president of ascent equity group, a serial entrepreneur, we're going to want to ask him about that. Okay, online course creator and host of the MD to entrepreneur podcast, which is really cool. Continuing on his unconventional journey, and I add it is which is what makes us really cool. His unconventional journey to medicine helped him learn the skills to excel in entrepreneurship. He's launched a seven figure online course. And he's bought over $1.1 billion in real estate. Wow. And he's helped hundreds of physicians launch their own businesses. His goal is to help launch 10,000 physician led businesses. That's amazing. So I want to welcome here to the show. Dr. Pranay Parikh. Welcome. Pranay Thanks, Grant. I'm super excited to talk. Grant It's so good to have you here. Now, we were both talking before we got started. We're both in the southwest part of the country. We sort of enjoy that. In fact, I think you and I might have even had our earlier grown up years in the Northern California area. So we got a lot in similar but the key difference is, I am definitely not a doctor. You know, I went bought band aids last night. And it took me awhile just to figure that out. So I'm more of a tech and investing guy. But I am not a doctor guy. But I'm excited to talk to you about this and your journey. So thanks again for being here. Pranay Yeah, super excited to talk to you and your audience.  Grant Okay, now something happened, right? You were growing up and you said, hey, I want to be a doctor and you got moving down that path. But somewhere along that line, well, first of all, what kind of medicine? Have you been practicing? Pranay So I did a residency in Internal Medicine, and I practice hospitalist medicines. So that means if you ever have to spend a night in the hospital, then I'd be your doctor. Grant Okay. Is that is that the same as as an internist? Is that? Is that what that is?  Pranay Yeah, so it's the same residency, but usually what internists do they do outpatient medicine, so you know, your primary care doctor, they do some inpatient, but I'm kind of sub specialized. I only see people in the hospital, the hospital itself.  Grant Okay, got it. Very good. All right. So you were on that journey doing that work. And something happened somewhere along the line, you sort of sat up and said, I need to add something, either to my life or my purpose or my vision on top of this medical route. Tell me about that. What happened? Pranay You know, so it's kind of funny. So to get into medical school, you have to be well rounded, right? You have to be doing all this other cool stuff, right? You can't just be a nerdy like I was really good at Sciences and maths and all that stuff. They want someone you know, reads a lot of the books and does all this stuff. But after you get in all that stuff gets taken away gets subtracted. And it kind of sucks for people like me that like doing a lot of other stuff, but you're so busy in medicine, doing your job learning that there's not much else you can do. So as soon as I got an opportunity to start doing other stuff I did. So for example, in medical school, I used to hold art shows. And then the money that we grades go to the doctors without borders, I was always trying to do stuff that was, you know, a little bit outside the box. So as soon as I graduated finished everything, I was like, okay, what can I do? What? Where can I go and all this stuff that's been suppressed for so long. Grant That's amazing. You talked about Doctors Without Borders, I have a friend just Sunday night I was talking with he, he spent a number of years I think went five times down to Central America, they were taking both dental as well as medical help and support into some of those areas there. It's fun to hear the service and the blessing that that is to people that are missing those things. So you did these art shows that was providing support to them. That's a cool thing. So it sounds like there was either there's altruistic thing that you needed to get filled in your life, right? I mean, something to reach out further, in addition to the work you're doing that led you to doing this kind of work. Now, there's that what led you at some point to say, I'm going to build sort of a business or be an entrepreneur on it is that what led you down that path? Pranay Still, so you know, when I when I looked at the landscape after being graduated, you know, at a pretty decent salary, but I realized the most important thing is what I do with that money, not how much I can make, you know, I could have just gone and bought a car or bought a house. And I'd be, you know, I wouldn't have a ton of money to consider for my future, right. So I decided to rent a little bit longer, keep driving my Honda Accord, which actually still have 2012. So you know, it's what I do with the money. So you know, I did the stock market, but in this world, people that are most well off and buy well off, you know, I don't care about the total number that's in my bank account, I care about freedom, I care about providing for my family, and I care about providing for my extended family. So the people that I've seen that been have been able to do that are the people in business, business and real estate. So I thought how, how can I incorporate one or, you know, fortunately, now, both of those into my life? And what's the quickest way to do that? Grant Okay, excellent. So you have a broader vision about, I want to make a difference, right? I want to help someone in the world, what not just my own family, but but the people within my reach that I can access or get out to? Is that right? Pranay Yeah. And as a doctor, it was great. Because I there's a lot of job satisfaction and being able to help people. But I was limited by time. And that's, that's one of the big issues that I had, I wanted to create this impact. I wanted to help as many people as possible, but there was only one of me, right? So if I'm not at the hospital, I'm not helping people. So how do I take that impact that I want and spread it to, really the whole world, and the what I thought was, if I can help other people, after I got my business up and running, I can help other people get their business up and running. And if they're able to either find financial security and be happier doctors, you know, you might have heard of this epidemic of a burnout, that's just been an issue. And a lot of that is not being able to pick your own hours and being forced to do things that you don't necessarily want to do a lot of administrative work. And if you're able to take control of your life, then those happy doctors are making a bigger impact. And that's something that I can, I can help people do as well. Grant So you're going after this audience of people that have proven that they have the ability to be disciplined to dedicate their time and their resources, their energy, not just to not just to their practice in their craft, right around medicine, but but now to take that and apply it to businesses. And those businesses the purpose of those sounds like have twofold one is to provide an outlet for them sort of emotionally, right where they can get outside and, and maybe really, you know, benefit their own family but also to use their businesses away then to further their reach to help people when they're away from practicing their medicine. Did I get that? Right? Pranay Correct. Correct. And you'd be surprised how often someone comes up to me and says, Hey, Pranay, I'm just a doctor. You think I could start a business? And it's funny because you have to get to get into medical school or, you know, dentistry lawyers, and I'm just talking about medical school because that's what I've been through. But you know, any professional degree, you've gone through a lot. You've studied, you've been taking all these tests and you've really stood the test of time. So you can pick up a lot of these other things pretty easily, you know, a little bit of trial and error. So a lot of it is just showing people that they have this innate ability. And a lot of times in medicine, we're taught not to try something, unless we have 100% chance of success. Grant It's interesting is about a year ago, I was running a class for a group of people on starting your own business. And, and one of the people that came into that class was an oral surgeon, and doing doing just great, you know, in terms of financial, right. And I said to him, why, why did you take this right to mean, you certainly don't. Okay. I mean, and he said, well, the, the one area that he felt like he was running into that it was creating a challenge for him was that, while he had figured out and was successful at the procedures, at some point in the career, those procedures became procedural. In other words, I was just doing I'm quoting him, I was just doing and still doing the same thing every day. And what I found that I couldn't do is I couldn't be creative, right? I mean, that wouldn't be the right thing, either for the safety of the patient, right, or for other reasons. It's not like I can say, well, I'm going to try this procedure. And other way. There's other times and places maybe to do that, but not while you're, you know, doing the the surgery. So he said there was a gap in his life was, he needed a way to be able to express himself and be more creative, and let that part of his personality have a place to do things. So he was starting a business on his own. And I'm wondering if, if that resonates with you, if that's also part of what was in your mind as well. Pranay Yeah, and you know, after and there's usually a slump, three to five years after you're done with school, you feel confident in your abilities. And a lot of times that challenge is gone, you know, it comes here and there. But that that daily challenge that you're, you're racing against the edge is gone. And it's nice to go into something that you have no idea about, you know, starting a business, starting your own practice starting a podcast like yours, and it's invigorating, trying to figure things out, you know, and, and failing and being successful and getting that first download and 10th Download, and so on. Grant There is something to that, isn't it? It's this, what's my next hurdle? Right? What's the next thing? You know, can I do this right? Can I grow or expand in that area? And so I think sometimes in that profession, well, not just that profession, others as well. But being able to have that side hustle, or that side practice helps to fill that out. And to round you out. That doesn't mean you step away from it. Now, maybe some do. In fact, on that topic, do you still practice then today in medicine? Or are you full time over in the sort of business and investing side? Pranay I actually still practice and most people are very surprised, given all the other stuff I do. I'm at around point eight full time, I probably need to cut down a little bit more. So at the end of the year, I'll probably do point five, but it's nice to keep my foot in the door a little bit. You know, like I said, I enjoy it. And it is using a different side of my brain. Grant Yeah, yeah, absolutely. Okay, so let's talk about, let's talk about that entrepreneur side of you then. So you went to real estate, because you said you saw that those that had achieved some level of freedom. Were doing business and real estate, and you sort of found that pattern there. Right. You talk a little bit about what it is that you do with that business. Pranay Yeah, you know, and so a little bit about the beginning, in medicine a lot of the times is you're learning from colleagues, you know, it's like Socrates, instead of more of a professor to student, there's some of that as well. But after your practice, seeing I reached out to my colleagues all the time, I asked them what they think so, you know, when I first learned about real estate as I thought, there's not that much people teaching how to invest in real estate without being a landlord. And I looked around, I looked around, there wasn't anything. So I went with my partner and said, Hey, let's create a course that will teach people how to do this, right? And we thought we were done after that. And after a couple years of doing that, we've had 1000s of students, people say, hey, you know, we learned we'd like what you say, but we don't actually do want to do the work. We want you guys to find the deals to negotiate, and most importantly, to follow up to make sure that the person who's running the day to day is doing what they say they're doing. So we created really almost like a bespoke, passive, real State, a private equity company called the cent equity group where we go out we find best in class operators that have done this for 2030 years. And when we partner with them, we negotiate for the best terms. And most importantly, there's many people that do that. But most importantly, we have a full asset management, we have a full team that if something goes wrong, we could take over the property. However, that team, they're really focused on the big picture, how do we maximize profit? And, you know, not really doing the day to day like talking to maintenance, finding about leases? How do we maximize profit for our investors, and, you know, we have at this point, hundreds, almost 1000 investors, mostly doctors, but some dentists pharmacist, and we really want to create the best real estate investment, that it takes the least amount of work. So I own a couple rental properties myself, I have a property management, property manager, and it's still work. You know, I don't want to really work very hard for my investments. But that's just me.  Grant Yeah, that makes sense. So wait, can you talk more Pranay about where the investing is? Is it raw land, single homes, this apartments are commercial? You talked about that? Pranay Yeah, so we, you know, we do bread and butter. And we kind of focus on people's needs, right? And people need a place to live. And what happens when, like right now we're heading towards a recession or mortgage rates are going up. People go into apartments, I've lived in apartments in the past. So what we do middle the ground, what is often called Class B, like boy, apartments, where when times are good people will move up into when times are bad people will downgrade from, you know, the nice downtown apartments to you know, maybe a little bit further away. And, or maybe they can't afford a house or they don't want to buy a house. And they go to class B. So that's the type of stuff that we've like, because it's recession, recession resistant. And even in 2008, the default risk was less than 1%. Because apartments are safe and stable. Grant Yeah, safe and stable. Another key part of that seems like the demographics and the geography. In other words, where these are located. Do you target a specific part of the country? Or where do you focus? Pranay Yeah, yeah. So we call it the business friendly states. It's kind of along the Sunbelt. And this has been a trend. We've seen it a lot more in the past, since 2020, after COVID, but it's been ongoing for five to 10 years, people are moving to areas that are more affordable outside of coastal cities. And now with all the remote work, there's there's no reason really to live in the coastal towns, you might as well live where you can afford, and they're starting to get, you know, robust neighborhoods and cultures and all that great stuff. Grant Nice. And when you when you think about the kind of profile, right, can you describe what does that look like in terms of the perfect person that you would bring into the ascent group? Meaning that you're focusing on the doctors, right, but is it someone that's done with medical school? Or is it Are they on their way? Or they they're 10 years down into their career already? I mean, what does that look like? Pranay Yeah, so all our deals have to be accredited investors, meaning that you have to make more than 200,000 per year, for two years, or 300 as a couple for two years, or have a net worth over a million dollars. And that's, that's not our requirements. That's the Securities and Exchange Commission by the government. So as long as they qualify for that, we're happy to take anyone, mostly doctors, but we have a ton of dentists. And it's usually, you know, they have to have two years of tax returns. So people started investing two years after they graduate, and then all the way towards the, you know, latter part of their career. So it's, it's nice, it creates stable income. It's tax deferred. So it's money you can start using, and there's a lot of tax benefits. So doctors especially like the type of investment. Grant Yeah, is it? Is there a certain commitment time? So in other words, are you expecting people will get involved for a five year period or 10? Or what does that look like? Pranay Yeah, so our deals, we tend to go on the lower end. So three to five years, that means you put in your money, you get your money back? Usually 1.8 to double your money in about three to five years just depends on the deal. And minimum is usually around 50,000. And we have that at that amount because it's really easy for people to get me on the phone and any of the principals who are all doctors And we want to create really a high quality great communication system. Because, you know, even if you're able to put down 50 102 50 It's scary. I don't care how wealthy you are, it's scary putting your money down. So we really pride ourselves in having great communication. Grant What impact have you noticed on this investing strategy with, you know, the increase in inflation rates that's been going on as well as mortgage rates is had some impact on this? Pranay Yeah, so and this is really important for people who are looking at these types of deals, also called syndications. People might know it as that the two things I'd really look at right now, to bet a deal is to sponsor you know, people who are running the day to day, that kind of makes sense. But the second is the debt. So interest rates are crazy right now. So I would try to get a fixed rate debt, you know, meaning that it's, you know, three to five years. So for example, our last deal that's close now, we had nine years at 2.9%, Raizy. And then four years of interest only. So we really go out and try to find deals that we think have a ton of downside protection. So the one of the most important things, it's always been important, but even more so is what type of debt or mortgage the people are getting. Because if anything's gonna go wrong, it's, you're not going to be able to cover your mortgage payment, and the bank takes the property back. Grant That's amazing, you share that, you know, I look at the interest rates today. And they are obviously higher than than they were just, you know, last couple of years. But it reminds me of when my wife and I had just gotten out of college, and this was late 80s. And we were in Chicago, and we were buying our first property. And interest rates were really high. I mean, like, 9% 10%, I remember us driving, you know, an hour because we found one mortgage place, you know, that give us you know, half a basis point last. And that meant that we could probably eat every month, right? Because he knows, several $100 less on the mortgage. And so that matters, matters. Totally what what you're saying to have that. All right. So what questions haven't I asked you, you've been very kind to share your time here. What other things do you need to share with people that would help them prepare to take advantage of the services that you're providing? Pranay Yeah, we, you know, one big thing I see. And we deal with a lot of investors on the active and passive side, and you know, the stocks and all that stuff, consider your time, the value of your time, right? If you're spending, you know, 20 hours a month on one of your properties, and you're trying to scale it up. Consider that right? Most of us don't pay ourselves first. You don't have to take money out. But consider the time that you're spending on this. And, you know, unfortunately, a lot of people in business as well. They forget the time they spend on it. They're like, Yeah, you know, it's making us it's making six figures a profit. And when you realize you're not paying yourself. So the nice thing about our real estate investment is maybe you spend an hour on it a month, a year, one, one investment one hour per year, you know, and we really try to create this bespoke experience that we take care of everything for our investors, you know, and that's not the right thing for everyone. And some people like to kind of pick their own deals, but we find that most doctors prefer this. And we give them a nice, diversified portfolio because we work with different people across the US and different types of deals. We'll do maybe some, like very safe deals a little bit more value, add meaning larger renovations with a higher return. So we kind of really spread it out. And we've had we've been going for a couple years now. And like you mentioned in the beginning, we've bought over a billion dollars of real estate and so we're really excited about the chance to give this opportunity to doctors, dentists, pretty much anyone that would qualify as an accredited investor. So if anyone is interested, feel free to reach out to me. Sure, we'll have it in the show notes, but it's Pranay@ascentequitygroup.com. And then one last small plug. I have a podcast if you want to hear more about podcasting, entrepreneurship, being a doctor things that I've been dealing with. It's from MD to entrepreneur podcast and you could find it on Spotify, Apple, Google pretty much anywhere you can find podcasts. Exclusive resources: finp.ascentequitygroup.com/ My podcast on Spotify: https://open.spotify.com/show/48VsN5DnCWY8lxiMyLbrMA My podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/from-md-to-entrepreneur-with-dr-pranay-parikh/id1625547221 My email: pranay@ascentequitygroup.com  My Facebook: https://www.facebook.com/pranay.parikh2 My Linkedin: https://www.linkedin.com/in/pranay-parikh/ Grant That's awesome, man. Pranay, thanks for taking the time here with us today. I really appreciate that. Very impressed with the journey. journey that you're on, and also the impact that that journey is having on people, both in terms of not only your medical practice but what you're doing in terms of business as well. Thanks again for joining. Hey, everybody. Thanks for listening to another episode of Financial investing radio. Until next time, check out Dr. Pranay Parikh at ascentequitygroup.com. Thank you for joining Grant on Financial Investing Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit cliclairadio.com

Today I had the opportunity to speak with "The Land Geek". Fascinating conversation. He's got over, well over four and a half million downloads on his podcast. He's got some interesting ideas and insights on investing with land that the returns higher than normal, I guess normal, what's normal, higher than, you know, eight 10% return, if you will, returns on these investments. So I'm very excited to have Mark here with me today. Thanks for joining us, we learn the secrets from the land geek. Everybody welcome to another episode of Financial Investing Radio. So I have been chasing this person for some time. He is super busy and has an amazing profile. I hope you take the opportunity to look into what he's done. We're going to be having conversation today with Mark, Oh man, Mark. I didn't even ask at a time. Podolski! Mark Perfect pronunciation. Grant Did I say that? Okay. All right. Very good. Mark. Welcome. Thank you for being here today. I appreciate it. Mark Grant Larsen, an honor. Privilege. Thank you. Grant You know, we found out that we are not too far from each other geographically, which is a real treat. There's some neat, neat things about the part of the country we live in for sure. Mark Absolutely. We've lost our complaining privileges living in Scottsdale, Arizona. Grant That's right. That's right. That's right. Even when it gets a little hot. I've learned to quit Quit complaining about it's not a Houston hot right. Mark Houston hot. It's 80 degrees in the pool.  Grant Yeah, that's. That's right. It's fixable with the pool. That's right, right. Okay, so the land geek, you are known as the land geek, you've done a great job getting your name out there as land geek, not only getting your name out there, but proving a framework. But we'll get to the framework here in a moment. Because what you've done is pretty unique compared to other real estate investing strategies that are out there, of which I actually participate in some of those others, right, but I won't get to that yet. So I'm interested to learn more about what you do here. But let's back up. You were doing what what got you started to say I'm gonna go after land investments in this way. Mark So if we rewind the tape now, to 2000 I was a miserable micromanaged. 45 minute commute to work and back investment banker specializing in mergers and acquisitions with private equity groups. And grant it got so bad for me. I wouldn't get the Sunday blues, anticipating Monday coming around. I'd get the Friday blues, anticipating the weekend going by really fast. And having to be back at work. On Monday. I was pretty much yeah, really blue. So I first hired this guy, and he's telling me that as a side hustle, he's going to tax deed auctions. He's buying raw land, pennies on the dollar. He's flipping them online, and he's making a 300% return on his money. Grant. I'm looking at companies all day long. And a great company great has 15% EBIT on margins are free cash flow, average company's 10%. And I'm looking at companies all day long, less than 10%. So of course, I don't believe them. And I've got three grand saved up for car repairs. I go to New Mexico with them. I do exactly what he tells me to do. I buy 10 Half Acre parcels and average price of $300 each. I flipped them online. And they all sell for an average price of $1,200 each. It worked. So I went to another auction and in Arizona, where we live. And again, this is 2000 There's no one in the room. I'm buying up lots of acreage or nothing. And I sell all that property. You know that what auction I made over $90,000 So I go to my wife. She's pregnant at the time. I said Honey, I'm going to quit My job and become a full time lead investor. And she said, Absolutely not. Yeah. Grant What do you say? And what are you doing on? Mark Yeah. So it took 18 months for the land investing income to exceed the investment banking income. And then I quit. I've been doing it full time ever since. And I absolutely love it. Grant That's incredible. So you just sort of stumbled into it, someone happened to say, they'd already sort of figured this out. Now, it sounds like what you did with that is after you've done this for a while, you've created a system out of this, then is that right? Mark Yeah, I mean, after a while, you start picking up things, and you start seeing, okay, well, how do I make this job, myself. And, really, my whole philosophy is, I can always make more money, I can't get more time. And so we want to use three levers to scale and grow a business, other people's time, software and automation, and other people's money. And so once I combined all three of those, I was really able to grow and work really about 30 minutes a week, in my business. Grant What, 30 minutes, you're at a point now where you work 30 minutes a week on your business? In the business? Yeah, that's amazing. Wow. But it took that scaling, right, took that automation to figure it out the team around you to help take care of certain aspects of it took all of those pieces, as well as other people's money. That's interesting. That took five years. Oh, that was gonna ask you so five years to get to that point, learn the lessons, learn the business processes that you should automate, and so forth. Right? Mark Well, five years just to start getting myself out of business, probably another five years, to get to my point where I can work 30 minutes, 30 minutes in the business, rarely, I'm just meeting with my Acquisition Manager. And we're looking at the numbers and saying how many deals are pending? How many offers went out? How many deals will be closed? How can I support you? Grant So when it comes to finding these opportunities is the auctions is that your main input, or source or how well find these. Mark So Grant, I'm going to walk you exactly through how I do it. So you live, I'm gonna use these as a case study. You're in Scottsdale, Arizona, but let's assume that you own five acres of raw land in Texas, and you owe $200 in back taxes. So essentially, you're advertising two important things.  Number one, you have no emotional attachment to the raw land, you're in Arizona, the properties and taxes in Texas.  And number two, you're distressed financially in some weird way. Because we don't pay for things like our property taxes, we don't value them in the same way. As resolved, county treasurer, keeps sending me notices saying, Grant, if you don't pay your property taxes, you're going to lose that five acre parcel, tax deed or tax lien investor. So all I'm gonna do is look at the comparable sales on your five acre parcel for the last 12 to 18 months, I'm gonna take the lowest comparable sale, let's say $10,000 and divide by four. And that's gonna give you a Warren Buffett would call a 300% margin of safety. So I'm gonna send you an actual offer on your five acre parcel for $2,500. Now you accept it. Why? Because for you $2,500 is better than nothing. In reality, three to 5% of people accept my quote unquote, top dollar offer. But now that you've accepted it, I have to go through due diligence or in depth research. I have to confirm you still own the property. I have to confirm back taxes are only $200 I have to make sure there's been no breaks in the chain of title. There's no liens or encumbrances. And because it's only $2,500 investment, I outsourced to my team in the Philippines, connected to an American Title Company. It cost about $11. I was investing $5,000 or more, I wouldn't take any title risk.  You have to close traditionally through a title company. But since this is a smaller deal, everything checks out. And now I'm going to sell your five acre parcel 30 days or less and make a cash flow. So I have a built in best buyer grant. Do you know who it is? Who is the neighbors, the neighbors? So I'm going to send out neighbor letters saying hey, here's your opportunity. Protect your privacy. Protect your views. Know your neighbor. So oftentimes the neighbors will buy now if the neighbors pass a gun to my buyers list, if they pass. A good little website you may have heard of, it's called Craigslist is the 15th most trafficked website United States. I'll go to one I know you've heard of called meta, or Facebook, buy sell groups in the marketplace, and then I'll go to the lands land moto.com lands of america.com land and farm.com land flip.com Land hub.com These are platforms where people buy and sell raw land. But the way that I'm going to do it is the secret. I'm going to make it irresistible for my next buyer. All I'm going to ask for is a $2,500 downpayment, to control this five acre parcel, and then I'll make it a car payment, let's say 297 a month, for the next 84 months at 9% interest. So it's a one time sale, I'll get my money out on the down payment. I could go six to 10 months out. And now I'm getting a passive income of 297 a month, next 84 months. No renters. No rehabs, no renovations, no rodents. And because I'm not dealing with a tenant, I'm exempt from Dodd Frank RESPA. And the SAFE Act, all this owners real estate legislation. So grant, it's a simple game, can I create enough land notes where my passive income exceeds my fixed expenses? And now working?  Grant Because I want to not because I have to guess you have to? So on those numbers, then Mark, when you look at that, and what's the percentage of those that carry all the way through, you know, you're obviously I love, I love how you have the profile for that best buyer, how you create that, that passive income, what sort of risks does that put on your shoulders, where there's potential for them not following through on the cash flow, any issues there? Mark We don't mind it, because we use a land contract. And a land contract means that we can we still own the underlying asset, while they make payments. If they default, they have 30 days to cure their default. If they don't, we keep the down payment, we keep the monthly payments, our cost basis goes lower, we resell that property, we get a new down payment, we get new monthly payments, and extends out a return on investment is... Grant Oh, that's awesome.  Mark So I've been investing my wife and I invest in real estate, but it's it's on. It's with the tenant model, right? It's, you know, creating sort of the cash flow on that site. And it has some of the challenges and headaches you're talking about. Right? It's I deal with property management organizations, I deal with tenants that aren't following through blah, blah, blah, blah, all that. And so this is appealing to me to be able to say, hey, you know, I don't have to worry about about that.  Grant Is there a return that you felt like is higher taking this strategy than taking that sort of tenant based model? Mark Well, our average return on cash is 300%. And on terms, it's 1,000%. Grant That's crazy. Because we you know, typically fight for eight to 10 or 15%, right? In this in this renter model. So that's, that's fascinating, fascinating approach. So a couple things here. The average hold time. So you'd mentioned 30 days, are you literally flipping these in a 30 day timeframe? Is that the average? Mark Yeah, that's average. Now, if it's more than 30 days, something has to change. Maybe we have to raise the downpayment, maybe people think it's too good to be true. Maybe we have to lower the downpayment, maybe we have to raise the price, lower the price, change the interest rate, change the terms, something needs to change, maybe it's as simple as the headline is conducting. So that's our litmus test for what's wrong with our app. Why did that? Why is this not selling 30 days? Grant What's the what's the benefit of turning it into that passive cash flow as opposed to just flipping and taking the cash right now all of it out of the deal and walking away with that in your pocket? Mark Well, cash is a problem, right? Because now I've got to do it all over again. And I've just created a really hard job for myself. So I'd rather buy the asset one time and hold it for as long as I can and have a cash flow rather than flip a tax. Hopefully, I get another deal flip, pay tax, the market could turn on me. Right? I've been I've overpaid and I'm stuck with an asset. Grant Yep, makes makes total sense. So I noticed on your on your on your YouTube channel here. I noticed on there you you've been creating quite a nice playlist for a long time describing some of the challenges the hurdles the things people might deal with some tips and techniques around that. You've been publishing for quite a while on this. If someone were to get started on your YouTube channel itself, you've got this nice entry YouTube video right there passive income without headaches explained. I love that as your entry one. I watched that and you walk through much of the framework that you just described here today, what would be some of the hurdles that you think people will run into? Or that you've seen people run into where, where this doesn't work? Mark Well, you know, the biggest hurdle I think, for people who get started is they think, can I do this in my backyard? Well, if you live in San Francisco, no, no one's gonna sell you an infill lot. 25 cents on the dollar, they're gonna go the biggest, baddest land broker in town. If you live in Manhattan, same thing. So we're looking at properties an hour to three hours from the nearest town. And also, there's 3007 us counties like where do you start? So if I live in Iowa, let's face it, nobody wakes up next boy, I'd like to buy some land today in Iowa, unless you live in Iowa. So to get your biggest buyer pool, you want to be the southwest, little bit the Northwest, California and Florida. These are the sunshine states. These are fast growing states. And there is just a plethora of inexpensive raw land. Grant Okay. All right. That's fascinating. The other thing that I think you wrote your book was in 2017, did I get the date? Right? I think 2018 and 2018. Okay, dirt rich, dirt Ranch is out on Amazon did a review of that awesome book. Tell us about that? What does that cover? Mark So Dirt Rich gives you the basics of how to buy and sell raw land, it also tells my story. So you don't make the same mistakes. I did. And then Durbridge two is coming out very soon. The next plot how to scale your business. Grant I love that the next plot, there's a play on words right there for sure. You're also out there on medium.com. I see you being referenced and talked about there. As well as you got your website, check that out as well land geek, the land geek.com, right, the Land Rover COMM And I'm assuming that is I reviewed that what I noticed about what you have here is this is where people can come to your organization, if I understand it, right and say, hey, I want to get trained in this. I want to get skilled and how to create my own business. I'm assuming this is where you take your years and years of expertise. And you condense that into a program, if you will, that take all the lessons learned rather than take 10 years to learn it. You'll learn it and you know, how long, what is sort of the up ramp for people in terms of amount of time and effort? Mark I mean, can really get going in about 16 weeks. Okay. Yeah. Grant That's, that's amazing. It's amazing. Have you do you participate in any sort of real estate investing other than raw land? Mark No, because I don't have an advantage in any other real estate niche other than raw land. So I'm a inch wide, and a mile deep. So and no one can really get my returns, it would be nice to get depreciation because land lasts forever. But that being said, I don't have to deal with the depreciation like there is depreciation for reason I don't have to deal with any physical structures. So I like the headache free piece of it. And I got it 90% automated with software and expensive virtual assistants. Grant That's incredible. So tell me about your, your best type of client that would come to you and say, hey, I want to participate in building passive income with your program. What does that look like? Who do you know?  Mark You gotta have a burning desire to change your life? Right? You don't have to grit. So anything we're doing in life is inherently hard. And so it's kind of like hockey, right? People who play hockey, really love hockey, because they love it enough to get their teeth knocked out and get back up. It's the same thing in business. You have to love what you're doing enough to get knocked down and get back up. And so if you have a burning desire, give me that more than somebody who has cash. So I always say commitment over cash. It's, that's really all it takes. Grant How much cash did you have when you got started? I mean, it wasn't a ton. Right? Mark I start with $3,000. My buddy Durant's are $800. We think that, you know, 5000 is a is a really easy number. We have some clients who start with $500.  Grant So you really don't need a whole lot of cash is needed that commitment or that grip that that desire to move forward with it. Okay, very good. So, any other tips that you'd want to share with our listeners on on doing this? Mark Well, if I were the listener, I would be thinking, well, Grant. It's so great. Why is Marc teaching it? Grant That's gone through my mind. I mean, You're talking Yeah. 300% To 1,000% return. Mark Right, right. So to answer the question, because when I started teaching that my wife asked me the same questions like, aren't you going to create your own competition? And it's a very valid question. So I started putting on my investment banker hat. And what's the first thing, investment banker looks at? How big is the market, and there are billions of acres of raw land available in the United States. And there is literally do you couldn't think of a more boring niche, like you could go on HGTV or the DIY Network and think, Oh, I'm gonna watch flip this land. The before pictures are all in the after pictures are all in. Plus, there's no hedge funds, there's no private equity groups. So you meet a million people can be in this niche, will all run out of money, before we run out of deal flow. Grant Plenty of opportunity for sure. So is some of this sort of altruistic in terms of you know, you want to give back, you want to see others sort of experience some of the same benefits you have is, is there a component to that that's driven yet to create these systems? Mark I mean, that really is my, my purpose, honestly, because, you know, buying and selling land is great. It's helped five people, really, my family. Yeah, that being said, nobody ever bought a piece of land for me, you know, called me and said, Mark, you changed my life with this land investment, but being able to help people retire their spouses, so they could spend more time with their children, being able to have people replace their income, and really get out of what I call so economic dependency, which means that they're personally not working. They're not making any money, so that they can move up Maslow's hierarchy of needs, into self actualization, and solve not just their money problems, but their time problems, to explore their highest purpose in life. That has been the most gratifying thing for me, professionally. And I absolutely love waking up to that idea every day. Grant This is the "why" of your journey. Right there. That last sentence, isn't it? Yeah, it's helping them to get out of that solo economic dependency for sure. You had mentioned at one point, or I saw when I was doing some reviews on you, five reasons you should be creative, Pat, you should be creating passive income in raw land. What are those five reasons? Mark The first reason is, it's just so simple. All you got is a piece of land and a buyer and a seller. So you know, juxtapose that to like, say multifamily, where you've got to raise millions of dollars, you have to get private capital, you have to get investors, you could spend a million dollars just on due diligence alone. So it's just a much simpler way to go. The other issue is there's just no headaches, nobody's calling you up at three in the morning saying my land is leaking. So you get to go to bed every night, knowing that you don't have to deal with the typical headaches of real estate, tenants, termites, toilets, that kind of thing. So you know, another reason would be that it cash flows. So why not have this passive income come in, and get total freedom in life. So you can work when you want, where you want, and with whom you want. I think another reason is just that there's no limit to it. So you can grow as big or, you know, you can get to a point where you kind of like the Mexican fishermen, you have enough type of thing. And then I just think the other reason, it's a lot of fun. It's a lot of fun. And it's not building another job for you, yourself if you just automate it. And I think that's great. Grant Yeah, yeah, that is great. It's interesting that you found this niche. I love how you described it. It's narrow and yet very deep, you become very specialized in this. That's awesome. Mark. Thanks for taking the time with us today. Any final comments or tips you want to share with our listeners? Mark I always love this quote from Zig Ziglar. If you'll do for the next three to five years, what other people won't do, you'll be able to do for the rest of your life. What other people can't do. Grant That's a very enabling. I love that for sure. Gosh, that's awesome. Mark. Thanks for taking the time, "The Land Geek". I appreciate you doing this. And everyone thanks for listening to another episode of Financial Investing Radio. And until next time, check out thelandgeek.com  

In this episode, I have the opportunity to talk with a self made entrepreneur who's followed the footsteps of her amazing dad, and in the journey has discovered what the tips are that the wealthy use to invest to change your life. Grant Hey, everybody, welcome to another episode of Financial investing radio. So I have been trying to track down this person multiple times and admitted it was my fault. I could not get my calendar right to meet with Stephanie Walter. So glad to have her here with me today. I'm fascinated with her background, what she's done in terms of growing wealth. But before I go any further, Stephanie, welcome. Stephanie Thank you so much for having me. I appreciate it.  Grant Yes, this is fun to have you here. Your journey is a fascinating one to me, because it's this journey as I reviewed it, of gathering some financial capabilities, but then not resting on that, but rather using it to leverage for future wealth. So I don't want to give that away because I want I want our listeners to hear this cool journey that you've gone on. But let's rewind. Okay, let's uh, we won't go back to where you were raised as a kid. But let's go back to you get out of school, and you're thinking I'm gonna go do some work. And that led you down a certain path? Would you talk about that? Yeah, I did what like most people do, I got it. I just got a regular job with a corporation. Stephanie I had some interest in insurance. So I became a claims adjuster. And, and I sort of moved up the ranks pretty pretty quickly to where I was working kind of as a liaison between the attorneys in that represented the insurance companies and, and the insurance company. So that was, that was really interesting, but just, you know, working in a corporate setting, I remember that, you know, my pivotal point was I was getting a 2% Raise after my superiors had said, what a fantastic job I was. You're welcome all 2% Did you get to keep all that 2%? Or did you know you got to keep?  Grant That's impressive. Yeah. Stephanie And I went home to my Dad, I just bought a house and was like, Dad, I just, you know, look at how much gets taken out. But with taxes, it was a big learning experience. I mean, well, as you know, I did put, you know, seven years into it, but just realize that, you know, if I'm making these 2% races for the rest of my life, you know, what is that going to look like? And my dad was an entrepreneur, actually, he's a second generation entrepreneur. And he's like, Well, you know, what you're gonna get if you stay in this, but if you go out on your own, you know, really what you build will be up to you. And it's your choice to do what you want to do. And I gave my my two week notice probably the next day, did you really? Oh, wow. That's okay. Wow, a woman a decision and action. That's awesome. Grant So, all right, so you gave you gave the two week notice you left, and then you didn't go to the pool? That's for sure. What did you go though? Stephanie Well, I have a I have a lot of relatives and insurance. And that's kind of how I started in doing in claims to begin with. And so I just knew, you know, the company I was really familiar with, went and signed up with them, took all the classes to become an agent and and just, you know, started started working, you know, right off the bat, state and insurance agent for about 16 years.  See, I started that I'm trying to think of like timeframes, I think it was 2004. And then right around that same time, I kind of decided, you know, with the when the bottom fell out to buy some real estate, single family homes, right in an area in Denver, I'm a native, as I mentioned, where I felt like if there would be growth, from Denver that, you know, over time, these would probably be good, good investments to have. And that was in the Sloan's Lake area if people are familiar. And so I became, you know, a untrained landlord, as well as a business owner at the same time, all in one shot. So quick question. So when you did this transition out of the corporate world, into the entrepreneur going to do myself. Grant How many entrepreneur books did you read before you got started? Stephanie You know, I? I didn't read that many.  Grant Yeah, that's like I could tell you just jumped in and went after it. Right? You got going on. Stephanie I think also my my step up in that area was I'd see my dad growing up who had never, who had always had businesses and so to I never saw him work a nine to five job, you know, have a two week vacation or anything like that. So I had a very good example of what an entrepreneur looks like. And so that's probably why I didn't need to read books. I have since read a lot of books.  Grant But yeah, yeah. But you had a role model, you had a mentor to follow after. Alright, so you go through this journey, you run your real, or excuse me, your insurance agency for 16 years. What happened? Stephanie I mean, as time went on, there was you know, also some distaff dissatisfaction. Because you, you know, you really like your clients, and they're doing business because of you. But then there's this big, you know, corporation behind you, mine was Farmers Insurance, who, you know, ultimately makes the decisions based on, you know, the claims and the the rate changes and things like that. So that became kind of frustrating. But then also, you know, again, my dad was, you know, pretty. He passed away, actually, shortly after I started my agency, but I'm sorry, yeah, thank you. But I had, I remembered a lot of things as time went on. I was like, Oh, I remember when Dad said that. And one of the things he always said was, don't put all your eggs in one basket. And that was for me, that meant just, you know, don't continue, farmers was very big on, take a loan out, to keep running your visit business, make it bigger, make it better, all these things, where I just sort of directed my money in my growth into real estate, because I felt like it wasn't so attached to to the business. And I loved real estate. I didn't know much about it. I certainly wasn't very educated about it. Oh, I think it was 2016 I had gotten an invitation to a boot camp and about buying apartments, which I had always been curious about, oh, cool. Went into there. And that's where I first heard the term syndication. And I was I was just blown away, I'd never heard that I loved the idea of a group of people buying something that no one could do on their own. And I from that point on, I was all in there. I did about three years thing me maybe it was only No, I think it was only two years of education, which, you know, was an investment, but you come out of that bait being able to be very knowledgeable about commercial real estate, which is very different than residential real estate residential.  Grant So if I hear you, right, I think you said that this transition to real estate, it was a diversification strategy for you following your dad's counsel, right not to have all your eggs in one basket. You still had the insurance company at least for a while, right? And then then you started investing in the real estate, is that right?  Stephanie Yeah. i And well, I had all I had invested, you know, in the single family homes and they're, you know, through the last crash and then wanted to do the apartments and then in 2016, that really was probably, I mean, I kept running my agency obviously, until actually last year when I Um, my investing had my income from investing had exceeded my, my business income. So I was able to retire, or in my case, just do do a different career for a while. Grant I love that when I saw that in your profile, I thought that was the ultimate, which was to sort of break away. It's that, hey, I'm no longer just going to be a business sort of operator, right? I'm actually going to step aside and let let your income or your assets generate that income for you, right. It's the money's working for you. And I saw that in your notes too, which was a, you said some interesting about the way the wealthy do it, which is the wealthy have their money working for them? Was that always at the forefront of your mind? Or did you sort of discover that along the way?  Stephanie No, that's a that's a big, I think it was big, the aha moment for me. And how I describe it is that I believe most people, and I was one of those most people believed in an accumulation model of money, which meant for most people is I'm going to accumulate the money in my 401k. I never really, you know, went down that path from owning my own business, I wanted to accumulate my money in real estate. But yet my idea was the same, which is I'm going to buy this property and manage it for 30 years until the loan is paid off. And then I will live off of the rents or whatever at that time. But when I started raising money for the syndications, I started becoming friends and, you know, meeting really, very interesting, you know, wealthy people, and just notice that they were doing stuff differently than than I was, I couldn't quite put my finger on it. But after a while of working with them, I realized that they look at their money as a good word is utilization. They're always using their money. They're using their money to give them cash flow. And there was kind of the light bulb that came on for me. Later on. You know, I started doing syndications in 2018, and I think it was the end of 2019 that I started to sell my properties one by one, and invest them in syndications. Because I realized I had these this great big chunk just sitting in and it's really not doing much as far as you know, cash flow, helping me out at all. Whereas, you know, if I just shifted my focus and where I put my money, the returns as far as cash flow, were really significant. Yeah, Major.  Grant Have you ever played that game called cashflow there? I've heard about the Robert Kiyosaki Robert Kiyosaki game. Yeah, I've never played it, you shouldn't play I have a feeling you do really well with it.  Stephanie Yeah.  Grant Excellent with cash flow is king, as you're as you're pointing out, and the ability to get that cash flow lined up and consistently look at money as a tool to deliver the cash flow is critical. So that 401k experience, I've had that same journey where I was putting money I was putting money in in a 401k. And then the first time the market in my career, you know, did this massive dive down, right, and I'm sitting there thinking, I know that there's someone on Wall Street's buying puts against my investment making a ton of money while I'm losing a ton, wait, who came up with this strategy, right? You know, also you could keep your you know, 2% Raise, it is it is a disservice to the working class for sure. So, did you did you ever get involved in the 401k strategy? Or, or did you ultimately just leave it wouldn't? What happened there?  Stephanie Eight, actually, I have my series six, when to be a n 63. So for the audience, that just means that I was registered to sell products, you know, mutual funds and things like that to my database of clients. I never really like to that I never really, I felt the variability and that the training that I was given never seemed to give me a lot of anxiety. And so I never really did much with that. And inevitably let those those licenses go. But though the 401k Actually, I'm in the process of writing a book and there's going to be there's one chapter that's dedicated to the 401k, which I do a great deal, you know of research on and you you pit. You know some really good points on on that. And but it's just letting people question it because there are so many things about the 401 K that are, you know, you're giving you're giving your money to someone else to watch. And they're not doing it for free. And financial institutions want to hold on to your money for as long as they can and give it back, as you know, little as they can. And so you're you're using your money to subsidize things that you probably don't even know your money's doing. And I know, these are hard things to come to in this day and age when it's just like, can I just give my money to this person and have him manage it? Or have the 401k make those decisions? Why do I have to be involved with everything, it really is a significant amount of money that you pay to these financial institutions. And you can do much better on your own. But I won't go into that. That's a whole nother discussion.  Grant That but that's your journey, though. I think your journey is you figured out Wait a minute, I can do something differently with this. And you took control of it right? You did the education you put in the work, you discovered cash flows. The secret to this, let my money work on just building even small incremental cash flow growth and how critical that is in the strategy. Stephanie One other point just quickly is I was watching something on TV last night, and there was several commercials that came on. And they were women. They were geared to women and there they were talking about, you know, women and finances are bad with finances. And the whole message in this commercial was save your money for retirement or whatever. And I was like, I want to just get out the message that it's not saving your money. It's investing your money and learn how to be a good investor, because those are the things that are going to allow you to retire early or retire in not a poverty situation. But in you know, invest and learn how to invest because it isn't as hard as people think it is, you know?  Grant Yeah, absolutely. I love that and are good. I understand. You're right. Did you say the messaging in that TV ad was that women are bad with finances? Is that what you say?  Stephanie Yeah, I think well, they were trying to you know, women were kind of trying to empower themselves by saying, I'm not bad with finances, even though you know, I've been told that women are bad with finances, but it seemed like the solution for them in that commercial was save. Save your money. You say? Like, no, no, no. Yeah. Yeah. Ah, good. Good. Debt should produce cashflow. Right. That's, that's actually that's actually the message right there. One of the things I noticed as I was looking at your background, and the things you've done, as you had made a comment if I if I've got this right. Grant Money myths, you talked about money myths, and I think you just touched on one of those. What other money myths? Have you learned that we hold on to that? Are these incorrect notions that actually hurt us financially? What have you found?  Stephanie There's one very significant one, which is people will say, well, the wealthy people, you know, I've talked to people and they'll be like, Oh, but the reason the people you work with have so much money is because they're willing to take these crazy risks with their money, and stuff like that high risks. That's that's why they got where they are, they are and I say actually, nothing could be further from the truth. The majority of wealthy people invest in extremely conservative things. They, the other myth is, you know, put put your money into the stock market. Many people can't tell you in their mutual funds, what they're invested in, what even one company is that they're invested in, let alone who's who are the team members on that, you know, on board or who's the CEO of the company, they can't tell you anything. Whereas wealthy people they tend to invest if they do invest in businesses, they invest directly in a business, either for themselves or they understand the dynamics of the business and the business plan and they invest that way. If they invest in real estate, they largely do the syndications and they get to know the team that's running, running the syndication. They know what kind of experience they have, what kind of past returns they've done. They do their homework in that sense. But then once they've invested in the team, they tend to invest again and again. And these are very conservative things that they're investing in things that are tangible in value. That's another. That's right. Three things I'm hitting is wealthy people tend to invest in things that have tangible value, which means, you know, an apartment complex Well, let's say, for some reason, it's terribly mismanaged and it goes out of nobody wants to be in it anymore. Well, you still have the building and the land in which you can sell. So there's, there's tangible value there. Grant Okay, so the tangible piece, that's interesting, too, especially in today's world, where digital assets are becoming more and more of a thing, right. In fact, I saw recently, someone talking about digital real estate on one of the online ads, you know, doesn't feel tangible, right. Are all the NFT stuff going on? Right are the crypto so many intangibles today as well? It Do you have any position or thoughts when they do that?  Stephanie I don't because I guess I always take the line if I if I don't understand it, or if it's not something that I want, you know, I guess it comes down to really understanding it. I've had a lot of people explain it to me, but I still don't, you know, the ups and downs, you know, lately, it's been a pretty big crash, you know, people are saying that's a good thing. Okay, I still don't really understand it. And I know that they're planning on, you know, digitizing, then that's, that's probably, you know, not too far away in real estate, you know, 10, I would hope it's going to be 1015 years in the future. I don't know what that looks like. But that's definitely something in the future. I don't think that is wrong. And when I say that the wealthy people invest in, they probably do have some investments in cryptocurrency, but that's probably less than 5% of their portfolio. Great majority of what they're invested in, probably 30% Every year. If you go on to the name of this group is called Tiger 21. And it's, it's for wealthy, wealthy people, I think they have to show a net worth of at least five or $10 million to get in the group. But every year, by agreeing to be in this group, they agreed to release as a group, kind of a asset allocation of all of their investments, and every year, there doesn't change that much. And over 30% is in real estate.  Grant Really. Okay. All right. That's interesting. That's fascinating. One of the things that I noticed as I looked at it now Now the name your company, you're gonna have to help me because is it Erbe Wealth? This Erbe?  Stephanie Well, Erbe Wealth, okay, everybody. Well, thank you Erbe Wealth.  Grant Well, so I'm on your site, I was on your site. And I was checking out. This is really cool. erbewealth.com. And I went to the about page and told my listeners, you should check this out. Stephanie's got this thing called the 15% Plus community. Can you talk about that?  Stephanie Yeah, well, I mean, my partner and I started working together in 2018. And we both realized, as many successful pairings go is that he had some skills in in this in the certain areas, and I had skills in certain areas. And together, we have really done very well together, and we just closed on our 12th deal about two weeks ago. And every single deal that we put together has returned over 15%. But truthfully, every every one that we've done up to this point has returned over 20%. So the person designing my website said, I don't know you might want to just put that down to 15%. But every deal that we have done has had an annualized rate of return of over 20%. So if you're we, our goal is when we hold the money for three to four years, then we'll double your investment in that time. And we have we've done that successfully, and we have a system and we're will we're continuing with it.  Grant That's amazing and that's that's leveraging the syndicated real estate strategy.  Stephanie Yep, that's we buy apartment complexes and a very specific market in the country, we have a very specific buying strategy that allows us to get in and make money when we purchase it, purchase the property. And then we just find areas where there's there's been a lot of growth, and there's been a lot of rent growth and population growth. And I think if anyone's been listening to the news is we know that there's a housing shortage. So we buy in areas where, you know, there's a great deal of population growth and not enough housing,  Grant What is your what is your perfect client look like? What's their profile? Like? Stephanie I mean, I would like it to be more broad than than it is, it's usually, you know, well, to invest in our deals, you need to be accredited, which, you know, that means you need to have a net worth of a million, or you have a $200,000 salary. And so I love working with business owners, that's kind of my thought to I tend to attract a lot our business owners, because, well, one is, they're so busy trying to make their business work, and I'm talking more like smaller business owners, you know, and, you know, trying to manage their company, which they're very passionate about, but business owners tend to not really plan that well for their retirement, because they're just, you know, they're thinking all about this. Yeah. Run on the business constantly, right? Yep. Right. So those are, you know, those are the people I love to work with, just to you know, get them some cash flow, that that is nice, but as well as just having, you know, great returns that they don't have to manage, you know, at all.  Grant So, okay, very good. While you've been very generous with your time, can you give our listeners a place to go to to learn more about you? Yeah, to your website?  Stephanie Yep. That's my website, which is erbewealth.com. There's, I have I think, right now, it's not a lot, but it's about 15 articles that I've written, that just I try to really educate the newer investor that isn't familiar with this type of investing. And then there's a track record of of all of our Not, not cherry cherry pick deals closer, every single deal that we've done together, up until this point, and then you can join, you know, the list the email list to get notifications, I like to really educate my investors, as well as then they get the first, you know, chance of getting the new investment when it comes out. But air Bay, actually is the German word for legacy. And my dad was a second generation, my grandfather came over on the boat right from Germany. And he became an entrepreneur after he paid his dues and did everything he needed to do to become a citizen. And then my dad, you know, followed in his footsteps and was an entrepreneur.  Grant So I was gonna ask you about the backstory on that name. I was trying to figure out Erbe. What is that? Yes, that's awesome. I appreciate that.  Stephanie Yeah, not to my dad, who never you know, saw any of this, but definitely, it's because of him that this has happened. I can tell you have an awesome dad. Really cool.  Grant That's awesome. Stephanie, any final comments that you want to share? Stephanie No, no, but I'd say you know, just just check on my website. I'm trying to, like I said, working on a book and that that'll be my next. I'm hoping to have it done by the end of summer. So when when it's available, it will be available on my website as well.  Grant That's awesome. Stephanie, thank you so much for taking the time with us today, everyone.  Thanks for listening to another episode of Financial Investing Radio. And until next time, check out erbewealth.com.  

In this episode, I have the opportunity to sit down with Christopher Nelson. A technology executive out of Silicon Valley who's figured out the secrets to helping you achieve passive income as a W-2 employee. Grant Hey, everybody, welcome to another episode of Financial Investing Radio. So today I have in the house with me, Mr. Dr. Why don't want to call you. Sophisticated, brilliant. Christopher Nelson is in the house here with me today. I love his subtitles technology executive real estate investor, author, podcast hosts and inventor of the space station. Was that right? Or did I get that right? Christopher I think you may have stretched on the space station. But I you know, space has always been a dream of mine. Right? I am a low tech tech technologist, a bit of a geek. So yes. Grant Excellent. Excellent. Well, okay, so let's start with that. technologist geek. What does that mean? What? Tell me about your background? Christopher Yeah. So So my background, you know, interestingly enough, I started with just a love of technology, right? You know, I heard some people tell stories, the other day of how I think exploring and wanting to take apart, you know, televisions and, you know, anything remote controls, understand how they worked. And I just was fascinated, especially as the computer age started taking off. What can we do with these? So, you know, my thought was, I wanted to go to university and I wanted to learn how to become a software engineer, so studied very hard. And then when I went to my first internship junior year, with a large database company, and I was by myself working on this code, I started going crazy. I started thinking, what am I going to do? Like, I, I love this, I do not like this. And it was actually in a job fair that I met somebody who, who had a similar major who was working in technology consulting, he said, Hey, there's different things. It's not all you have to be a software engineer, I solve difficult technical problems with people moving businesses forward. And so that really, I mean, hit a lot of cylinders for me and took me off into technology, consulting, which is where I started my career. Grant That's awesome. I love that background. i It's funny, I didn't know that about you. I had a similar journey. I come through the technology world. But I ended up bouncing from doing my first startup right out of college. Because when you come out of college, I don't know if you know this, but you typically have all the answers, right? So so I jumped, I jumped right into startup, I'm gonna go solve these world problems. Clearly, everyone's having struggle with course, I failed miserably on my first startup. So then I jumped into the technology consulting world with back then one of the big six, you know, in Chicago doing some technology work. But yeah, it's funny, I jumped to the people side of it, also. And that, that was liberating for me, because if I spent all the time just and I love I still code today, don't tell anybody. But I use it as a tool to keep my brain active. But if I had to read every day, all day, I think I'd go crazy. Christopher Right? And, you know, it's, I actually tried to now actually take that message to people, kids graduating from college is the fact that you can actually be involved with technology. And it doesn't have to be this one way like we see, you know, again, media, movies presents this one view, but there's a lot of other things that you can do. And I found that it was great start to my career, to really get me launched. Give me a ton of skills. Give me a great background, because I did I went to work for Accenture, right that came out of one of the you know, the big six became the big four. Yeah, it was, you know, one of the big four back in the day. Well, well, we're constant names.  Grant Yeah, I did. KPMG. So there you go. Yeah, technology. There we go. Wow. Okay, which office were you located? Where were you? Christopher At San Francisco. I mean, I felt like that was a great opportunity because I while I was doing the tech consulting thing I did want to work for high tech, like I wanted to work for companies that were really, you know, moving the needle. And that's actually set me up. So about eight, nine years into my career, I realized that I had this great experience. And this is what I call in my book, Your career capital, which is a combination of your education, your experience and results. And I realized I could actually go trade this for equity in startup companies, because like you said, I've been working for these tech consulting companies, I got that figured out, right? Well, I went to work for my first startup, abject failure, right, I found myself and then a year nursing and Ulster having a bad boss. And I didn't know how I got there. But taking a step back, and I think a lot of us figure out pass through failure. And so I don't think we should fear that. And I realized I did not look like I did not think like an investor with my time. Like I'd been, you know, in the in the stock market investing since, you know, the 90s. But I had not thought like an investor with my time. So went back, put together a due diligence criteria started work in my network, what did people recommend? The next company I chose employee for 17, at a company called Splunk, in 2011, and up IPO in 2012, and it turned out to be a big win for my family. Grant Yeah, that's absolutely cool. Yeah, Splunk, boy, they have done a great thing for the industry. It's funny, united, similar routes, I, after I did my KPMG route for a number of years, I had this itch to get out to Silicon Valley. And so I was chatting with this startup company out there. And, and I said, my wife, you know, I'm just gonna fly out, you know, we're in Chicago, the time's gonna fly I just checked, probably won't do this, right. Uh, probably, but I just gotta go check them out. And of course, I get in there. I'm talking with them. And after several hours of whiteboarding and brain, brain exciting things, they go, do you want to, you know, come join us? And I'm like, Oh, I think the answer is yes. You know, and I remember being on the phone in the little conference. You know, maybe my mind's changed. You know, I think we're switching. We're moving out here. Yeah, I did several startups out there as well. And those course weren't much different. So you know, you need to have that first one that kicks you in the teeth, right? And it's like, okay, wait a minute. You do, you do? Christopher And I think sometimes, right? It is, you know, those those learnings that help like anything else, right? I mean, this is where I'm trying to now build this thesis and conversation around, it's an investment of your time. So sometimes you go, you get skin in the game, and it's not the right thing. But then does that mean you stop investing altogether? No, you go and adjust your lens and you you go, and you get feedback, and then you go, and you do it again. And maybe this time you say, I'm just gonna, in my head, I may sign a four year contract, maybe let me just do two years, see what's there and track it as they go along. You're going to also be looking at those quarterly meetings, when they're giving readouts a lot differently, you're going to look at the market a lot differently and say, What can I do with my time? Grant So I think that's one of the cool things that I noticed about you when I was reviewing your profile. And it was this, it was not just let me go after doing this startup. But it was to leverage what I'm getting out of this either equity opportunities, but to move it into some passive opportunities. And I think that was, for me a real Aha, like, you know, earlier in my career, I wasn't even thinking that right? I'm doing the various startup companies. I'm like, go, go, go, go go. And you think you've got all this endless energy. And at some point, you think that way, but what I love is a year connecting the dots from take that energy and translate it into this passive mechanism that's going to benefit you. So I don't want to steal your thunder. But talk more about... Christopher Well, well, what led me to that is it was actually that first IPO. So all of a sudden, you know, I'll never forget that day was you know, April 18 2012. Phenomenal day. And but the most memorable moment wasn't this big party, the fact that it went out double what we thought it was, was when I went home that night, my pregnant wife is sitting there, she asked me two questions. She says, When can we get the money and when can we buy the house? And I had no answers. And I literally went into the next room and started emotionally, you know, falling apart a little What I didn't realize is I was experiencing what's called a sudden wealth event, which is sudden wealth syndrome, which is a psychological syndrome that when you have these things you feel lonely, depressed, stressed out. If you're not prepared. You're not and I wasn't like I'd fought so hard to get there like most of us do. I wasn't prepared aired in so what I found grant is that many people aren't prepared like they like and I literally have been interviewing a lot of people from my book a lot of technology in place. And they will. I heard Mark Cuban say like you have to work for equity if you want to get ahead or, you know, I heard Gary Vee or somebody, Robert Kiyosaki gives them the idea that they want to be an owner. They go get it, and then that's it. They stop. And so my experience was okay, now we got this. I was okay. How do we now start getting this? You know, because I was tired during the six month lockout period, over 90% of your wealth and a single stock. I'm telling you, man, like I ate a ton of Tom's during that time, like you do. Yeah. Yeah. So it was really been this plan of, you know, how do we start moving it into something passive? How do we get it to that, you know, Kiyosaki goal. And so as we were doing that, as we were, you know, scouring the earth in the Bay Area, trying to understand it, we started getting exposed to real estate syndications, we started getting exposed to crowdfunding. And we realized that we had to get sharp on the math, we need to really understand how to do diligence, how to, how to invest. And, you know, we went on a journey of where we figured out how to start getting some skin in the game that, you know, was something that we could afford that we could afford to lose, and start really understanding those types of investments. And as we started seeing more and more success, other people started raising their hands, because we were very excited, we would talk about what we're doing. We always feel, you know, with our children with our friends, we should talk about careers and money. Like we should just have the conversation in an appropriate way. Not in a braggadocious way. But in a What are you doing? What are we doing? Because if we don't talk about it, you know, like you do here? Yeah, we don't do anything is people who sit on it. Yeah. Yeah. Grant You do, you do sit on it. Absolutely. So. So tell me about I think I saw something that you wrote. "From No Dough to IPO". I love that. Christopher Yeah. And so this is, this is the first book that I'm writing. And it's really going to be a playbook on how to tech employees think like an investor when trading their time and talent for equity. Right. And I want it you know, you said it grant like, nobody gave us the playbook. Right, we had to go in there. And we had a trip over our own shoelaces, you know, get beat up, figure this thing out. I you know, I have interviewed people in my network who have done three or four IPOs serially, I've interviewed people that have never done anything pre they've all been in post companies, and they get these companies public, and they take them from, you know, 500 million to a couple billion dollars. And they've gotten tremendous wealth, already liquid the whole time. And I've turned it around and put it into a framework so that w two employees can really be time investors and say, you know, for myself, I had lost a small business. That was my side hustle, I was broke in, I realized I had to go and work for startup companies if I wanted to get ahead. And I think many people are trying to figure out, how do they leverage their skills, their time and their talent to do that? I wanted to put it out into a book that says, here's how you actually set yourself up. Here's how you negotiate for the equity. And then once you get it, what do you do you need to plan protect, and then you need to start producing passive income. Grant So that framework that you've alluded to, and obviously it's in your book and what it is that you provide in terms of services to others? Sure. In that framework, how far does your organization get involved in terms of taking people through that and applying it as a good go all the way through into the real estate piece itself? Or tell me more about that? Christopher Well, so I mean, the book is really launching the education platform. So right now the book is really the key anchor, we are looking to follow with some online training that people can do. And we're considering some coaching programs. But yeah, I mean, getting back to the origin story is that this this endeavor started from us doing done for you investments for technology employees, my wife and I who were actively investing in, you know, multifamily in ATMs and mobile home parks. We were asked by our friends to help start creating some vehicles for them because they wanted the success they didn't want to go put it together. So that's how Welford capital was born five years ago was in in an effort to help technology employees and you know, we're the lead investor, we're leading with our families dollars, we're creating a model portfolio that's based on where we've taken our tech equity and how now we've put it to work in real estate with our my wife and I shared goal the goal of getting to $300,000 of passive income a year. Grant Yeah, that's awesome to get to that. Point, what's the trajectory? Like? And I? I'm sure part of the answer is how much you're putting into it financially. But in order to in order for someone to get to some, I guess what I would call, in fact, I was watching on your YouTube channel use an interesting phrase I'm looking at right here. It was something like, yeah, you wanted to get to a point where your passive income could overcome your W two income, you made that video. So I realized that's gonna vary by person. But yeah, sure range like, Is it A, is that a two year or five year journey? What does that look like? Christopher Well, I think to your point, Grant, it depends on, you know, how much you can deploy, I really think that it is a five year plus journey. For most people, I think that if you have if you're sitting on a ton of dry powder, right, if you've had, you know, a couple of exits, you know, still it's gonna take time to deploy that capital. Right. And this is what I tell people is, if you walked in, you know, with $10 million, you would not want to put that into a single real estate investment, because then you're going to have the same thing you should if you're sitting in the single tech stock, you're gonna be not diversified. Right? So I think on average, it's, it's, it's a five year journey to really start making some impact. And this is why, you know, I want to go upstream to people earlier in their careers thinking about it, because my honest belief is that if I can free technology employees from having to work for a W two, many of them still want to make an impact. And this is the conversation I love having is if you didn't have to work for money, what would you do? You know, eight out of 10 tech employees don't say I'm gonna go travel the world, they'd be like, I want to solve this problem, or I want to fix that problem. And that, to me, is is really powerful in our community, right, as a technology employees is how can we unleash that power, and allow people to do the impact work and not be focusing just on the survival work? Grant So I have a fundamental question for you, when I saw that your focus was on the tech space employee. I'm assuming this could apply to any sort of w two employee? Or is there something about what you're doing that says, This is really relevant to a tech space employee? Christopher Well, so the reality is, is, you know, I want to be able to speak into my people, my focus, my niche is focused on technology employees that I really understand, I understand, you know, their concerns, I understand, you know, you know, how it is, it's a very high paced, fast paced lifestyle. And so, in. So that's where, like, my writing my books, and everything is very much focused on how do I help these people and try and pay it forward, if you will. Now, on the real estate side, it's interesting that I've been very focused on technology and plays in helping them however, different people relate to the story, right? I mean, I have people that come in and say, Oh, I've I've gotten equity, it hasn't been in tech. Can I invest with you? Of course, you can, you know, or, interestingly enough, I had a jockey that reached out and wanted to get a call and said, I actually won this big stakes race. That was my big one. And I went through the sudden wealth event, I really related to what you were talking about that, can I invest with you? And so of course, I'm open to everyone participating in our investments, but I just feel really nobody feels sorry for somebody who goes through the sudden wealth event, right, you know, works in an IPO, all of a sudden, you get a multiple seven figure, payday, nobody feels sorry for them. But those people still struggle. A lot of us, myself included, didn't come from money, had no idea what to do with it. Yeah. Right. And so it's really, you know, in my mind that I want to really help and lean back into my community, because as I mentioned before, like I think that that community, financially independent will actually then turn around and bring some huge benefits to to everybody. Grant I can relate with that, I grew up milking cows and hauling hay. So yeah, that was that was that was the foundation of that. So I'm interested in your thoughts around because one of the cool things I saw that you did was you're helping people think through their career paths, and with everything that's taken place with COVID and the impact to the workforce and and even what people are expecting from their jobs today and how that changed. The mindsets changed. How is that impacting the things that you're doing? Christopher Well, I think it's creating more opportunities, right? I think coming post COVID I mean, it was in the middle of COVID. I ended up going back to work for another technology company. I had a very interesting offer. And it was for an all remote company that was built all remote they came it was developed all remote in 2011. And it had an An amazing trajectory and amazing story. And it was important for me to really experienced that because I think that the future of technology employment, while I know a lot of people are being brought back into the office, I think it will truly, ultimately become hybrid, that where you're going to have office time to connect, you're going to have a lot of home time. What this allows for, you know, I think certain roles, certain specialized roles is the ability to make tech money and live in the low cost, you know, income or a low cost of living areas. Yeah, that wouldn't change. It is in this to me provides opportunity to what to save more to invest more, right? There's this whole concept of people talk about lifestyle by design, well, what if you designed your lifestyle to invest more, and I mean, this is what, you know, really motivated us to leave the Bay Area, come to Austin, Texas, in 2017, where, at that point, we got in here at a much lower price point, we are able to sell our house in the Bay Area, get some single family homes and this home, so we're able to now more passive income, right, but lower our overall cost of living so that then we can invest more, I think there's going to be more of that type of opportunity. I think that, you know, there, people are going to be able to have much more flexibility in that geography, which I think creates more overall opportunities. Grant Yeah, definitely see the the change there with that. And in fact, having so many people shift and move around the country right now you see this sort of exodus from some of the coasts, you know, coming in to the Central part, companies, large companies that we've seen, of course, moved out of places like California and such, you've talked about in your material that I reviewed, on your YouTube channel, some of the reasons to invest in real estate, and why you picked that as an avenue for doing this. There's obviously lots of places certainly crypto which has, of course, been kicked in the teeth, you know, recently, but, but why why real estate, he touched on that. Christopher I would say the most compelling reason right now, to be investing in real estate is the income by like, a lot of, you know, asset classes do not provide, you know, to me, there's there's the construct of a investment that says I made for income. Income to me is not a growth vehicle that says I'm going to take your money and expand it know, an income vehicle, like real estate, or like what we used to have in the bond market, you know, pre 2008 is, I am going to preserve your capital, potentially increase that, and I'm going to give you returns on top of that. I think that real estate is the place where then you have a hard asset behind it, that again, gives you you know, those those multiple benefits, I know that, you know, four ways to make money in real estate, right, there's the cashflow, there's the appreciation, there's the equity pay down, and then guess what the tax benefits as well, right? We know that we need a lot less real estate money to duplicate w two or where we want to be with w two income because of the fact that we don't need as much because you get a lot of the depreciation, you really get, you know, good tax planner going there. You know, you can you can reduce that. But this is where, you know, I don't see any other vehicle right now that's going toe to toe that can preserve capital and put cash in your pocket. Grant Yeah. What are your thoughts about current trajectory, you know, around interest rates and potential soft landings of the real estate market? Is that create any concerns for you? Christopher I mean, it definitely creates concerns. I mean, I think that a lot of the the increase in interest rates has slowed down a lot of the transactions on the real estate front. I know that for, you know, multifamily, you know, partners that we have here in Central Texas, you know, there's just a real long pause, like, where's this going to land? And I know, a lot of, you know, investments that were in process, you know, now have have stopped because, I mean, just negotiations because the rates are moving so quickly. I do believe that, you know, this increase in interest rates will do what, you know, the government is looking to, which is going to slow down, cool it off, prices are going to have to start ratcheting down to excuse me move the market forward. But however, in real estate, there are opportunities to buy I know that I'm very bullish right now in the mobile home market. I know also in the self storage market, there still are a lot of opportunities because the fact that those industries have not been standardized, let's say like multifamily, or a lot of you know of some other commercial properties as well, there still are real buying opportunities there. And so I think if you stick to the fundamentals of Buy low, get something that's cash flowing very well, there, then is the opportunity to still make money. Grant Is there a particular asset class real estate that you enjoy more than the others and mobile homes? Is the multifamily? Do you? Is there a particular place people should start, you know, when they think about this? Christopher Well, I mean, I think everybody should start with with the fundamentals. So there's a, a book that I recommend to everybody, it's what every real estate investor needs to know about cash flow by Frank gallon le, and it's a math book, and it gives you the fundamentals. And you go online, you get a spreadsheet, and you understand the math, not complicated, but that's where everyone should start. And it gives you a breadth of asset classes. For me right now, I am bullish, and my partners and I are acquiring in the mobile home park space because of the fact that it's a scarce asset class, scarce meaning that's in high demand. And for the demand, they're not making any more. So that means that if you look at, you know, I have a chart that I share with people that the net operating income of mobile home parks has been steady up into the right since before the year 2000, because of this whole thing that, you know, when you have a scarce asset class, that is going to force appreciation. The other thing is, you know, there's there's a lot of inefficiencies, and a lot of, you know, mom and pops that are baby boomers that are looking to get out right now. So, you know, with a good operations team, there's, there's a buying opportunity. And I do want to I think one of my partner said the other day, and I love this piece of advice to say when when times are good, you want to be in affordable housing, when times are bad, you really want to be in affordable housing. Well said, yeah, right. I like that. I literally wrote it down. As soon as it says, I go, that's gold, that isn't that is good. Grant I picked up that wealth word is a verb. That's one of the things that I discovered in reviewing what you're doing. Tell me more about this verb. And what I mean by that is, I'm trying to relate it to those that are listening, where they may or may not be in the tech space. But let's say you're in the tech space, and they're early in their careers, and they're thinking, How do I get started? What do I do next? What's the verb? Right? What's the action that I should do? What I start doing differently? Christopher Well, and so the whole concept is always be moving towards wealth. And this is always just having your eyes and I do have spec. So you know, having your investor lens on. And so I think that, if it's in your W-2 job, you know, it really is then how can you work for equity? How can you get a piece of ownership and this, you know, I call it Kiyosaki is bridge, like he doesn't call it out in his book, because he sort of has, you're the employee and the owner over here, there's this gap. But equity when you're able to get, you know, again, can that be in technology companies? Sure, could you, you know, I know people work in private equity companies, they get staked there, you know, and other public companies, you can too. But if you're working for equity, and you have salary, you know, a bonus, and you have this equity, what I always tell people, then you can live on your salary, you can party with your bonus, and you invest all your equity, you know, that then gives you this additional capacity to be able to start investing. And then, you know, as you and I both know, on the investing front, it's how do you stay diversified? How do you have a portfolio that's fit for your lifestyle, and you have goals, and you're working on it? And to me, you know, in my wife and I created this word, you know, sitting around and trying to think of how can we create this word that really encourages people to just be thinking about moving towards wealth, which could be your health, right? You want to be taking care of your health, you want to be taking care of your mind, you know, because wealth to us is an abundance of resources. And that can be you know, energy, it can be, you know, a network, right networking with people is moving towards wealth. So, that was really the spirit behind it. Grant That's awesome. I love that now if someone wanted to learn more about you and your organization, what you're providing where do they go? How do they find this? Christopher Well, they can go to Wealthward.com. That is my core website where they can go learn more about what we do and get time on my calendar. They want to understand what we're doing in the mobile home park space, that is ThriveCommunity.fund. There they can go see a free webinar of you know, really how we're, we're, we're really doing something very really interesting in that space and have this blue ocean concept. And then tech careers and money talk. That is my podcast I'm recording episodes gonna be launching in September. But that's tech careers and money talk where, you know, like yourself, I'm facilitating this conversation in the corner of tech careers, you know, and really, there'll be a Tuesday episode. That's all about how you build and grow your career. How do you go from individual contributor to manager, there's going to be a lot of strategy, there's going to be a lot of interviews and use cases, then Thursday's money day, right? We want to bring in stock options, lawyers, what do you need to be looking for when you're negotiating for equity? How do you generate passive income? How do you buy houses in high cost of living areas? Right? We want to really speak into all the problems and challenges that people have and in hear from people who've been successful. Grant What a fun journey. I look forward to listening to what you're doing on your podcast. That's really awesome. Christopher Well, yeah, and I mean, it sounds like, it sounds like you're gonna be a guest too, because little did I know about your secret tech background. Like I think we got some, some stuff to talk about. Grant Oh, yeah. Like I said, at the beginning, three in Silicon Valley sold them all IBM acquired one of them. So I spent some time as an executive, they're definitely bounced out of that world. Christopher Yeah, I'm ready. Yeah, I'll be on the other side of the mic. I'll be asking the questions. There you go. Grant That's right. Good stuff. Christopher, this has been a pleasure. Any last comments you'd like to share with our listeners? Christopher Well, I think, you know, what I always like to share with everybody is just the fact that, you know, if you ensure that you are taking time, energy capacity to focus on this building your wealth, I think there's a true opportunity to, you know, live a life that is sustainable, and you can actually design it yourself. So that's my encouragement. Grant Why don't they teach this in school? Christopher Well, they may soon enough, I'm I'm working on that curriculum to say. Grant I think you're the one to put that curriculum together.  So that's right. Yes. Hey, thanks for joining everybody. Thanks for listening to this episode of Financial Investing radio and until next time, reach out to Christopher Nelson. Thank you for joining Grant on Financial Investing Radio. Don't forget to subscribe and leave feedback.  

In this episode, I have the opportunity to sit down with someone that has digested and synthesize the tax code and brings the tax saving secrets to you. Grant  Hey, everybody, welcome to another episode of Financial investing radio. So today I have with me someone that just barely met. But as I review, his biography, his profile what he does, it is in one of those places, which I admittedly know so little about, I lean on so many people for help in this area. Now I get to meet with and speak with an expert in the area of how to take it to the tax man. All right, let me welcome Mark Meyers here today. Welcome, Mark. Mark Hey, thank you so much, Grant, I'm excited to chat with you about this. And, you know, hey, if you can keep more of that hard earned profit. It definitely helps in the wealth accumulation realm for sure. So this exciting topic. Grant Boy, for sure. You know, when when you think about taxes and talking about taxes, you know, it's probably right up there with flossing your teeth, right? It's like, oh, everyone should be doing it. Right. But oh, my gosh, do I really want to talk about taxes. Turns out, as I was reviewing some things that you have done to help people, individuals, businesses, really reduce their tax burden. And putting that money, like you said, or leaving that money back in your pocket, suddenly, it becomes really an interesting topic to address. But before you give away any secrets, let's back up. How is it that you got interested in taxes? What is it that even got you to this point?  Mark Grant, you know, it's an interesting story, because I started out my career at the University of Florida, with as an undergrad in exercise physiology, get my Master's in sports management, moved to New York City to manage health clubs, and then moved to Los Angeles to edit, manage more health clubs. And in the process of doing that, I helped a really large high end brand, open a number of different locations. And in that they were they went from a 10 clubs to over 100 clubs. And in that process, I really learn to be an owner operator, every club that I would open or go chant, you know, help return around, I'd have to really be mindful of driving revenue, minimizing expenses, putting the operations in in place, you know, the best practices in place to get the best output. And of course, I was compensated on EBIT margin, so I'd get a base, and then I'd have cash bonus based off of how profitable is the company process, I realized, you know, hey, I might be running health clubs. And I might have a background here, but I have a knack for running companies. And I know there's a lot more opportunity in the financial markets in the financial world, particular to consulting with business owners, that's when I said the light came on after, you know, working well over a decade, you know, 365 days a year, and these clubs are open from, you know, five in the morning till 12 At night, you know, they never got hit. So I'm like, Okay, I'm going to shift gears here and do something fun that can ultimately help other people, and also helped me kind of increase my income opportunity here. Get out of this glass ceiling environment. Grant Yeah. So So you are living this life of just constantly being on right, the lights on, right, because your clubs are on right, the gyms are open, and so you're trying to optimize as much as possible. Talk about school, hard knocks, right? I mean, you learn the lessons along the way, for sure, right? Oh, for sure. Mark I mean, it's one of those things where you know, every penny counts, particularly in that industry. And of course, I worked in a higher end layer. So it was, you know, we're looking at 200 plus dollar a month memberships with spa packages and training and Pilates and Yoga. But at the same time, you still have to be mindful of your margins. That's really, really important. So it was it was a nice experience. It was a nice way to understand how to really learn the p&l, learn the people, learn the drivers, and then of course transitioned over to say, hey, I can speak to business owners, I could speak to those that are looking to, you know, increase efficiency. And there's a lot of opportunity there. So that's kind of where this all kicked off. Grant So in the course of doing this, you start uncovering, I'm assuming, oh, here's a little secret about how I could save a little more money or take some out of the taxes. I imagine over a period of time you started to build up this cadre, or list of or selection of wait, here's some best practices of actually taking the tax back from the tax man and leaving it with you. There's it was it was it that it was a 10 year journey that you invested in to build up that knowledge base? It sounds like, Right? Mark Absolutely. And you know, you really said something important, and it was very accurate in that my, my getting to where I am now didn't happen overnight. In particularly shifting gears, I'd say the last seven or eight years is when I really really shifted gears, to not just talk about can I just not consult with with individuals on their businesses and help them with maybe some financial planning, really shifting gears and saying, hey, there are a lot of different opportunities to reduce tax. And I just went out there. And just like in the past, in my first few years of this, I was kind of more of an advanced insurance specialist and consultant to business owners, and I could go out there and work with any insurance carrier. And I could basically look and say, you know, this is the carrier need for this solution. And this is why this is I realized I could do the same thing in the tax realm, there's just not 100 different, you know, tax savings providers out there, there's probably about 20 to 30 that you want to do business with. And these are small groups are generally fairly boutique, they're not huge. And they offer something very, very specific. And it's somewhere in the 70,000 pages of tax code. And they just so happened to analyze it, apply it. And basically, that's their gig key. So I have a lot of different tools in the tool chest, I have a lot of different relationships with groups that do these things. And I break when I do consulting work, I just put all the pieces of the puzzle together. And it's really cool. I'm not a CPA, I'm not a tax attorney, I'm really literally I call myself a tax savings architect, I've just developed this ability to consult, oh, that's a great title.  Grant Wait, say that title again, you're a what? Mark A tax savings architect, Oh, I love it together, I just build I build the plan. And then I bring the vendors in, right, the right team on the on the coach, I'm bringing the right team in to put the right plays in at the right times. And then the implementation goes, you know, off, you know, from there. Grant So, what I want to highlight is you have developed this by doing it again and again, maybe making a mistake fixing it again and again, oh, learning more again, and you put in that 10 year effort to gather and build that experience and that that's the value platform that you bring to the people so they're not off doing 10 years of learning the lessons, right? Mark Oh, for sure. Grant, I'll tell you, these are types of things that, you know, people don't have time, even the best CPAs right. And you can think about any CPA out there, you've got out of every 10 You're gonna have to my experience, you're gonna have two out of 10 that have developed their practice in a way that they'll have forward looking have a forward looking approach. And they'll have more bandwidth than just, you know, recording and tracking and filing right most of them record track and file. Do you have any more expenses? Are you sure you don't have any more you know, you can buy this capital, we get section 179 it so why don't you spend $1 to save 35 cents? No, that's, that's really a good idea if you need what you're buying. But two out of 10 are forward looking right? They're actually stepping outside of the box and seeing what's out there. They're 70,000 pages of tax code. So this is where the key is at 10 years, you're talking about their 70,000 pages of tax code you if you're going to win a championship. If you're a team think about a just a collegiate team or a professional team. They have multiple coaches. They have strength coaches, they have quarterback coaches that are talking about football they have offensive line coaches defensive line coaches look at businesses the reason why Amazon and Microsoft and General Electric and Nike and DuPont the reason why they pay significantly lower taxes than the average individual is because they have teams they have accounting teams, more than one CPA, they have attorneys they have business strategists. So this is what I really do is I bring that team approach to the small to medium size business owner or you don't even have to be a small to medium sized business owner to have tax savings you can be a high income earning executive. Oh really how to Matt navigate the tax code. There's things that you can do to reduce your tax. Grant Okay, so that gets the the question I was gonna ask around who is this for? Definitely the business owner. But if you're, if you're in the High Net Worth areas and individual this is applicable to you as well. Mark Absolutely. There's three there's three He kind of avenues, business owners. And the reason why is business owners are great to work with because they have control over their income, they can determine how much salary they take, they can determine how they take their income. It really they have the control. The other side would be high income earning executives that maybe don't have as much control but they're they're looking at, you know, half a million dollars or more of taxable income per year. I can work with business owners with much less I mean, they can have as low as 250 or so in taxable income, okay, because the more there's more flexibility. And then the Third Avenue is people that are selling highly appreciated assets, once a lot of crypto traders or somebody that had a stock portfolio, but they didn't have this huge blend, it wasn't like a qualified account, it was just a brokerage account, they had positions that just blew up, and now they're sitting on, you know, $5 million with Apple stock. Well, if they pull the trigger on it, they're looking at, you know, if they're California 37%. Grant Yeah, goodbye to that.  Mark Yeah, well, I can help them, you know, really take care of that as well either eliminated or different depending on what solutions we're looking at. Grant So, okay, what about what about on the, on the inheritance side, same same sort of story. In other words, let's say you inherited something is you have techniques that helps with that group as well. Mark Well, on the inheritance side, I don't spend a lot of time there, because generally, that should be done prior to and a lot of the work that I do actually blends in with maybe some estate planning attorneys, because you really want to solve that equation prior to the need. If you don't, then unfortunately, if you're above the exemption rate, the estate tax man will come take, you know, 60% of that from your kids, right? Not from you, but from your kids and your loved ones. And they might even have to be, they might have to sell appreciate it like this is a real estate high net worth real estate holder, they're selling off real estate just to pay the estate tax, which is never a good thing. So we when it comes to the gifting and the you know passing on to heirs, we generally integrate that into our planning, but we're doing it prior to so that way the kids and their loved ones can breathe, you know, sleep well at night and not have to worry too much about worrying too much about about that sort of thing.  Grant So so let's take the scenario of you're a small and medium business owner and you've got the you've got this tax burden on you. Do you need to be doing the work ahead of time? Is this a whole year of effort that the business owner goes through? Whether they have to be intentional throughout the whole year? Or do your practices, techniques allow you to just sort of come in at the last minute and who 30% off? Thanks, Mark. Have a great year? Mark Yes, well, I would say a little bit of all apply. But proactive forward looking is always the best. The best approach is a forward looking approach. Now I can do hindsight foresight, and then give you insight. On the hindsight we're always looking at, well, what did you miss that you can go back and pick up a lot of people don't realize, particularly in business, there's tax credits that they can pick up in previous years. Currently, right now for the next few months. There's the employee retention tax credit. So there's there's r&d credits, there's there's trends for investing in renewable energy, they can go backwards and pick up previous taxes paid, it's always look at the hindsight, what can we pick up that you paid in the last year or two or three? Ford, Ford sight? Or, you know, foresight is okay, what can we do to change the trajectory of your current income? Because right now, the way we're always looking at pathways, how are you taking your income, because what we need to do is look at creating new pathways. And if you have different pathways to receive the same income, there might be a different taxable situation. But those pathways aren't going to save you. You can't save any money until the pathway is created. So the foresight we're always looking at, well, this is how much money you're taking in now. And you've taken it in one pathway, or maybe two pathways, and this is your taxable outcome. But what if we created two additional pathways? Now you have four pathways, and we're not talking about deferring it. And putting into qualified accounts. I'm not talking about any of that, obviously, that's been going on for years and years and years is there's arguments as to whether or not that really is saving them anything. At the end of the day, we're talking about really taking tax law and the tax code by the horns and saying, hey, the tax code says I can do this, therefore, this money is not taxable. Right. So now that you have this money in your hand, that's not taxable you and you've lowered your adjusted gross income because you took a portion of your money that the tax code says is not taxable because of the way that you've structured it structure that we've just just decreased your taxes and you're like liquid, like the money that you save is liquid, it's in your pocket. If you want to use that for investing you can if you want to use that to recapitalize your business you can if you want to use that to go to Vegas, you can. Grant Yeah, your choice and that's the whole point. It's your choice rather than Uncle Sam. So the strategy seems to be tell me if I've got this right. It's don't fire your CPA because you're going to keep The CPA as a business owner, because you're not the CPA, but what you are bringing is a way to be more productive as you work with the CPA, right, you're bringing in additional knowledge or insight that will then be brought into those conversations with the CPA currently have is that close 100%. Mark And what I always like to tell business owners, and right off the bat is, you know, let's, let's not put your CPA on defense mode, right? They, there's no CPA out there that has the bandwidth, to have this time to vet and research all these different ways to save taxes. I mean, they know a lot, but because they may not know everything that I'll bring to the table, that's okay. So the first thing is make sure that they're not on the defense. And also, I'm not looking to take over any bookkeeping, your tax filing or tax prep, I'm just looking to add additional layers that usually increase revenue for the accounting because it usually does increase their need to maybe have an additional filing each year, or maybe some additional bookkeeping to take to oversee these new solutions.  Grant So it's all day for you as a partner in it, your they don't see you as Oh, I'm here taking business from 100%. Mark Most of the time, when I'm speaking to the right CPAs. And I'm introduced to them by the taxpayer, I get introduced to three or four new clients that the CPA has, because they're like, this is great, I have you know, four or five, or depending on the CPA, they might work with a higher net worth individuals, they might have more, but I like those relationships, because they open the door for more opportunity. Now, I will say there's going to be out of that regard every 10 CPAs, there's two that are for thinking. And there's eight that are really just doing the numbers really doing the prep the recording and filing and prepping. Sometimes the eight, there could be some resistance there. And it's it's, it's only because they don't know what they don't know. So in those situations, hey, I always say it's up to you. It's up to the taxpayer, that you're the decision maker, the CPA is not the decision maker. And I have you know, I never want to pry someone away from their CPA, but if they really liked what I'm talking about under CPAs, just very resistant, doing anything that they're outside of their norm was to have dozens and dozens of CPAs all over the nation that would be very happy to interview you. Or you could interview them because they know the solutions and their clients use them. Grant Yeah, I was gonna ask you. So how does you know? What's some guidance for our listeners on? How do you pick? How do you find those two out of the 10? CPAs? Right? What are some of the key things that someone's looking for? to vet your CPA or while you're searching for someone to say, now you're you're part of the 80%? It's you you're actually not going to help me as much I'm looking for the 20% What What are some tips you have? Mark Yeah, first thing Grant is ask them if they do quarterly meetings. If the CPA or the tax preparer for you is not meeting with you quarterly, they are not forward looking, they're likely going to say hey, let's meet in might not even be before the end of the year, it might be in like January or February to say tell me what you have, is that all the expenses you have? Are you sure because we need to file. But if they even if they're only meeting once per year, at the end of the year, and just trying to figure out, okay, we need to shove a little bit more in the qualified account. And maybe you can have any more, you know, maybe you can buy some more capital equipment. That is not, you know, I'm not saying that's well, let me just say this, that is not the accounting, that that someone needs as they're climbing, the echelon of income, if they're still using that CPA, when they're climbing that income bracket, they are going to be paying retail taxes. When everybody else that has reached that level. When they get to a certain level of of success. They figured out how to pay wholesale sales, no different. There's no different there's a retail price, and there's a wholesale price and the people that pay the wholesale price go above and beyond and look for the coupons where the coupons they're in the tax code. Awesome. The IRS is not saying I'm giving out all day. Yeah, they're not putting a flyer out your mail.  Grant Wait a minute, are you saying there's no mobile app that they could the tax code mobile app with coupons? I think we should build that. That's a great I should yeah, that's my app.  Mark Together Grant. Grant  Let's go build that. I bet there's a market for that. Mark Okay. I would bet there is as well. Yeah, absolutely. Grant I love that idea. Okay, so there's something I saw on your profile that intrigued me many things intrigued me and one of those. I just gotta read this off here. Because when I read it, I was like, Where do I sign? It? It was it was learn how to get the IRS to fund a portion of your retirement 100% tax free. So like, right next to the mobile app. We just talked about developing there's this also. Okay, tell me all right. We want the IRS to fund a portion of our retirement 100% tax free any secrets you can share on that? Mark You know, there's a I'll, I won't give everything away, right? I want people to want to ask me some questions. But I will say this, there's a few different ways to do it. One of the ways is when you find these tax savings, right, when there's that when you're applying the code that's going to basically give you a deduction, right? Because there's codes I can actually, you know, there's a little golf tournament. Here's a nice secret out there. A lot of people know about this, but let's a lot of people don't as well, golf tournament out there. It's been going on for years and years and years. And the winner gets a green jacket. I'm not sure if you're a golfing fan. Oh, sure. Or Yep. If you know, who gets the green jacket every year, you know, there there is somewhere in Georgia and a little town. That's right. And it's a very prestigious golf tournament. So about 60 years ago, the higher net worth individuals that lived around that, that golf course realize that they could rent their homes to the corporation's coming in that were or anyone coming in that wanted to, you know, watch the tournament, and they could rent their homes for significantly more than they would you know, what would cost them to go have a little nice vacation? So in that process, they said, Well, these are our personal residence. This is not a this is not an income property. So they lobby to the their friends and Senate and said, Hey, we're not renting our this is not an income producing property. But we're not getting any deductions on this. We're not getting any tax benefits. But so can we have some benefits. So they basically the Congress, and there's two tax codes that validate this, you can rent your personal residence for up to a certain number of days per year. And the dollars that you receive for that rent is tax free, as long as you don't rent your home for over a certain number of days. Now, here's where it gets fun grant, because some people don't want strangers in their house. Right? So even if they could rent their house for significantly more than they could go do something else. They don't want to Airbnb their house, what if they're a business owner? Well, can they rent their business? Can they rent their house to their business for business purposes? Using the same tax code? Absolutely. Now, we just have to determine what the value is, and put it in your bot and your minutes and ultimately integrate it and know, now when you create those deductions, right? Because if you're renting your house, from your business, and you create a deduction at the business level, but didn't cost you anything at the personal level, or the business level, maybe there's a, you know, maybe the meeting you did was whatever, you know, lunch, well, you just created deductions with no cost, no cost. Now that tax savings now the tax savings is getting to you from that's where you're funding your retirement tax free from the IRS because those that those dollars are non taxable. Now, if you get them into a Roth, if you get them into a cash value life insurance policy, you're never paying the taxes again. So now you're looking at never even paying taxes again after you didn't pay the taxes on it to begin with. Okay, that was one little nugget. Grant That's you know, would you just drop the mic on that? You're not holding in my car? Yeah, yeah. Mark No, but if I dropped it, it might fall off my desk. Grant I might, I might fall off that. Yeah, I tell you, Mark, that that was awesome. Just following that flow of what you articulated. I think that's a beautiful thing. Okay. So let me ask you this. With all that you're doing, and with all that people are coming to you for? How do they engage with you? How do they dial it? How they interact with you? How do they how do they say I'm a good candidate for you? Where do they go to find out more about you? Mark Great question, I always just invite him to my website, Grant, I just PeakProfitSolutions.com. And as you know, peak as in a mountain peak, P-E-A-K Profit Solutions, plural PeakProfitSolutions.com. On that you can have, there's places where you can go get case studies, I click here for some case studies, there's a link that says, hey, book an appointment with me, and 20 It literally only takes me 20 minutes to have a conversation with someone to determine if they're a good candidate for any one of the dozen or more solutions that I can bring to the table. That's really the best thing though, the most important thing to know is, you know, just take a little bit of time, even if it's 20 minute phone call, you don't have to come super prepared. They don't have to come with their entire balance and their their previous tax years and their p&l Like just come and say, This is what I how I'm structured. This is how much I'm making every year. I'm writing a lot, you know, just all they need to do is do that. And I and from that point forward, I can determine right out of the gate if they're a good candidate for the architecture to start. Grnant The tax architect. Mark Architect, tax savings, architecture, tax savings architect good building. That's right. Grant That is awesome. That is awesome. Excellent. Mark, thank you for your time today. Any final comments? Mark No, I just appreciate being on the show. Appreciate your, your hosting style, and of course, all the interviews that you've done on your channel so are fun to listen to. So thank you. Grant I'm having fun. It's a fascinating world, right? There's just so many great people doing so many cool things. So when your profile came across me as like, Oh, I gotta talk this guy. He's got some secrets about reducing taxes. Okay, yeah, he's in. So thanks for doing that mark for thanks for taking the time everyone. Thanks for joining another episode of Financial investing radio. And until next time, go check out go check out Peak Profits Solutions. Thank you for joining Grant on Financial Investing Radio. Don't forget to subscribe and leave feedback.  

In this episode, I have the opportunity to speak with someone who demonstrates true grit to make a whole life. Grant Everybody welcome to another episode of Financial investing radios. So today I have with me, someone who is more than what you might think she is an expert on whole life. But do you really know what whole life is? She's going to help us to understand what that means. I'd like to welcome Svetlana. Svetlana Thank you so much. It's such a great joy to be here with you and sharing this space. Thank you for having me. Grant Oh, it's so good to have you here. So I had this opportunity to interview your wonderful husband twice. He's so gracious with his time. He's really skilled in the financial markets. I've even gone through a whole bunch of his courses and applied those. And so it's been I'm a Jeremy Newsome fan. Yeah, yeah. Yeah. And and while I was talking with him, he said, Hey, have you thought about interviewing my wife? And I'm like, Well, what is she talking about? And says, well, she talks about whole life. And I'm thinking well, yeah, whole life such a critical part of life, no doubt. But let's back up set. Svetlana, would you tell us a bit about your origin story where it all started? And how you got into this business? Svetlana Absolutely. Yes. So I'm originally from Kazakhstan. And I moved to America at 19, turning 20 with big American dream as the $100 in my pocket, and I had an opportunity to come and create opportunities and live here. And it wasn't as easy as I thought it was, right. But it is a beauty of a young person that, you know, be 19 and 20. And so courageous, and so naive in so many ways, making those leaves a lot easier than when we become a little bit older. And we have more things that we start considering. Yeah. And so I was blessed that that time, was that mindset, like, what can happen? I'm just going to go and have all of these dreams come true. Grant I'm just gonna go to America, everything's gonna be fine. Right? Svetlana Exactly, exactly. That was like people are so nice there. And so how cool is the wonderful, and it's really a beautiful way to see the world. And I kind of when I was telling my mother, I will come to America. And in five years, I will have all of my dreams realized, I will have, I will get married and I will have a baby and I will have a beautiful home and nice cars and travels. I will have my papers like all of it. I just kind of described for the life that I created. And I knew like just give me five years, it will take me a lifetime in context and to create those things. Wow. But it'll take me only five years in America. And that is the beauty of an opportunity that exists in this country that most people don't recognize. Because when you're born here, you can't think this is how it is. Grant Yeah, you take it, you take it for granted. I mean, I've traveled the planet for 35 years doing business. You do recognize that when you're in other countries, but it is easy to forget it or not be aware of it. Svetlana Absolutely, absolutely. And so when I came here I was like in a Candyland. Like oh my god I can do all these things like it's so amazing. And to start a business you just go on mine create LLC pays $150 get a business license, build a website and you're ready to go. Right like it's a marvelous back home who do you know who do you pay it on the table? Is it my favorite though because the government like I was just like was so impossible, and it couldn't like believe the ease and I recognize truly that there is only one obstacle, truly one obstacle in us. And it is us in our mind, our mindset. Grant The mindset is the obstacle. Svetlana That's a like there is no actually real worldly obstacles that you have to seriously overcome. And yes, you might have a harder life and different things. And not everyone have the same cards dealt. However, you can I was going to library, right, using the computer in the library, did you write emails and just start doing some things. And I was walking for two years didn't have a car. But that still felt like I'm gonna Candyland. And it was amazing because the hardship that I experienced before in my life. Grant So compared to the hardships that you came from, you looked at this hardship of oh, I have to walk somewhere. Oh, I don't have a computer and in your mind, those weren't showstoppers. Those are just things you're gonna go do because you're pursuing that dream, that vision? Svetlana Yes. And the computer is in the library. And it's on the 20 minute walk. Yeah, I mean, my did it was Las Vegas. 120 degrees in the summer. Still possible was a bottle of water. Grant A bottle of water and maybe a hat or an umbrella. Right. Svetlana Exactly, exactly. And so you know, life started. First couple years were very difficult. And then life started to add folding. And indeed, I got married to this incredible man. And we bought our first home. And we have two BMW in the garage. And we I have a great job. And he has an amazing job. We are here pregnant and having a baby boy. And I'm getting my green card in the mail. And I was looking at everything. And it's like four years and eight months. Mike, oh my god, like everything that I told my mom totally can show every single dream. Wow. Yeah. Oh, my how incredible that is. And I was just in the jail and then thinking, Okay, what is next? What is next in my life. And then, two months later, the biggest tragedy of my life happened. Oh, no. And it was the darkest time of the darkest time of my soul that I ever experienced. And I wish no one ever experienced that. But my husband died very unexpectedly, within a week, within a week. Wow. And we had all this plans for the future. And we had tickets bar to go and travel and visit my family and the plants for like, we will come back and get pregnant again. And all of it ended. All of it ended. And I once heard that the hardest thing about heartbreak, and then the hardest thing about deaths is having a funeral to all of your dreams. Right? Because what you saw your life would be is no more. And then it's a time of void. Yeah. And a very deep healing is that in sir in search of what's next. And so during that time, I was very blessed. He was in a new position for the six for six months, and I got life insurance proceeds. Really, and I didn't have to go to work right away. So that money that came into my life allowed me just to lay there and cry, and grieve, and pour into myself, allowed me to get help allow me to do all that I needed and my soul needed and to really rest in grief. And I took nine months off to be with my baby because it was so unfair if he would lose his father to death and then his mother at the same time because she had to go straight to work. shut down everything it just started providing for the home that was created for two incomes, right? Grant Yeah, very difficult. There's some level of an emotional suffering, maybe even an emotion No doubt that you went through as well, I would imagine. Svetlana Absolutely, absolutely. And that was also an awakening into, wow, I need insurance. Because what if I go? What would happen to my baby? Boy? Do I have actually any paperwork? Because I didn't, I had no way or no trust, we didn't have any of that stuff. And I remember when we bought our first home, we received a letter in the mail saying, Would you like to add insurance for 9099? That in case one of you die, the mortgage will be paid off? And my husband was like, ah, should we do it? And I'm like, why would we spend $20 a month? Grant Oh, yeah, at the moment, just now why would we write? Svetlana Yeah, because we never think it will be our story. It's never our story until it's our story, right? It's never our life until it is our life. And that's the unfortunate part about it. And an insurance world we say the money pays for the, for the insurance, your house is what buys it. Like so many people reach out to me when they in a health crisis. And I Hey, can I have a life insurance policy? And the answer is no. Most of the time it is now because now you're not insurable. It is that time where you are well, and everything is going good for you. When you need to just put a month into it, get everything put together will trust Power of Attorney insurance and be done. And hopefully you will never ever ever in your life again, need to look at it. Next 15 years. Grant I mean, the best time to buy insurance was 10 years ago. But if you didn't do it, well then the best times today, right? Svetlana Absolutely. Absolutely. Yes. And so really, that was my coal into insurance runs? Well, because I had to learn about it for myself. And I remember I called my you know, car insurance plays whoever was insured at the time. And they asked me, well, What type do you need? On my there are different types. Like I had no clue. Like, well, I didn't know like, and there was no one really to educate me. And I recognized Well, I'm not the only young woman that is a seeing single widow, with a baby that has all of this questions and insurance world world is huge. You open the door, and there are millions things. Grant So how did you educate yourself on this? What do you do? Svetlana So I got a lot of information here, a little information there. And then insurance agent came into my life. And she told me about certain policy, and I bought it. And then I learned that that policy isn't actually good, that I got it as a policy that I learned that that one wasn't good. And then I was like, Okay, I just need to like figure this thing out. Right. And I was very fortunate that an incredible Insurance Agency, Candace grabbed me, and my G just saw this potential enemy. And so the hunger and the knowledge, so really the heart that was behind it to truly help people. And he sat down with me, and he taught me everything I needed to know and more. And what I find and again, I don't know everything, right? It can be a lifetime on studies. Things are shifting and changing all the time. But I know a lot and a few things that I don't I have incredible people to go to you. Right and and work through those parts, right? And it is usually a bigger project such as estate planning class $30 million. Well, we do what do we do there? Right. So I'll make the phone call my hey, I need you how can we do this together? But pretty much everything else I've done by now and I'm very comfortable with. Grant So what occurs to me, Svetlana, learning about your origin story and how you get going in this is that the common thread and all of this appears to be your grit, how to use the term grit, right meaning you've got this, this desire and this burning desire, right to go pursue something. First, you got to come to America, whatever it takes. And then when you get here, you start ground up and that's just pure grit to make it happen and then then you're thrown this option Typical of your husband passing away. And yes, you needed to heal and do some recovery. But then you got back up on your feet, and you had grit. And you said, I'm gonna go figure this out. And you did both learning, but also trial and error, right? So it was a bit of learning by faith, right? And faith, meaning you would take the next step, you would exercise your faith, try something learn. But each time if there was a setback, you didn't just pull back and say, I'm done. Rather, you would pick yourself back up and keep going again, that's what comes across me is one of your main qualities. Svetlana Yes, it's a beautiful observation. And thank you for saying that. And I would, I would pull back for a little bit and I will, at least in a pass, and that would feel sad for myself and bad for myself. And I would now I'll give myself a day or two, I'm like, Okay, let's be depressed for a day. Yeah. Yeah. Okay. Now it's time to see see beyond, right sees through says it shows. And it is, it has been a lot of personal experience. Absolutely. And then having that greed to rise up again and again. And I think is truly my love for life. I love life and I'm so I find it to be so miraculous, and so magical and so beautiful. Just to have this physical body to experience this life on Earth to experience the depths of love and, and hog and Tai Chi and, you know, be able to eat a delicious cookie. Grant Oh, there you go. Yeah, cookies. I like that a lot. So that leads me to another part of whole life. So when I was talking with your husband, Jeremy, and he said, Hey, do you want to talk with Svetlana? Oh, yeah, sure. What does she do? She does whole life. And I'm thinking whole life insurance, which is, of course, what you do. And then I went to the site, that was I saw off of your email, thelightfreedom.com. And I looked at that, and I'm looking around your site and like, I wait, where's whole life insurance? And then I realized, Oh, this is whole life. Can you talk about that? Because I think this is fascinating. It's two key ways to view Svetlana, she's an expert in whole life insurance, but also in whole life, you talk about that? Svetlana Absolutely. I am a certified life and relationship coach and I work very deeply with the divine energy of, of light, to create healing, healing in mental and emotional, spiritual, on a physical body. And that is what my calling and my life purpose is my life purpose is to free your life people's light, so they can shine bright and impact many truly create a beautiful world. And, and it is as it is whole life, right? It is about mine. It is about heart and energy, and emotions, about your connection with your highest self, your spirit, your faith, God. And it is about the whole wholeness. Because when we only address parts of ourselves in a western society, we're so attached to our minds, right? It's about how smart I am and how much I now. But unfortunately, no, in my view, doesn't create a space of fulfillment, or joy, or just magnificence and life. And my desire for people to experience wholeness, whatever they truly means to them. So they can live out their life purpose, they can share, they live in a world, whether it is through having a financial podcast, right and teach people how to become prosperous. And as my husband says, money don't sell don't solve all the problems, but money solves all the money problems. Right? Grant Yeah, that's right. And money solves the money problems. But not all the problems. I agree. Svetlana Exactly. But it's so mindset can be really be solved. Whereas was finances was those resources when we have them and money is an amplifier. Out of the love that is inside of us. And when we are love, we become so much more love with money and we create healing in a world we go out and build schools, we go out and do beautiful missions, and we just do great things. And the problem becomes when you are dark inside, and then you have all our money, and then you're like, Okay, you know, it becomes more of the darkness, because that's what the inside and managers multiplies what is inside of you. Grant In fact, I remember Jeremy saying something like money just becomes the amplifier, right? of whatever is inside of you. Money just amplifies it. Svetlana Exactly, exactly. And so, Jeremy works on that amplify it on amplifying things, and I work on that to make sure that here is light. Yeah, let's make sure that here is harmony and peace and love. Let's make sure that you have forgiven yourself and those who betrayed you or hurt you. Let's make sure that you learn how to love yourself. Because the reality is I remember having this beautiful 20 year old that I got to work with. And I asked her, please tell me what's good about you? And she said, Oh, Svetlana, I can't. I feel like I am just so selfish and just bragging. But Mike, okay, tell me what's wrong about you? And she gave me the whole list, the whole list of things. And I recognize the madness of this world that we actually been kind of having this applied for? Oh, yes, you are. So you're recognizing how bad you are, and all of these horrible things about you. But when you come in and say, I'm good. I love mine. I am a wonderful, incredible human being. All of a sudden, no one wants to hear it. And everyone is like, oh, yeah, that person is so selfish. And so we go wrong with this mentality that we actually, when we beat ourselves up and talk horribly horrific ly to ourselves, we are fully Grant Yeah, we get held up as oh, gosh, this is good. Sometimes it's viewed as though you're being vulnerable. Right? You're you're you have the courage to expose your weakness, right? Yes. Svetlana And it is very different when you expose your weakness from all the a healed place when you are sharing it, and yes, it is my weakness, and I acknowledge it. But there is nothing wrong with me, I still adore love myself, even though I'm not perfect. And I have this weakness inside of me. Right, and I'm working through it, I'm working on it. And maybe it's a part of my personality. And I want to really embrace that. Also, whether I'm going to heal it and overcome it, or whether I'm going to just embrace it and make it just a part of me instead of fighting with that part of myself, because I'm just exhausted of the sport. Grant When you see people go through this right when they start to embrace whole life and the way we're talking about it here, what's the impact to their lives? What's the outcome of that? Do they end up making better decisions? Are they more kind and loving? I mean, what what's the net effect of of coming to understand that are embracing that? Svetlana They get to start living the life of their dreams. What I help them is to heal so that they can come into the homeless, and then to really look at us, okay, what is placed into my heart by divine for me to live through an experience in this lifetime. And those are your dreams. Our deepest heart desires. Our dreams are not ours. They're not. They are placed by God in our hearts, because this is the way God wants to experience itself in a world. And it is for us to truly be for us to truly experience and to embrace. And the people who work with me, that is really the result they get to live their dream life. Whether they have dreams is to have an amazing partner to have a family to have a business that is prosperous and brings a lot of cash flow and they are creating impact in a world where it is, you know, living at a harmony in a beautiful place because that brings more of the goodness because of who they are, and how they can create, in write, and do podcast and Ted Talks, whatever that is, right. And so one of my primary courses that I teach is limitless creators, the dream scores. My what I take people on this five week journey of creating a dream life, downloading the deepest heart desires. And a lot of times, it is a very first time, they actually true to themselves, they actually admit, this is what I saw truly want. I just been so scared to admit it. I've been so afraid that what if I say that, but it wouldn't come true, or it would come true. But I'm not ready for it yet. I'm not. I'm scared of it. Right. And I held them to really open up, open up and release all the blocks and fears and limiting beliefs and any kind of limitations, so they can live their dream life. Grant And you know, one of the comments you made earlier was that one of the biggest hurdles that you've noticed, coming to America was that the obstacle of mindset. And it sounds like that this approach to what you're doing with Whole Life helps to address that mindset obstacle. Svetlana Absolutely, absolutely. Yes. Because for as long as we have these blocks in our mind, we cannot live the true life, we cannot truly create what is meant for us to create in a world we cannot truly show up in our highest light and be unapologetically. And that mindset is a huge, huge part of it. Grant Huge. It's interesting that you come from this background where you've recognized where mindset is the major obstacle, whereas other places there's other obstacles long before mindset. But here, here in the US we've gotten mindset as a major obstacle. So let me ask you this if if someone wanted to learn more, both about whole life from Svetlana in terms of insurance, as well as whole life from set set, Svetlana, in terms of living your dream life, where do you send them? Svetlana So the best way to reach me is through my email it is the SvetlanaNewsome@gmail.com. The second best time is actually messaging me on WhatsApp and my phone Yes, my phone number is 702-557-8157 so WhatsApp me and I will send you probably a voice message. That's that's the easiest for me. I do have an incredible soul that is eight months old. So yes, I will drop the message was is in a voice voice recording. And my website is thelightfreedom.com and so is my Instagram is The Light Freedom and I'm very active in Instagram, you can always see me reach out to me there. And that is the best way to reach me and ask any questions that you have about the wholeness of the life the whole life? Or about the insurance surely if you have any insurance needs, or any questions about insurance and the insurance world period, I would be happy to guide you through it. Grant So I really appreciate you taking the time and your beautiful life of true grit to accomplishing your purpose and your life's dreams. Just from the beginning till now i i look forward to continuing to watch you and Jeremy make a difference in the world. You guys have already been making differences. Thank you for spending the time with us today. Any last comments you want to share? Svetlana Love yourself fully love yourself more. And remember that you are an incredible human being the way you are. You can only become more of the goodness because you're already good. Grant That's awesome. Thank you so much Svetlana appreciate you taking the time in your busy schedule, everybody. Thanks for listening to another episode of Financial investing radio. And until next time, get some whole life. Thank you for joining Grant on Financial Investing Radio.

In this episode, I have the opportunity to speak with Alex Hagerup who is solving the problem of using AI to take costs out of your business. Grant Hey, everybody, welcome to another episode of ClickAI Radio. So today I have someone that I have been admiring by looking at his background, here with me today to talk about some amazing aspects of his journey to solve business problems leveraging technology, specifically AI which is quite cool. Anyway, let me stop right there. And welcome Alex Hager group. Hello, Alex. Alex Hey, Grant. Thanks for having me. It's a pleasure. Grant Thank you. Thanks for being here today and for taking the time. I know you're getting ready to head off trans continental here pretty soon on a trip. So thanks for for jumping on this conversation here today. Alex Absolutely. It's exciting being able to travel again. So I going home to the Motherland for a few weeks is exciting. I haven't been there for more than a few days, for the last three years, actually. So I'm definitely excited. Grant Have have have the COVID situations and those numbers pretty good over there at this point. Alex Yeah, everything is fine. So Norway is completely open again. And it's all good. But But Norway was one of those countries that really looked down hard. And also since the US didn't allow non US residents to actually come back into the country. If you left. It was just a problematic situation to go to Europe in general. Wow. Grant Yeah. What a great opportunity to get home to family. Well, thanks for taking your time here with me today on this. So Vic AI. All right, who is Vic AI? What happened here? How to what are you? What are you solving? What what problem? Are you looking to address with Vic? Ai? Alex Yeah, absolutely. So I'll take a little bit of the background to set that up. So I, I've always been very interested in both accounting, finance and technology. So this is a company that lives in the intersection of those. My mom had her own accounting firm. So I grew up there, which probably influenced my interest for for accounting in general. And I built a couple of companies. But one that I spent three years with just prior to starting, Aki was a cloud ERP system, an accounting and accounting platform that was being used by about 30 40,000 companies back then it's about 80 90,000. Now, and during working there, you know, observe the sort of, let's say, the challenges of accounting and the manual, repetitiveness, the tediousness, all of that from, you know, every day you felt it? Grant Where? I mean, what is the excitement in that? Right? I mean, where's the excitement? Yeah. Did you credit that properly?  Alex Oh, gosh, yeah, exactly. So so we were we were just observing this. And then this was back in 2014 15. Just before we started with AI started having a new, you know, like a new summer or a new renaissance in a way and, and we were thinking like this has to be we can maybe like we can solve this in a better way than how the technology has solved it so far. And after some deliberation, we sort of thought that we could create AI algorithms that would be able to actually do accounting transaction processing better than humans. Grant Because that's a really key point I think you're making. So the way that we've been solving this problem up to this point has been, let me take the tasks that we do and just automate the tasks themselves. Right. In other words, let me take your actual transactional activities that you're working through, and just put some increased processing to that. That's how it's historically been done, right? Alex Yep. Correct. And that's been entirely driven by rules, right? So, you know, transaction, Uber, transportation, as, you know, rules based automation, right? And there are all sorts of problems with that. It's obviously better than doing everything manually, right? So we aren't we're progressing through stages there. If you go back before the spreadsheets, you know, everything was done entirely manually. So we are progressing here, but, but what we're building is not next face that comes off there, you know, what everyone is using today, all over the world. And, you know, AI will solve this in a more scalable and gracious way and more more effectively. So that was all right. Yeah. Grant Yeah. So real quick on that, can you articulate how is that different? Right? Because all of us come from this rules based way of thinking. So what is it that AI is going to do better? How will it do it differently than what we're used to? Alex Yeah, I mean, that's a that's a great question. It's kind of the essence as well. So when you when you look at how it happens today, it's not only rules are not only automation, it's automation, and a lot of human hours involved. So you can always ask yourself, why are all those human hours involved? If it's automation, right? So so it begs the question, right, so what happens in reality is that sort of rules and templates and RPA isn't sufficient, because there's so many edge cases, and there's so much variability in the world of accounting. So you know, rules only takes you so far. And then you have to staff up and have human cognitive reasoning step in and do the rest of it. So where AI comes in is that it can do both of those things. So it does the automation without rules, and it can do the reasoning that humans are there to do today. So I always say that AI is, is great at sort of mimicking that reasoning that humans are doing. So one of the areas that we're in is invoice processing. And when I give an invoice to any human, you know, it will always tell me, oh, that's the vendor, you know, that's the invoice number, lots of total costs. But that's not obvious to a computer. And if you're gonna write rules for every variability in the world, you can just end up writing too many rules. So it's just not a, it's not a great technology for it. And AI is way better. So it's just like in the early stages, so that's next sort of digital transformation, transformation journey that we're all. So as Grant So as you know, when you're working in the AI space, and you're saying, Oh, I'm going to apply AI to a particular problem, you end up building different models with different AI characteristics based on the nature of the problem, some more aggression based some more sort of classification based, as your people as your customers look to use your platform, do they get exposed to any of that? Are they even aware of what elements or aspects of AI are at play? Or? Or do they just jump in and start solving the problems they're used to, and then the right sort of AI model behind the scene is executing on their behalf? Alex Yeah, we've hidden all of that from our customers. So we do try to keep sort of Explainable AI in the way where our user interface is explaining why our AI predicted something. But we've kept all of the complexity, so sort of models and model training. And all of that is in the background, we decided to do like an end to end service where the customer, they don't really need to do anything technical. It's, it's, you know, just another SAS subscription that they're using, that they plug into one of their processes. And then we deal with all of the complexity of the models, both global models and AI models, specifically the same for each customer. So we keep all of that complexity hidden. Grant That's awesome. That way, I'm not touching any of that as the end user. So if I see the name Vic AI, I don't need to shy away and say, Oh, wait, I need to be an AI expert. It's more that this is the enabling technology. And it just turns out that you've made that simple for the people without needing to know that Wait a minute. So that brings a question on my use, you know, one of the challenges around AI is the whole notion around bias. And, and, and with the cognizance that's required for humans up to this point, and still largely today to do you know, counting processes. Therefore, that has the opportunity for some bias that comes in right terms of the way things get handled. How do you deal with that then in terms of applying AI so that that bias doesn't creep through? Alex Yeah, it's a great question. And it's a challenge for everything where we're data sets are involved in training, AI. So one of the ways that so one of The one of the good things with accounting data, if you start with that is that it's ultimately numbers and classifications. And you, you kind of want to have that, right? Because otherwise your your books of your business is wrong. So unless you want them to be wrong, you know, you have a very good incentive to keep this right. SO into SO, you know, I think we generally see kind of less less bias in accounting data, and then some other more like subjective data in a way. And then also we draw on data across 1000s and 1000s of customers. So we have customers in both Europe and the US and many other many other regions as well, but the little fewer and most of the concentration in Europe and US. And in all sorts of industries and all sorts of sizes, we have about 13,000 customers on the platform now. So when you start looking at such a wide data set, you also hopefully reduce some of some of that bias. And then you also have auditing processes that sort of sits at the end of their accounting. And hopefully also they'll you know, annually they'll catch corrections, and also fed in as well to make AI. So those are some of the tactics. Basically, it's all about keeping clean data, so that our predictions are accurate. That's really what we're trying to get to. Grant Yeah, boy, that's that's so critical, especially as you pointed out with the with the need to of course, be accurate for the business for sure. All right, so So let's say that you've got this data, you've cleaned it up, you've done the preparation, you're on the Vic AI platform, the question now in my mind becomes, therefore, what changes in the lives of the people that adopt this right and others, they may change? I'm supposing something about the way they do their daily work, or the way the CFO does certain things, or maybe even impact on regulations and audits. I mean, what's the impact of the organization when when this gets adopted?  Alex Yeah, I'm also in question. And I want to point out also that the beginning of getting getting live with Vicki AI isn't complex for a customer, because we built an automated system to ingest and clean their historical data, which then goes into our system automatically and train our AI algorithms so that when we go live, we have, you know, pretty good knowledge of what's going to be predicted. We know your system, we know your accounting, we know how you do things. So when you start pushing new transactions through our classification, accuracy is high. And there's no complexity for the customer. In that process, it basically happens automatically in the background. Once you are live with Vic AI, some things will will change. But it doesn't sort of change. Like overnight. One of the things with AI is it gets better and better over time. And one of the things we're driving towards is what we call full autonomy. And full autonomy is you know, the AI's version of automation. But it means that it has perceived this not to need human review. And that's when it's fully autonomous. So that's our sort of end goal with the autonomy of transactions is that, you know, the AI system is perfectly confident in the work that it has been doing. So it doesn't even ask a human to review it. So this this increases over time. So when you start with Aki, you, you have a you know, you have a better interface, you have a smoother operation, you have already probably 50% time reduction in the first month of using the system. Grant Let me stop you on that right there. 50%. So, that would be 50% of those that are doing sort of the daily operational activities, or is that of the CFO? Or who who is that, that that percent impacted? Alex Yeah, that's on the accounting team that is doing the processing. So if you're doing you know, if you have five people or if you have 15 people doing invoice processing, whether they are onshore or offshore, you know, just in the beginning, you can drastically reduce that, and then that percentage just increases over time because you're substituting the AI for for for human FTE. So basically, and and what we see is that everyone wants to do something else than just sit and do like data entry and accounting classification, right? You you, you can be more proactive, you can do more value added work than that. And we're sort of at that phase now where the AI can substitute that's partly augments and also fully run things autonomously. So when you put this in place, in the beginning, there is a little bit of effect right away but you know, you got to read the sign some of your some of your processes and some of your routines because you have a platform here now that is doing Most of the work for you, and you're just reviewing and you're reviewing the AI and training the AI to become more autonomous. So you've got a little on my mind shifts, and some sort of routines, you don't need to double and triple check, check in have four people involved in reviewing, you know, one thing because the AI can tell you how confident it is. And if it is very confident, maybe you can have one person review it in the beginning, and then eventually it will be fully autonomous. If it's less confident, then it will also tell you, and you can review it in more detail. So it's pretty, it's pretty, pretty fascinating. Grant It is dang fascinating. And I'm assuming that there are some that have run into this, and they've worried about their jobs, right? They're like, wait a minute, you're taking my tasks away from me? Do you have to help them overcome that fear and say, hey, you know, you're gonna move towards more value added activities within the organization? Have you run into that problem? Alex So it will we see is that it's, everyone is just squeezed on time, right? Everyone's trying to hit the deadline of the month, the clothes and all of that. So it there is no shortage of work to be done in the accounting piece. And, and just having, you know, having faster turnaround time having more accurate data, because AI is more accurate than humans, like it doesn't fat fingers thinks the same way. And when it's uncertain, it asked for a human verification, a human looks at it, and then you get more accurate than then all the way. So we see that, you know, there's no shortage of things to do. Everyone wants to progress their careers. And I don't really perceive that as an actual problem. But it is like a, you know, people think about that as a problem, but I don't think it is in reality. Grant So this has been several years and coming. When did you start this 2 to 3 years ago? Alex Yeah, early 2017, was 5 years. Grant Okay, that's, that's awesome. And you built this ground up, meaning all of AI development AI technology that your organization's created, right? Alex Yeah, exactly. I mean, it's been challenging. That's why it's taken taken quite a few years, as well as because you we started, you know, completely scratch, and we had to figure everything out. So one of the things that sets the KPI apart a little bit as part of our founding story, where we were able to start the company, we had access to a gigantic data sets of accounting transactions, and all corresponding documents to that. So that helped us just spend the first two years we just spent on data analysis, data science and machine learning development, because we had some thesis and theories that this could work, but we didn't know. So it just had to do that in the beginning, for for a couple of years. So when we saw that this actually has promised, like, we were predicting more and more accurately, and you know, we're gonna get to this inflection point where it's better to use PKI, that do not use PKI. And at that point, people will use it, and then, you know, continue growing more and more customers and more and more data and more and more corrections, and better and better predictions. So we realized we would get to that point. And then, you know, then we raised the seed round back in 2017. And, you know, started developing, Grant What a great journey. I love this story. So tell me about impact outcomes. So you talked about a large number of customers that are using the platform now, what is what's been the results that they've seen? Alex Yeah, so we see. So there's a couple of numbers that we that we statistically enroll from the from the customers, we see that customers have about 80% reduction in the overall time spent on the process. And that comes from two things. One is the percent of fully autonomous. So let's say you're 50%, fully autonomous, that means you spent zero time on 50% of your volume. And then that other part, we've drastically reduced the number of minutes in two seconds. For each transaction a human has to review because the AI has done all the upfront work, humans just reviewing it, rather than sort of processing it from scratch. So you're at seconds rather than minutes. This turns into sort of an 80% reduction in in time. You also have things that we do like prevent duplicate payments, and we have some fraud detection in the system. So you also have some of those benefits that can turn into multimillion dollar when you have a large enough cost base. And then we have audit trails in the system, which helps with figuring out you know, making sure that you know, all of the approvals are doing rights. If anyone has changed in amount or something. It's all logged into the system electronically. So you have some some compliance and auditing benefits from it as well. But right now the main, the main effective impact our customers have, and we typically sell to the mid market and enterprise. So these are larger organizations, they can have hundreds of 1000s, or millions even have sort of vendor bills per, per year, and a substantial amount of people and resources involved in dealing with that problem. So they see very significant ROI from it. Grant So to sell into that particular group, then I'm assuming you've got to have a decent amount of integrations into all of their incumbent systems, right, all of their ERP systems and CRM systems, etc. Right? What does that look like? Alex Yeah, that's, that's true. So we build out these connectors as we as we go, it does, for each connector, the first time we build it, it does take a little bit of time. And then once we have that connect, for instance, you know, to NetSuite or intact or Oracle, the, you know, the next customers that we bring on board, we can leverage the same connector. So it is it is some work initially to build out all of the ones that we need. And then you can grow and scale on top of that for the customer. This is no complexity like we're taking not on completely. So the only thing we need is to know what type of system they have. And we develop that connector as part of the offering. Grant So let's talk about looking to the future in terms of when I'm doing FPA, a financial planning and analysis and I'm, I'm looking at my numbers, and I want to leverage a view into the future. How does something like this help an organization with that? Alex Yeah, that's a, that's a beautiful question. And a big passion of mine, actually. So what we're doing now sets us up to solve that specific problem. So if you think of where we are, now we're in accounting. And what you're talking about is more finance, which is what we'll dive into the idea and the vision that we are getting to is that we want to develop an AI that's basically is a real time cost optimization engine that serves predictions and monitors that in real time, we will be able to help you project kind of what your cost base should be, and how you could reduce spend in various categories and with various vendors. That's that's the piece that we're trying to solve as part of that equation of the of the future. So that you always have multiple pieces of the accounting equation, you have the cost side, you have the revenue side, you have adjustments and close process. And we're trying to stay within one of them for now. And make sure we sold provide real true value in that swim lane before we move on from from there. Grant Yes, I think that part's fascinating, as well, I've seen and interacted with organizations that are trying to leverage shift P and A even into capacity and resource planning, and trying to figure out what that looks like, and especially with it what's going on in the world today, right, with certainly high inflation and certainly supply chain challenges. The need for this kind of capability, I think is dire. Right. I think having the ability to provide something like this sooner rather than later is really crucial. Alex Yeah, definitely. And I think the technology has the ability to hold much more context and see data across all of the things that buck forms, like ours can do in in, in a compliant and anonymized ways, you can see data patterns, you know, across different companies. So you can inform yourself, no more than just sitting looking at your own data sitting inside your own office and their own silo. So that I will, you know, in the future greatly help with, you know, capacity planning, or, you know, cost reduction initiatives and so on. So I think that's one of the powers of sort of, like the cloud combined with with AI and big data, if it's leveraged in the in the right way. Grant And so if I go back just one moment on something, so if we take an AI model, and and we take it through a data set, and we were able to get sort of two key views at it, one is sort of more hindsight, right? One sort of looking backwards, right? It's the AI is looking at it saying here were the drivers that that contributed to the certain behavior, right? So it's more analytic Right. But then there's the other style of using it, which is looking forward, which is more predictive. And oh, okay, here's, here's where based on things we've seen, here's what the high predictive sort of correlations are opportunities are things that we expect moving in the future. When you when you look at Vic AI, and everything that you're providing, do you focus more on one side versus the other? Or you're combining both of those views together? Alex Hmm...yeah, we so in the, in the course of the work in the platform we have now, so every thing we do is a prediction on new data. So that means a new so if you sort of break it down into like, there's a new signal or a new transaction, or a new document coming in, that we've never seen before. And, and the platform then predicts what this is, Donald to vary like line item level detail, how to classify it, and so on. So it does predict this based on the algorithmic design, and the historical data that it seen both for this customer and globally. So it sort of is our engines are purely predictive in terms of what they're trying to predict what's going on. And you can extend that to two other parts, like we just discussed on the cost side as well. You can predict how how you should, you know, maybe they'll just classify this cost, but like, how, what your cost base should consist of a new look at categories. And you can sort of predict this based on your own current spend your growth in the company and maybe other comparable companies, so you can start predicting a journey based on the same technologies. Grant Awesome. So quick question for you, as COVID started to hit, obviously, a couple of years ago, and large companies have their AI models, you know, out there deployed, we're running, suddenly that disruption created an impact to the way in which business was typically being done, right. Not all industries were hit. But certainly a lot of industries are hit. And therefore, business operations changed. And the way in which people conducted business altered, which meant then, that those models were making assumptions based on former operational models, right former ways in which people executed, and therefore invalidated some of those predictive characteristics or capabilities of those models, which meant then, as you know that they had to go do some rework, right had to reduce retraining, right and update the data sets and so forth. So to what degree does Vic AI continually learn, right, so as you pointed out, you get a new document you've never seen before. At some point, I'm assuming it pulls it into its corpus, right? And it continues to learn or relearn on that. Can you talk to that? Alex Yeah. And it's a key feature of the platform, which, which is actually pretty enjoyable when you see it working in reality. So we, so for every transaction that goes through our platform, or every invoice or vendor Bill divest process through our platform, that leaves that leaves us with some learnings. So, you know, it's either it's corrected, and we made a prediction and a human chose to change it. Right? That is something that is fed back into our data set, and also our learning database of sort of, why would that ever happen? What did we get wrong in this prediction? And so for every prediction that we do, we learn something because if we predict something, and it's correct, we also learned something. So you know, we have millions of transactions just flowing through the system every month. And for each of them, we learn something so the system then improves basically, for each transaction that goes through the system. The level of, you know, transaction processing we're at isn't really affected by kind of like global changes, like you mentioned, for for COVID. Like, the only thing that could happen is you suddenly have some some new transaction because you need to do something differently, or you may have fewer transaction, which then just you know, is additive to our system. So you know, we will get something maybe we'll have lower confidence on this because this is something new. So you know, you will have a human come in there and tell the system what to do with this. And then that will be additive to the to the data sets and, and all the algorithmic design, if that makes sense. Grant It makes total sense. Absolutely. Yeah. And I loved how you tied the two together which is, well, I'm still gonna have the transaction. Although the world the macro world around me is changing the transaction occurs while may change is the rate of them. or how, or maybe even maybe even the units or the number of units, etc, right things like that will certainly or could change because of disruption. Being able to predict or understand how the organization can respond to, to disruption, I think becomes more and more critical, as we seem to continue to have more disruptions in business. Right? That happens a lot. Alex Yeah, absolutely. And I mean, what happened with with COVID specifically was also a lot of disruption in how people worked. And, you know, they were used to working from home and, you know, we have some, some of our customers, they have millions of invoices a year, which means, like, 1000s of invoices a day and, you know, suddenly you have, your whole workforce is like, not as productive as they were when something like this happens. But you know, you still need or your your system to be updated, you still need to pay your vendors on time. And otherwise, you know, you'll incur fees, and it'll so just like having an AI system in there that doesn't sort of care about those things. It has the same throughput and outputs 24/7 all year around, makes you very resilient for for sort of things like that are like workforce changes. Grant So that's the right word resilient, you want that organizational resilience, and as the point I wanted to drive out, which is, even though there's these disruptions that take place, most of which are outside of our control, getting our companies into a position to handle and respond properly or well, to that, or to pivot is what organizational resilience is about. And my experience has been AI is one of those tools to help us do that. Yeah, that's, that's awesome. Okay, I want to ask you a very forward looking question. Right? You're ready? Yeah, sure. All right. So I'm not going to ask what are you going to do Norway? Or what I'm asking is tell me about blockchain? What does that mean to you in your world and things that you're doing? Alex Yeah, I mean, that's, we've been thinking about that. And, to be, to be honest, we haven't really seen yet the connection between specifically what we do and how to leverage a blockchain effectively, I like the technology of a blockchain is really awesome. And it can unlock a lot of things. But it doesn't mean that you need to use it for everything. One thing that I think could be an interesting exploration is when you start looking at accounting, ERP data, and whether the accounting systems could run and have their data in a blockchain that is verifiable, to prevent all sorts of, you know, fiddling with the data. That could be an interesting application for for blockchain in in our area. And there's clearly things around, you know, like money movement and value movements that blockchain can solve pretty pretty well. So just in the layer where we are, we haven't really found a great application that applies to us with the blockchain. Yes, but I think that technology is super fascinating. And I think that there are many areas where it is a superior technology to sort of like the trust based system that we have now. And it's an epic thing, when you can run that successfully in a resilient way. Grant When you think and that that makes a lot of sense. It's, it's got some, as you pointed out some interesting potential benefits to the industry. But I suppose at some point, it doesn't really matter where the transaction sits for you, right, in terms of the problem that you're solving, right? In other words, whether it's sitting on a blockchain or it's somewhere else and an Oracle ERP or whatever, right to it, at some point, that probably doesn't matter. Right? Alex Yeah, for us, the Store of the the sort of the store of the data ultimately is for us in the ERP system. And we are not an ERP system, we just layer on top of the ERP systems. So that is the ultimate sort of storage of the data. And that's where if, if, if at all the like blockchain technology could be leveraged to, to kind of store data in in a safe and transparent way. But there's also some new or some transparency issues with accounting data as well, that's going to be kept in mind. Grant Yeah, I would imagine that one of the greatest use cases to for what it is you provide is in the area of fraud to fraud detection, right the ability to regardless of where the data sitting, and regardless if it's on a blockchain or in an ERP system, you Your ai i would imagine would help to discover or uncover some of those potential opportunities. Is that accurate? Alex Yeah. And you have this this platform benefit as well is what if one customer sort of reports. So what happens a lot is just invoice fraud, right? You have, you have invoices that are not from the vendor that they appear to be from. And you have different variations of trying to change payment details, and you know, all of that. So when you see when you see data across 1000s of customers, you, you have a way of detecting those things, and at least flagging them for like, Hey, someone should have an extra look at this, because something may be up, if we're all good with it, that's fine. But you know, have an extra Look at this. And for people, you know, when we, as people, when we're doing something for six hours straight, you know, the last few hours, we're just like, you know, we're just clicking enter and trying to get, you know, the day over with, and, and things can easily pass by that shouldn't pass by. So this is this is problematic in two ways. Number one, your accounting data could end up being wrong, like you just have things that are misclassified. And then you can have things that are, you know, paid, or you're paid twice, or you pay the wrong thing, or you pay an extra zero, you have all of these things that are just humans are it's easy for humans to do those mistakes. And you just want to sort of get rid though those with a technology that can help you remove all of those errors. And we know how, you know, a billion dollar cost base? Just you know, half a percent is, you know, quite a lot. Grant A big number. Yeah, it is. Yeah, you'd hate to get false positives on a fraud situation, right, where you might be accusing someone that, that they've conducted fraud, when in fact it was was just a mistake. This is fascinating technology. So, Alex, where do people go to? I mean, where can you point them to, to learn more about you and your organization? Alex Yeah, I mean, the website is, is the safest place to go right now. So just go to Vic.ai. And we have, we have a good bit of marketing collateral and informative collateral blog posts, we try to educate the customers and the finance team. So finance teams are interestingly, you know, they're, they're not buying new things that often because you know, your ERP system, you have it for a very long time, which which you definitely should. So you know, you're not like a high. There's no high velocity necessarily on procuring things for the finance team. And, and we try to do a lot of educational material as well, because AI is new, but it has true effects or the finance team, and they'll find them blog, blog posts and collateral and can happily reach out to us and we'll help guide anyone through the process. Grant Oh, okay. That's, that's awesome. Alex, you've been so generous with your time here today, especially as you're getting ready to jet off into the sunrise just real quick. Any last comments that you want to share? Alex Um, I think this has been this has been a cool chats. It's been it's been really good. I love talking about, of course, AI and our company, but also the technologies in general. So I think this has been been super cool. And I think that it, you know, it's just wanted to say that we've we've spent a long time building the platform, so glad it's not complex for the customers right now, all of the AI performance, which is actually mind blowing, but you have no level of complexity. So, you know, if you if you want to get started with with AI for a function, this is a very specific function within invoice processing, where you can deploy AI, get the effects out of it with no complexities, and we'll deal with all of that for you. Grant That's actually the beauty of this, you know, to be able to get that down to a few points. Now splint clicks on a SaaS solution. They have all that taken care of, I've written my share of AI code, and oh, my goodness, the fact that you've handled that so seamlessly for the business people is huge. Nice job. Really great job on that. Yeah. Thanks. Great. Alex Thank you. Grant Thanks for taking the time today. Again, Alex. My gosh, this is awesome, everyone. Thanks for listening to another episode of ClickAI Radio. And until next time, go check out Vic.ai. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit clickairadio.com Now.  

In this episode of Financial investing radio, I speak with the person that introduced overnight trading to the financial markets. He will give you some guidance on how to build your wealth, I speak with The Wealth Architect. Grant Hey, everybody, welcome to another episode of Financial Investing Radio. Okay, so today I have in the house, it's taken me a couple of tries here actually, literally, to catch Mark Yegee with me here today, longtime expert in the investing world. So grateful that he took the time to come here today and talk with us and share some of his secrets on how to grow your wealth. In fact, I think he's known as The Wealth Architect anyway, without me saying anything else. Mark, welcome. Mark Well, thanks. I'm not I'm not sure I like the title of longtime expert. But you know what, I guess it goes with the territory. But thanks. Great to be here, Grant, and I can't wait to get into what we do and what you do and have some fun with your audience. It should be great. Grant Yeah, thanks again. I appreciate that. So one of the things that caught my eye when your organization reached out and I was reviewing your profile, I just have to start here. There's this tip here about you getting into this world into the investing world at the age of 12. I mean, holy smokes at the age of 12. I was milking cows and hauling hay. I mean, there was not a stock market in sight. So I asked you, I mean, the only stock I saw had horns, right. And where I was milking it. So you know, if you'd have said, How's the stock market? We're like, well, we got, you know, 50 cows in the pasture, but like, What are you talking about? So, how did you get into this at 12? Mark Well, you know, my dad was a grew up on a farm. I didn't, I grew up in the city. And so I you know, I mowed my yard and cleaned my pool and got paid to do that. And every day, I would see my dad reading the Wall Street Journal, and it had all these symbols in it with numbers by them, and he would circle stuff. And I was so curious about what he's doing. And finally, one day, I'm like, Dad, what are you doing? I think it's like you thought I was ready. And so he said, Oh, this is how you invest in other people's businesses. And I was like, Oh, great. So over time, he started to kind of teach me that, you know, you're running your own business, doing your lawns, but you can also go out and invest in other people's businesses. And I thought that was fascinating. And after a while, after maybe a couple of months of telling me about it, he goes, but the only way you're gonna learn it is to take some of your money and put it in and I did. And so my first stock, I think it was 12 or 13 years old. It was right around that time. It was it was 100 shares of a company called Ailey company. Grant I've never heard of, I haven't heard of them. Mark No, I don't think they're around anymore. But they were they was this women's clothing store in the malls. And it fit all the criteria. Low P I mean, my dad taught me a few things. And it was alphabetical that I went, you know, I finally went, Hey, here's one. So I circled it. And I bought, you know, $300 worth of that stock was 100 shares at three bucks. And I just would watch it every day. And I was fascinating. Oh, there's the new print on it. And there's a new price. And the stock went to $6. So it's it was probably the worst thing that could ever happen. It's like, it's like when you grab the golf club and you hit it right down the middle. You think you're a good golfer? This is easy, right? It's as easy as like this, you know, or if you go to play craps and you hit it on the first you know, first dice roll. And so I I invested again, I saw commercial on TV, you know, and it said us, it said Allegheny Airlines is becoming US Airways or us there. I think it was at the time. Yeah. And I was like, Wow, what a cool name for an airline. And I you know, that was the only reason yeah, I think it might have might have met some of the other criteria as well. So I bought it and it went from 17 I remember to 35 so I doubled it again, which is probably the the next worst thing that could have happened but now I'm like, this is a piece of cake plus I don't even have to work the money is doubling and yeah, anyway, so you know, I guess I could buy more candy than I could. But that was it. That was the beginning and then he started intermediate. introducing me to books. My dad was a big Personal Growth Guy. So I had read, I had read books, you know, by Dale Carnegie very early on, I read books by Edward Thorpe McShane, the business who wrote a book called, oddly enough, he wrote, this first book was called beat the dealer need to deal with a guy who was an MIT professor. So he taught math at MIT. And he went to Las Vegas, and he figured out how to beat the roulette tables. And they, you know, he had another guy helping him and this was kind of a rudimentary metric computer in the 50s. And he figured out where the ball was coming out, and how quickly it was anyway, he figured out a way to get the probabilities in your favor, and he started to beat the roulette tables, and they kicked him out of Las Vegas. So he went back, and then he figured out well, I'm going to figure it out on Blackjack, and you've heard the story, there's been a movie made about him. Is that the movie? 21? Yeah, yep. All these kids went to MIT, you know, the students. And he brought them they all cleaned Vegas out and then finally got kicked out of that. And then he turned his his efforts to the market. And then he wrote a book called beat the market. And it was basically how to buy a stock and sell the warrants against the stock, which today are basically known as options. They're still warrants, but most people don't know what they are. And it was covered. It was a system of covered calls before they even had options. They didn't have options until 1971 or 1974, I think. And so but I was fascinated by because my dad was like, Oh, you got to learn this. And it was this thick book. It was really boring. And but I, I started to apply it. And I applied it so much that when I was 16 1718 years old, by the way, I bought my car with the winnings and the monies that I made in that those first few investments. I set up a brokerage firm at EF Hutton and my dad had this old timer broker. And I said okay, Harry, I want to buy 100 shares of IBM and I want to sell the you know, the call options against it. And he's like what? So we had to call in New York and get the options principal from EF Hutton on. And he understood why zoom, but my broker didn't even understand I did teach him what I was doing. And so all through high school and college... Grant And this was a high school you did that you sold your first in high school high on IBM? Mark Yeah. So IBM, and this is back when you pay a commission of $300 to do IBM. And they had quarterly options. And you know, the different it was a different game. And now we have so many more tools that are at our disposal. That's great. So yeah, I did this all the way through college. And finally, you know, I had several different entrepreneurial ventures and then I actually sold copiers, which for me was the worst thing. Anybody. Grant Like they just sort of jumped into the white when you're selling options, and you went from that to copiers. What happened? Mark? Mark Well, I mean, when I went to college, I didn't think that making money in the stock market was going to be on my my career. So I went to college. I got a marketing and business degree, and everything was hunky dory. And then I got out and pretty. I I started an entrepreneurial company in college. I was back when the Swatch watches were this big craze. Oh, yeah. And you remember those people put 10 Swatch watches on their wrists? Yeah. And I thought, Well, I went to the University of Florida and I thought why isn't there a swatch watch with a gator on it? Like a University of Florida Gator, you know? And, and I went to the I went to the, you know, the alumni office and I said, Can I license the gator. And this was back before the internet. This was back really? Right around the time faxes were becoming popular, but still pretty early back in. In 1980. What was this 85? Yeah. And they licensed the Florida Gator trademark to me. And I figured out a way to get to Hong Kong. I had no money at all. And when I was a junior and senior in college, I went to Hong Kong and I met manufacturers and I figured out a way for them to put the gator on these watches that I wear orange and blue the color the exact colors of the school. Yeah, and I brought back these you know, 2000 watches are added, manufactured and shipped. And I had them in my college wasn't my dorm room was my fraternity. I lived in the fraternity house last semester. And so I had them stacked floor to ceiling all these watches, right? So I go to the games, and I would sell these things and and I learned a lot about a lot, right? Traveling, manufacturing business, you know, buying good quality products versus crappy products. And I expanded to 23 schools in the southeast and finally just got out of that business about four years later. And then I sold copiers after that. And I that was miserable. But it was that was again instructive. And then finally I I said why I've been doing the market all these years. Why don't I just go do that? Amazing. Yeah, I got a couple of I got a job with a guy named Ernie Ollie who had already discount old discount stockbrokers. It was a discount broker like, like Charles Schwab, they were actually good buddies and they started when those got deregulated in the 70s. And I worked for Ernie for or a year and a half, two years, something like that started my own brokerage firm with a partner. And then we grew that to a to a pretty big venture became a Wall Street company that applied to do this financial technology, I could bore the I could bore you with all the little stuff that I've done in between. But it all has led me to this spot where I had a, you know, a big trading firm, Wall Street trading firm. And we traded, you know, billions of dollars worth of securities. Grant  I saw that, that's well, and did I read it right? That did you guys introduce after hours trading? Is that true? Mark Yeah, that was actually my idea. And everybody thought I was crazy at the time. But I thought, you know, we have this system that all we do is if somebody wants to buy and somebody wants to sell, our system was a computerized system, they just matched those sellers. And I said, why does that have to stop at four o'clock? Why Can't We? If somebody still wants to put the order on? What can we do it a 401? And then if we can do it a 401. Why can't we do it at 601? And then why even shut the thing off? Let's just let it run all night. It happened automatically anyway. And so yeah, we introduced after hours trading in 1999, I believe. And I was on NBC Nightly News with Tom Brokaw. And you know, a few things. Grant And then, and you're now you're talking to me now how am I now? Mark And now I've moved up to talking to you. Yeah. Grant Wow, you finally got up to Grant Larsen. I mean, Tom Brokaw, it's been a long time coming! Mark So just a stepping stone. You know, you got to stand on the giants that came before you gotta get to the grant Larsen. So. But, uh, but I'm glad to be talking to you. Because you know, everything that I've done in my life has led me to this exact point. And that exact point is now where I have a few hedge funds that I run. They're all based on all these mentors and all of them knowledge that I learned over the last 45 years. And now we help people call, you know, make what we call Safe, reliable income. Although if you look at it today, with this market, it's not safe, reliable income today. Grant Yeah, I turn my head and I'm looking at it that can you say sell off? Mark Yeah, don't don't even look. It is a light volume sell off. So I believe that there's a bounce coming in a couple of days. But boy, it's, it's painful for a lot of people right now. It's, you know, people think you just buy a stock and you hold it. And that's the way you invest. And then you get these 25% corrections in the market. And people's 401 k's are decimated, they go to 5060 70%. Yeah, it's just a shame. It's just a shame. Grant Ever since November, I saw got it at the end of last November on my systems and went, Okay, I'm gonna start preparing to hedge here. So I've just been building my hedging positions since then. And yeah, we've had some interesting volatility a couple times. But right now it's down hard for sure. Mark So it's horrible. Yeah. And you if you started in November, you probably if you correlate it, it's the exact day that Jerome Powell from the Fed said, we're going to start to raise rates. And from that point, we're down about 27 28%. And some stocks. I can tell you some stocks are down 50%, 60%, 70%. Grant Yeah. Facebook and others. I mean, they're down like massive 5060 out, yeah, Netflix got hammered with your training thing. And, yeah, just a lot of them are down really soon. Mark But it doesn't have to be that way. Right. Like, you know, a lot of people just don't know what they don't know. And we tell people that they can make two to 4% a week. Now, that doesn't always happen. But our goal is just like that analogy that I threw out before about the craps table. It's, it's to get the odds on your side, right? Yeah, I mean, I know, it's this is not gambling. But if you use gambling as an analogy, you can understand it better. If you're sitting around a poker table like Annie Duke or Phil Ivey or those guys that are on, you know, the the World Series of Poker, they don't win every hand. But if you have a pair of aces, you have the odds in your favor. If you stay in, unfortunately, sometimes three kings comes up and your opponent has a king, and then you lose, but that doesn't mean you shouldn't have been in the game. So what we try to do is we try to create safe, reliable income by renting stocks, to other people that are going to our B that are willing to gamble and pay us a premium for having the option to buy our stock. I can explain that a bit more with an analogy, if you if you want to hear it, but that's really what we do. Grant And I'm selling options, then that's that's your main strategy. Mark Yeah, yeah, we buy we buy a good solid stock. So we have, we have a system called the cash flow machine, right? We call it the cash flow machine because you you put cash in, and then it gives you cash out more cash. And that's it's, it's a system that creates income, using what we call the four cornerstones it's the right stock, not just any stock, the right market, because you want the tailwind to be behind you. So we use a component of market timing and does help and then it's got to be the right spot on the chart. And usually you can find a high probability spot on the chart where this were the end institutions are behind the stock exchange in the right direction. Yeah, and we don't want to be against the institutions. That's the big money, right? We're little people. Yeah. So we want to be with them. And we can see where they are, they leave footprints on the chart. And then we go in that direction. And then the fourth Cornerstone is we squeeze the juice or we collect the rent. And that's the option premium that we get for selling upgrades and income. And it's a defensive strategy that we make, you know, two to 4% a month, conservatively. Grant Now, there's, you know, there's obviously a fair amount of margin that's needed in order to do this kind of thing. So you typically need to have fairly decent size accounts to do some of that stuff. What what's sort of the entry level that you see most of the people come in at how much is what sort of account size or capital do they need to have? Mark Well, it depends, we have a breadth of options that you can use so so I have a hedge fund that I run using this strategy for accredited investors, people that are worth, you know, more than a million dollars, you know, rich guys, basically, but not everybody qualifies for that. And I want to do whatever I want to make this accessible to everyone. So we have a set of courses. And we have my favorite thing is a mastermind group. And so the mastermind group is around a series of courses, and their video courses over my shoulder, I show you how to do the trading and you and you understand the philosophy behind it. And I give you the whole strategy. And then it's also surrounded by a full ecosystem of support. So we have like minded people that are also giving you support people that have just gone through the learning that you've gone through, you get mentorship from me, and I've got, you know, for decades of doing similar things in this, you get, you'll actually get something called the private access group where I put out the actual trades I do in my hedge fund. So you can learn from them, mimic them, do them, you know, do subsets of them, whatever. And then on Friday, and again, this was what I was alluding to a bit ago, on Friday, we have a mastermind call where we all get on a zoom call, some of us will share our screen show the trades we're making, I'll usually teach a concept about the current market or something, you know, that we should know. And then we hold each other accountable through a chat group all week, like, Hey, what are you doing? Who's doing what during the Fed announcement? Why are we you know, selling, you know, the Tesla when Tesla's coming out with numbers, you know, things like that. So to answer your question, that mastermind group, it's an investment in yourself, I give a money back guarantee, if you don't make enough money to cover the tuition because it's not a it's not a small amount. But it is the small amount of it's an investment in yourself, and you make it back with your investments. But in order to, for me to feel good justifying that you need about $150,000 to 2 million as a minimum, Now, not everybody has that. And I've had people that just you know, take the courses and do extremely well with five or 10 or 15,000. But they're not going to afford to be in the mentorship program, and the mastermind group and all that kind of stuff. But they can take the courses. And so we have a full breadth of offerings for people just so that we can they can learn it. I also have a free course on my on my website that you can sign up to take that kind of introduces you to the concept of what we do. You know, we got all kinds of stuff. My goal is Grant, it's financial education, right? We don't teach people about money in school. We just don't Yeah, it's it's not at all. I don't know about you, but I use money every day. I don't use Romeo and Juliet every day. And I don't use the Pythagorean Theorem every day. Grant One I don't use while shopping the grocery store. Okay. Mark I haven't used the Pythagorean Theorem, I don't know in at least a week. Yeah. And, you know, I don't learn I don't know much about you know, I don't use Cleopatra, and Henry the Eighth and his wives every day, but boy, I use money, it would be nice to know, would have been nice to know without having to go outside and learn how to buy houses in real estate, how to invest in the stock market, how to do my taxes would have been nice to have learned a little something like that. Yeah. So I believe that that's the biggest thing that people can do is they can invest in themselves by getting financially educated. And so that's part of that's a little part of what I do in the world is is help people with that. Grant So it's interesting that you're making this available to a wide range of people regardless of where they are right certainly you've got the capabilities to help those that are accredited, but for the person that's just trying to get going I mean, you walked that journey so you understand that and therefore you're made this available to them to help them ultimately get there are you positions intended to be longer term Are you have sort of a timeframe Are you more like a swing trader? Are you sort of long term Are you did sort of break it up you got portion of the portfolio's shorter term and some sort of longer term investment What's What's your philosophy on that? Mark Well, I can give you the short answer or the little bit longer answer that has some more depth let me give To the longer answer, since we got a nice podcast format going here, the longer answer is that everybody says, oh, you should be diversified, right. And to most people who are uneducated, don't have the financial education that we should have. They're educated by Wall Street. And Wall Street is run by two groups, lawyers and salespeople. And so lawyers are there to not get the firm sued. And for that, they've put you in average investments, because how can you get sued? How can you sue anybody if you just did an average return, and the salespeople are there to grab assets and a lot, the more you assets you grab, the more they pay the salesperson, but the more the firm can trade of that money and make money on it. And so what they want what we hear, and I was a Wall Street guy, so I can say this, is they want you to be diversified. So they tell you put your portfolio in a nice little portfolio of mutual funds and ETFs, a couple of stocks, and you know, maybe some bonds and you won't get hurt, right? And you get this average low returning 8% thing that you feel great about because woohoo. But that's the average, right? The s&p 500 over the last 500 year or sorry, 100 years, has made 9.4%. So if you're doing around nine point you do 9.6%, you're feeling really good about yourself. But you know, I did a study, exactly. You pat yourself on the back, right. But I did a study a few years ago, and in 2000, I think it was 13 and 14, or might have been 14 or 15. I can't remember but doesn't matter the years, the the stock, the stock market did about 28% or the s&p 500 Dow about 28% during those two years, but the top 10 stocks did 185%. So what you're doing when you diversify is you're you're supposedly spreading out your risk, but you're also muddying up your returns, you're taking the good returns, and you're making them crappy returns by some stocks even went out of business and the s&p 500. And the rest are kind of in the middle, just kind of figuring it out. Because not everybody can win. So why not just invest the top 10. Right. And easier said than done, of course. And so what we do is it's a probabilities game, we we you know, when you and I if you buy a stock, and I know you're a futures guy, too, but if you buy a stock or a future or an option, or any kind of investment, you've got a 50% chance of being right at the moment that you do it, yeah, you have a 50% chance of being wrong. Yeah, because there's a smart person on the other end that's got the other side of that trade, and they got a 50% chance. So it's whatever you do after it. So what we do is we we try to find the right stock stocks that are trending up, have above average return on investment, return on equity, earnings per sales, growth, per share growth, sales, growth, those kinds of things, great, great products. That's that, that gives us a little bit of an edge maybe takes us to 52%, then we try to find the right market, because 70% of the stocks performance, it comes from the performance of the market itself. So whatever. So they're in, right, whatever. And then sector performance is 38% of the stocks performance, right. So you're now you're adding you're stacking these, these percentages 52 to 54, maybe 5556, then you find the spot on the chart where it's about to break out or where there's institutional support, or it's bouncing off the 200 day moving average of the 50 day, there are spots on the chart that statistically over the last 120 years on on the right stocks seem to be where that they are going to support the stock. So now you're inching your probabilities up, you never get to 100%. But if we can get to 6070 80%, great, then what we do is we create income from the stock. Now, I don't know if your audience wants me to get wonky with statistics, but I'll give you one more. Okay, go for it. All right, here we go. When you buy an option, and an option is the right to do something, but not the obligation to do something at a certain price before a certain time. When you buy an option, you have an 80% chance of losing all of your money. 80% Wow, that's statistically what it is 80% chance, all options expire, without the buyer making money 80% of the time, because there's no free lunch. But there's also the other side of the trade, Somebody sold that option to the buyer. Well, if somebody's losing money, 80% of the time and they're the buyer, what do you think's happened on the other side of the trade? Grant I mean, someone's got 80% wins.  Mark Yeah. And that's right, it approaches 80% Doesn't always happen. But it does have the statistics in your favor. Because when you sell an option, you always pocket to time premium. And this is what we teach in the course of of how that works and what that is, but you always get the timeframe, you always get the amount that the gambler is willing to risk to have certain amount of time to be able to do something because they're getting leverage. And you know, you want me to give an analogy so I can tell you kind of what we do. Go for it, mark. So most people understand real estate way better than they understand these intangible pieces of paper. They're not even pieces of paper anymore in the stock market. So imagine you open up your window and your front door. And you look out the front door. And there's a vacant lot across the street that your your other neighbor, your friend Jim owns, right? And Jim puts up sign on it says For Sale $100,000. Right. And so Wow, you got your neighbor's got his one acre lot across the way for $100,000. And let's say this other guy, Bob is driving down the street. But Bob heard that there was a Hilton going to be put right up next to Jim's lot. And it's going to make Jim's love worth, not just 100,000. But since it's going to be this Hilton resort, it's going to be put there, it's gonna be worth a million dollars. Yeah, problem is Jim's broke. He doesn't have $100,000 or not, Jim, but Bob, the guy driving down the street. Yeah. But he goes to Jimmy stops his car and he finds Jim in front of the lot. They're, you know, cleaning it up getting ready to sell. And he says, Hey, I'll tell you what, I don't have the $100,000 to give you right now, you know the to buy the property. But I do have this $10,000 Can I give you the $10,000. And all you have to do is promise to take it off the market and not sell it to anybody else. You get to to keep the $10,000 for doing that. But anytime in the next six months, you have to sell it to me for 100,000. And Jim, the guy selling it goes, Wait a minute here, hang on a second, I get to keep the $10,000 I take the property off the off the market, and you're gonna buy it from me for the same price I'm asking anyway, sometime in the next six months. And if you don't I still keep the 10 Grand. And Bob goes, Yeah, that's the deal. And they shake hands and you make that deal. And they write up a contract. Now a couple of things can happen. One thing is Bob could have been right, and there's a big Hilton, they make an announcement. There's a big thing in the paper Hilton to buy, you know, the lot next door to Jim. Yeah. And now. Now Bob took his $10,000 investment. And now he turned into a million dollars. Yeah, he made a high huge amount of reward for knowing about that rumor. As you and I both know, information is not perfect on Wall Street. Yeah, it was a rumor. And it never even happened and nothing ever happened in the next six months. And there's no announcement. And so the the option expires, Jim kept the $10,000. Yeah, so now that now he's got a $90,000 basis in the property, let's call it Yep. And Bob lost the whole $10,000. So Bob had high risk, because he lost it all. But he could have made a killing. But Jim made the $10,000 no matter what. And he could turn around and find another Bob and sell it to another Bob for 10,000. and another and another another. So to answer your question, what we do is we find a position that we like, like I said, it's the right stock. And then we do the same exact thing in a stock market. So we find a position like Tesla right now is the big one. We're all in. We were an app a lot of a sudden Apple still to some of us traits and Apple, we have these great stocks like Nvidia and Microsoft and you know, the big ones. And there's certain criteria that they fit because this doesn't work for every stock. And then we just find a gambler out there like Bob that was driving down the street that thinks he knows more than everybody else. And he wants to give you some money in order for you to take that stock off the market and sell it to him at a certain price. Before that happens, and we do it weekly and monthly. We don't wait. Okay, we do weeklies, yeah, we do weeklies and people are paying a lot of money to have the option for a week to buy a share at Tesla. In a week that goes up, you know, they'll they'll pay you 20 bucks for a week for the stock to go up another $20 plus more.  Grant So it's really high at that point to write on those weeklies so yeah, it is yeah, yeah, it is. Mark So it's, it's, it's and it works. I can tell you some stories about some of the people in our program, and a lot of people are, you know, physicians and the physicians are. This is funny, funny to me. I didn't know this grant, but a lot of physicians just don't like being physicians, not because they don't like helping people, because that's what they really do. They just don't like the politics. Oh. So they don't want to be told when to be at work. And they don't want to be told the politics and other things they have to write up in the computer education, they have all this stuff. And so they can't wait to retire. And I always say why well wait till you're 65 and your hips don't work and your knees creak. And then you can travel the world and you don't feel like it. Yeah, I don't retire a little bit earlier. So a lot of our guys and gals in our program are retiring early, using using some of these things. And I'm really proud of that.  Grant That's, that's an amazing I love the analogy. And so it sounds like you're doing weekly as well as monthly sort of positions. So you're turning them around that you get involved in leap at all are you doing really long term positions is also. Mark We actually do we do we do long term positions as a proxy for the stock. That's something called synthetics. And we that's a wonky concept because there's deltas and all kinds of things that you'd have to teach people about, but yeah, the two to 4% that we make as our basic and then we kind of ratchet things up, if you want to take a little bit more risk, we like to tell people, it's about three times more return that you get, but take 1x more risk. But it all depends on the stock and the market and how you trade it. And, and 90% of this, at the end of the day comes down to emotion and mindset. And I always tell people, that that's that, to me. Grant That seems like that's one of the most critical aspects of this, there's the mechanics that you're describing that have to be right. But with all those being, quote, unquote, right point, that mindset, if you can't hold that position, or you're not confident in the system, then you really get whacked hard. How do you how do you get to the right mindset to do this Mark? Mark Well, you know, the premise starts from the word emotion and motion, money is tied to emotion very significantly, right? It's the number one cause of divorce even even more bigger cause than bad sex. And, and so money, money is a big deal. And people try it, they work hard for their money. And then when they put their money, it's so easy to click a mouse to get into a stock, right? Click, boom, you just invested $100,000, you don't have a strategy for when to get in. You don't have one to get out. You heard Cramer say something on TV that you should buy the stock. And pretty soon you're like, oh my god, it's down $10,000. And now you're getting emotional. And I don't know about you, but when you're angry or sad, or you know, the you don't make good emotional, emotional decisions, right? Not a time to make a decision. It's not the time. So what we do, and I believe that anything that is worth doing is worth doing right? Is we teach people a series of rules, right? Because rules allow you to say, is this, it's either yes or no, right? If you have a role, it takes the emotion out of the event, doesn't mean we don't have to deal with emotions, because boy, there are days like today, where things are moving around a lot. And you know, but we also teach you what to do in markets like today, like what do you do? Do you react? Do you protect you buy a color? Do you do whatever. And those and that system was just a system of rules is designed to reduce emotions, because when emotions go up, intelligence goes down, and vice versa. Right. So our goal in anything that you do in life, right, have a system like Michael Jordan had a system. And if he became the greatest basketball player ever, anybody has to have a system to do something really, really well. Grant So hands on help to overcome or manage meaning not overcome, manage the emotions through the system, the core of it, that helps you to have and maintain the right, the right mindset. I have another question for you slightly different. Time for one more question. Mark I got as much time as you want.  Grant Okay, question. This is crypto, what is going on there? Is that the place to go put your money? What do you think?  Mark Wait a minute, you said "Do I have time for one more question". And you asked me about crypto, which is a whole new universe of stuff? Yes, I did. Oh my god. Yeah, I have so much fun with Bitcoin right now. And it's, it's because a year ago, I was the biggest Bitcoin skeptic that there ever was. And today I have a cryptocurrency hedge fund because I decided that if I'm going to be in the financial services business, I need to learn about this. And I need to figure out why am I so skeptical? And why are so many people making money on it? And then when I got into it, Grant, I started to realize there are so many and it's not every crypto, there's almost there's like 20,000 different tokens. And I'm not recommending them I'm a Bitcoin guy with a with a little bit of cryptocurrency on the side maximalism. Right? But it's mostly because Bitcoin maximalism for me. And boy, I could get into all kinds of stuff. But if you just look at the whole man, I don't know where to start. But to keep it just keep it short. Let's let's just talk about what money is. Right? Money has certain properties, right? So we'll talk about and if you put three things in your brain as we talk about these. It might it might help but money is first of all, it's it's portable, right? You can take $1 Bill and you can walk across the street or you can go to get on a flight and go spend it right it's yeah, it also means it has to be accepted, universally accepted. So your dollar bill in your wallet will be a universally accepted somewhere else or they'll change it into something else. They won't look at it like a conch shell like they used to 500 years ago and say, well, the shells too small. We can't I used to actually trade with conch shells. Till some country said hey, we got a ton of these. Let's go buy a bunch of their stuff. So it's got to be universally accepted. It's got to be standard, right? $100 Bill is $100 bill, it's standard. It's got to be divisible. Well, you know, you sometimes you need a little less than 100 bucks, maybe not in your case. Maybe you're walking around with wads a hundreds but a lot of us we need you know dollar bills and $5 bills and pennies and nickels, and so it's divisible and it's in let's see what else it's um It's a store of value. It's a medium of exchange. So if you keep those so So looking at the dollar, I just described the dollar looking at Gold. Gold is pretty good, too. Gold's a good store of value, right? It's a good hedge against inflation doesn't pay you in any any interest or anything, but it's a good store of value. And a good hedge of inflation. Problem is, I'll bet you that you don't have any gold on you right now. Grant Yeah, that's it right there. Mark Yeah, that's it. So you're not walking around with a bunch of gold. And if you wanted to walk around with any kind of wealth in your pocket, you couldn't carry it in gold, right? It's heavy, you couldn't go across the border. Imagine if you're in Ukraine right now trying to come out of your country, because you have all this money, your bank account is closed? How do you get your gold out, they're gonna confiscate it, possibly at the border. If your guy they're not even letting you leave. I want to make you fight. So, you know, gold has got some really great properties. And for 5000 years, it's been a really great hedge on investing. You know, they used to actually shave off pieces of gold, but then you couldn't measure it. Right? And so they went to silver and then that's how coins got the ridges on the side of them. I don't know if you know that is because with with the people would shave off the silver, and then the coin would get smaller and smaller. So if it didn't have the ridges, they wouldn't accept that. Anyway. Grant Are you serious?  Mark That's yeah, that's why the ridge is... Yeah, yeah, absolutely. And then and then we can talk about Bitcoin. And now let me just give you a background of Bitcoin, bitcoin is called a cryptocurrency, which, right off the bat eliminates most people from understanding what it is, but it's actually a really simple, it's a really simple product. All money is a ledger based system. When you have a bank account, it's held on the bank accounts, books as a liability, they owe you that money, right? You can go in and say, I want to get my money, and they owe it to you. Right? So it's an asset on your books. It's a liability on theirs, depending on on what you believe, how the Fed really interprets that. But that's, that's another conversation. Yeah, yeah. But but it's all a ledger system, right? You know, you own a house that's got a value, and then there's a liability against it with the mortgage, those kinds of things. The same thing with cryptocurrency, and I'll give, I'll give you the analogy, just in case, there's somebody here that doesn't understand what cryptocurrency is, because it can be very wonky. Imagine you and me and Susie are sitting around a coffee table. And I've got this book, that's this blank journal, and we all decide to write a book. So I write the first sentence. You know, the dog bit, Johnny. Okay. And then you take you take the book, I pass it to you, and you go, Mark wrote the dog bit, Johnny check. That's what he wrote. And John, and Johnny screamed is your sentence, and you pass it to Susie and Susie says, Mark wrote the dog with Johnny check, Grant wrote, and Johnny screamed, Chuck, and that she writes her sentence. And then we just keep passing that around. And we pass around, and then we write this story. And the journal gets thicker and thicker and thicker and thicker. And now it's 1000s or millions of pages. But you know what, the first sentence that I wrote is always in there. And the second sentence that you wrote is always in there. Yeah. And when those sentences are in, that's what's that's the blockchain. It's an immutable ledger ledger that can never be changed. Now, with Bitcoin, it has the advantage of this last component of money. And that this component of money is that was one that the dollar doesn't have, or any other fiat currency doesn't have. And Fiat just means by decree, it's just created by the government. It has scarcity. There's only going to be 21 million Bitcoins ever made, there's might have been 19 million made, the next 2 million would be made over the next 110 years. And so there's a scarce amount of those things. Well, you and I both know that, you know, if you gave somebody a dozen roses, that has a lot of value, but if you gave them two dozen roses that has some good value, and if you know if you gave them you know, 50 dozen roses. Well, that's cool, and you could story but pretty soon that last vowel that last rose doesn't have as much value as the first dozen roses and if you gave him 1000 roses, and 1000 Roses, the day after that pretty soon you'd be like, What do I do with all these roses? Now they're a nuisance and they don't have the value. So with scarcity it's like if you ever saw that tulip mania thing that you'd probably have in in in the Netherlands years ago the Dutch tulip mania it's that's indicative right? Because there was there was a scarcity you know, they created scarcity, but this is legitimate scarcity is 21 billion Bitcoin now. I'll tell you one more story. I know I can get a little bit wordy, but I just got back from El Salvador. So the reason I went to El Salvador is because number one, I run a cryptocurrency hedge fund and predominantly we're tracking Bitcoin. But El Salvador this little third world country that had civil war and has drug issues and Ms. 13 and nobody goes there. He has this really young, really visionary president named naive, okay? And this guy said, if if we're going to use the they use the US Dollar as their currency, and they see what we're doing to our currency in the US. And he's like, why would I want to put my I want to create a change in this country. I don't want to stake everything on this US dollars that's being debased. So he adopted Bitcoin as the first legal tender coin that I heard, and I thought, I gotta go check this out. Grant Well, close. Interesting.  Mark I was hoping it's a small country. I was I was sick. I thought you might have. Yeah, I met some other really cool people because I got invited to some thing with bunch of a bunch of government dignitaries on a different cryptocurrency launch, but it was really, it was really cool. And so I went down there because there's this place place called Bitcoin beach. Oh, no, ran an experiment for a year. And you might have seen it was just on a 60 minutes episode and Bitcoin beach. They just went to everybody and told them, You have to start accepting bitcoin, all the restaurants, all the hotels, all the people selling, you know, the little shell bracelets, and the necklaces and all that stuff. And they said, you have to start accepting bitcoin. How do we do that these third world, people would say, Well, you have this wallet that we're going to give you called the Chivo wallet, that's the name, the name of it, and you put it on your phone? Well, everybody's got a phone, right? And so you just accept it with this little QR code, QR code, what's a QR code, and they show them what that is. And so I went down there, and I bought my dinners, and my hotels, all with Bitcoin. And these people all understand it. They're third world people. And I go down the street and I tell people about cryptocurrency and Bitcoin and they look at me like I haven't unicorn sticking out of my head. And they're like, this will never work. Bla bla bla, it will work because it has all of the properties of money, but you can carry it with you in your brain. All you have to do is memorize 24 words. And now you have access to your cryptocurrency wallet, anywhere in the world. So when they when they when we left Afghanistan, they shut down the banks, anybody who had wealth in the banks couldn't get at it. But if you had the foresight to have Bitcoin, you could get at it. So it's transforming. one more statistic couple more statistics. 70% of the world is unbanked. Imagine the person in Ethiopia, or somewhere in Africa where they don't have banks on the corner like we do. Now. They've never seen a bank. And so they use systems of barter, and they use systems of exchange with and they don't have a banking relationship. But with a $50 phone, and a wallet that holds your cryptocurrency or your Bitcoin, you now have a bank on your phone. So these people are now able to create this ecosystem where they, they they can be banked. The same statistic happened in El Salvador 70% of the people were unbanked. And 30% of the people had access to some kind of banking relationship. After they announced last year, that they were accepting bitcoin as their legal tender. It's the reverse now 70% of the country now has Bitcoin on their wallet because the country gave them $30 worth of bitcoin. So they can either save it, spend it, you know, give it to their buddy, whatever. And they're all part of this like new ecosystem, they figuring it out, they're spending money. And it's it's fungible, it's accepted. It's it's a store of value. It's it's it's universal, it's divisible. You don't have to buy one Bitcoin at $40,000, or whatever it is today. You could buy 100,000 of a Bitcoin. Yeah, you might fraction, right. Yeah. Yeah. So that's the long answer to a very short question. Grant Well, yeah, well, it is it is a future. A lot of organizations pursuing it, who feels at risk by crypto who sue who isn't that's going to lose, right? What organizations or governments would fight against this? And why would people fight against moving to crypto? Mark Well, first of all, it's more accepted than you think. There's another country that accepted it in Africa. So there's two countries now that accepting it as legal tender. There are cities there's a city in Switzerland that is now accepting it. It's being widely adopted. So first, it was just a couple of nerds. And then you know, I don't know if you know this, but the first transaction on Bitcoin was to buy to Papa John pizzas, and I think it was for 10,000 bitcoins and the guy goes, Yeah, I'll give you the pizzas for stupid 10,000 Bitcoin. Well, that's bitcoin is now worth $453 million. But that was the first real transaction and it's actually a great story about two pizzas being worth $400 million, or whatever the number is. Grant So man, I did not know. Mark Wow, yeah, no, that's they call it the pizza, the pizza trade. But there are some entrenched interest in doing this because the government first of all is debasing our currency, our currency is lost 99 Point 5% of its value in the last 100 years. Right? That's why a car an average, sorry, an average house today cost $250,000. But that house, you know, it's a similar house in, you know, 20 Sorry, 1920 cars $5,000 We've We've debased our currency to almost nothing. And we feel like we're getting rich, our houses are going up, but you're not getting richer. It's just the denominator is getting more debased. So the governments are all threatened by this, and they don't. So what they're doing is they're trying to come out with something called a C D, BC, a centralized digital banking currency. Right, Senator CBBC. And, you know, they think and if you think about the dollar, it's already electronic, like on my phone, I have Apple Pay and Google Wallet and visa and, and I have, you know, I can move money through my bank account. One other thing that Bitcoin you can do is, and I had somebody that wanted wanted some money from the hedge fund last week, and she asked for the money on Wednesday, I had to clear it out of the brokerage firm on Thursday, it had to get to my bank on Friday. And then I had to wire it over the week, you know, on Friday, and it got to the she got the money a Wednesday on Wednesday. And I said, if you had just asked for Bitcoin, you would have had this money in 10 minutes. Yeah. Because banks, Bitcoin never closes, right, you can sell the coin on a Saturday or Sunday at three o'clock in the morning. So the government's are trying to figure out how to get in the game. Because if they're not in the game, they're going to be out of the game. The problem is, you don't want the government to be in charge of having your control of your money. That's the problem we have now. You don't want more of that. Now then they could just print that and infinitely like like many of the other stuff they've been doing. Yeah, yeah. That's, that's the big deal. Grant That is that's, that's a huge deal. Okay, so let me ask you this. So you've shared so many great insights mark, it's just, it's amazing. You're a wealth of insights? Well, you're a wealth architect, I guess you're living name, that's for sure. So where where can people go to learn more about you, and what it is you and your team are providing? Mark Well, there's lots of places, you know, marquee around the web. But I set up a site, a little page for us here for this particular podcast for your audience. And it's if you want to grab a pen or put it in your phone, it's it's go dot Destiny creation, because we believe in creating your destiny. So it's go dot destiny creation.com, forward slash grant. Very nice. And so if you go there, we'll have we'll have this podcast there and some notes and some links, but I'll give you guys who are listening. Not only a free book ebook called relic, regular paychecks is how to how to create regular paychecks out of the stock market. But if you poke around there, on our website, you'll figure out a way to get a free course to seven day we call it the accelerated training program, it highlights and teaches you actually, two of our programs. One is called the stock trade genius program. And the other is cash flow machine once for growth and once for income. And, and you know, then you can poke around and see if you want to go any further with us. But the bottom line is I want to educate you, I want you to figure out what you don't know, right, because there's a lot of times people just don't know what they don't know. And I don't want to see people happy with 8% returns and having to work for 45 years, and then retire on 20% of their income. I want to see people wealthy and you know, thriving and even in this market. So this is the this quarter has been the worst quarter in it since in since the Great Depression, the worst beginning of any years since the Great Depression. Most of our investors in my hedge fund made money this quarter. So it shows you that by playing defense, you actually can play a little bit of offense. Grant At the market today, we're already back to like, it's almost wiped out. Like the in fact, I think was wiped out, or at least on the index is the entire year. Right? Yeah, it's wiped out. Like, like, like, like the entire year. That's amazing. Mark And yeah, at least Yeah, that's it. And that's what the market does, right? It goes up. They always say it goes up with the staircase, and that comes down with the elevator. So the market just gets hammered really quickly. And it goes back and you go wow, it took two years to get this. And we gave it back in three months. Grant Got it. Yeah. Okay, so it's go.destinycreation.com/grant. I appreciate you doing that. That's very kind. Mark. Thanks for your time. Any final comments you want to share? Mark Not really. I mean, first of all, this was a lot of fun. You had some really great question. Do you have some really great insights, and I hope I didn't talk too much. I have a saying and I'll just leave you and your audience with the saying it's never give up your power in your health, your wealth, or your time. So thank you for your time and I was so honored to be here with you today. Grant. Grant Thank you. So much Mark. I really appreciate all your insights and the wisdom that you shared everybody. Thanks for listening to another episode of Financial investing radio. And until next time, go get your destiny creation. Thank you for joining Grant on Financial Investing Radio. Don't forget to subscribe and leave feedback.  

Hey everybody. In this episode, I have a fascinating conversation with Heather Dreves who shares some secrets on how to get returns from your real estate without being an expert. Grant Everybody, this is Grant, Welcome to another episode of Financial Investing Radio in the house with me today is Heather Dreves of Secured Investment Corp. Now someone reached out to me and said, Hey, have you taken a look at Heather's profile. And when I reviewed it, I started to realize, hey, there's some things that she's talking about that I've been able to dip my toe in, in the real estate space. And when I saw what she was doing, I thought, oh gosh, this would be really fun for us to hear more about the unique solution that they're bringing to clients. So let me take a breath and say welcome, Heather. Heather Well, thank you for having me. I'm excited to be here. Grant Very good to have you here. And you know, when we were talking before we got started, I think you probably live in one of the most beautiful places on the planet, which is awesome. So thanks for coming out of the cocoon of beauty where you guys live, talking with the rest of us. Okay, so first things first. Tell me a little bit about how your dreams and what got you going into this business area in real estate? Heather Yeah, well, I think everybody has a story, right? Like, a lot of us started with something else. And then was led down a path that was probably a better calling, per se. I went to college to be a teacher and decided I really liked my children but wasn't really interested in spending all day with everybody else's kids. That's it. Grant That's good to find out.  Heather Yeah, yeah. Well, my kids gonna test it out. They're like, You were terrible at helping us with homework. But anyway, so stayed home with our kids for a long time, we were kind of entrepreneurs and my husband was running an indoor soccer center facility. But I had a very good friend of mine that was in the private money industry. So when our youngest son was in school full time, I decided to go back out into the adult world. And he's like, you know, come to work for me. I said, I have no idea what you do I have a mortgage, is it the same thing? He's like, No, come down to my office. So long story short, 20 years ago, walked in there and just, it blew my mind. I had no idea that one, if you were a real estate investor, you could get funding outside of a bank and traditional sources. And two, I had no idea that you could invest money that way. I thought everybody went to a financial advisor, everybody, you know, stocks, bonds, mutual funds, like I just I was, my eyes were wide open. Grant I mean, that's such an uncommon knowledge just right there. Even some of our listeners will be like, wait a minute backup, say that phrase again, you can do what say that one more time. Heather So you can buy real estate, and get funding for real estate transactions outside of a bank. So you can acquire what we would call private money lending to buy real estate, a lot less restrictive than a bank, you know, they typically private lenders will look at what we call an after repair value, which banks don't do, you know, they want to know what's that property worth as it sits, and that's the most they're gonna lend on it. private lenders are much more creative. And they can look at a property and say, hey, I can see the value you're going to add, and I can see your vision. Absolutely will end on that. And so, that was mind blowing to me first. And then I started started with working with investors that said, you know, I've got money in the stock market, but I also want to invest in real estate. But here's the deal. I don't want to own the real estate. I don't want to rehab the house. I don't want to deal with a tenant and toilet but I want to reap the benefits of that. Grant So intended for more passive participation. Is that the idea? Heather Absolutely. Yep. So I'll huge majority of my clients, you know, have been active real estate investors in the past. They like real estate as an asset class. Again, they don't want to do it themselves, but they want To be the lender per se, and so there was this opportunity to match active real estate investors with people that are looking for a more passive Path to Wealth and and match the two and it benefits everybody. Right? Grant So what's the what's the typical profile of someone coming to service like this? Heather I would say on the borrower's the active investor side, you know, there there are people fixing and flipping, and they're everything from the guy that has a full time job that's doing it on the weekend to the very active clients that are for like 100 houses, really now they don't want to deal with a bank. There's too many restrictions. You know, we obviously have a very clearly defined guideline process and underwriting process, but it's much different than a thing. We're we're focusing on the asset, and then also focusing our attention on the borrower, we're making sure that they have the financial wherewithal to actually finish the deal. Can they make the payments, you know, all those things, we look at credit. But then on the on the passive side, the clients I mostly deal with there, you know, one of the niches I've really gotten involved with is high net worth dentist right now. Grant Did you say dentists? Heather Dentists. Grant Okay, I did not expect you to say dentists. Heather Well, here's the thing. They have tons of capital, right? Yeah, they have no time to manage their money invested, you know, and a lot of times their biggest asset is their business, they'll sell their practice, when they retire. Now, they have this influx of massive capital. Oh, interesting. A lot of them are very educated with real estate, but they don't have the time to do it. And so I would say that the profile for my passive people are people that have started to create some wealth for themselves, and they want to either create cash flow, so maybe they're retiring. And they don't want to tap into what they've saved, but they want to live off their earnings. Got it, or a new niche I've really I'm pretty excited about is our growth minded clients. So you know, I don't know how much you know, about a real estate fund. But for a long time, you had to be a high net worth accredited investor to even ever think of investing in these types of funds that we're talking about REITs. Yeah, well, we are similar to a REIT. But we are a privately managed real estate. Oh, interesting. Okay, got it. Well, we're privately managed, but the model is the same. It's real estate assets, throwing off profit. And that's how people make money as off their earnings. But 10 years ago, you could only open that type of fund up, if you were what's called an accredited investor. That's the only people that could invest. So you had to have a million dollars in assets over income of $200,000 a year 300. As a couple. We feel really passionate, we think everybody should have the opportunity create wealth. So four years ago, we actually have the ability to open up what's called a Regulation A fund, and anybody can invest in it. We started our minimum at $1,000. And it's open to anybody, and we pride ourselves on this, it took a long time to get approved for this spot. Grant That's fascinating. Okay, that's the first I've heard of something like that. How many organizations do that?  Heather I mean, I haven't heard of anyone, not many that are a real estate fund. Most of the time, they're a crowdfunding platform. So you're, you know, you're investing in a platform, that's an angel investor, or they, they provide business loans. There are only a few real estate funds in the United States that were approved for that. So it's pretty uncommon. Grant That is, wow, that's very interesting. Okay, that must have taken a lot of effort to get that I mean, so that's feels like that's such an untapped market too. Huge market. Heather What we cater to is we work with a lot of clients that have 401k Is that they didn't move from a previous employer have small Ira balances, you know, they funded it with six grand and, and then they never did anything with it. This is a great opportunity for those kinds of funds. You can roll those accounts over to self directed custodians. So they're still tax deferred, you're not, you're not taking a distribution on it, okay? You're just, you're just now deciding where you want to invest it instead of having a financial advisor or a money manager, and I'm not here to tell you to people to pull all their money out from their financial advisor, really, it's just a way to diversify. And it gives people the ability with small dollars to invest in real estate because a lot of people think, well, I've got to have, you know, a couple 100 grand to buy real estate, no, you don't, you could invest in a fund that's investing in real estate and you reap the benefits of it and you have zero headaches.  Grant That's, that's awesome. So let me say back to you because it sounds almost too good to be true. Okay. You're telling me that someone could take their four When k or whatever other capital they have, there's not a bar, a minimum bar, they can roll that into this investment fund investment fund is going to be making the investment decisions, as well as managing the properties. And these people then just participate that investment participate in sort of a passive, passive return is that you said? Heather Yep, yep. Grant That's really awesome.  Heather Average yields are eight to 9%. We've managed funds for 10 years. So we have a really good track record. They are very regulated by the SEC. So there is a another set of eyes overseeing it. And they're fully audited funds. Grant Okay. Okay. Then what's the risk side? Heather Well, the risk side is, you know, not any different than any other investment, right risk is the market shift values decrease, I will tell you that our funds are a little bit different than most in the sense that we focus around residential real estate, okay. And we were not what people would call a syndication where we're investing in apartment complexes, we focus our investments around single family up to four units, all in the affordable housing market space. So we do two things with the funds. So part of the fund, we lend the money out. So real estate investors that are wanting to fix and flip we'll lend them money, we take a first lien against their property. So worst case scenario, they don't pay, we foreclose on the house. And now we own a house with 30% equity in it because we don't lend more than 70% of the the other portion of the fund we buy real estate, specifically in Coeur d'Alene, Idaho, and Spokane, Washington. So we invest in projects locally. We don't preach people trying to manage a rehab five states over and we don't do it either. So that's our model. Grant So are all of your properties in that area, then? Heather Yes, all the hard assets that we buy are only local, we went out is nationwide. Grant Because I've worked with I've worked with some groups that will take the approach of the look for places in the US where the volatility of the real estate market is small. The intent is to create a cash flow through sort of this renter model, if you get appreciation on the property great, but it's not about that it's more about sort of that rental cashflow. How different are you from from something like that?  Heather Well, we look at all of that. And that's another reason that we stay in the affordable housing market space. Because let's let's say that, you know, our exit is to rehab a house and flip it and the profits flow back into the fund and the market shifts. When you're in that affordable housing market space, you have a huge opportunity to cash flow that property in the event that you need to write out, you know, values decreasing. So we're much like that. And I think you'll have to be as a fund manager, you have to be nimble, you have to have multiple strategies. If you start relying on one stream of income for that fund, you're in big trouble when things change. And so we're much like that I you know, in the past, we've done very well, fixing and flipping, and our clients have done very well fixing and flipping, we're right now educating our clients that hey, some markets are turning like in an inflationary time, real estate's a great place to be. But it's also a great place to be for rental market. And so we're seeing a ton of opportunity for cash flowing properties. And so we're starting to add that to our to our portfolio. Grant So a couple minutes ago, you were talking about another class of investor that sort of a high growth or high high growth minded and then I think I may have entered interrupted you on that. What is it about them that you find interesting? Heather Well, the nice thing about our funds is when people make a decision to deploy capital through our funds, they can set their accounts up however they want. So what I mean by that is, they can either set their accounts up to reinvest their earnings. So earnings are paid out monthly. So we pay out all profit, which is really different than most real estate funds. Because if you're investing in a fund that's buying an apartment complex, for example, typically your returns are realized five years down the road, you get the benefit of depreciation, which is great, but you don't typically see your high yields till they sell that property. Right, right. Are our funds cash flowing? So we're lending on loans or paying off or buying properties? We're selling them. So all those earnings or profit are paid out every month? So you can either reinvest your earnings and roll them back into your equity membership, which is yeah, if you're working with self directed IRAs, that is an awesome option because you don't have these dollars going back. All right, and then they sit there and deploy until you have enough so you can reinvest for more growth minded clients, or for my guys that are looking for cash flow, you know, retired sold their practice trying to replace their income. They liked the monthly model because they're getting, you know, the earnings. Yeah. Grant So on the property, so sorry, Heather, quick question on the property itself when it's purchased, is it the fund that is on the property that owns the property? Or is it you know, allocated to the individual? Heather No, the fund owns the prop. Yep. Any loans that we originate? The fund is the lien holder. And that that's another thing is these are tangible assets. So risk is, at the end of the day, the markets go haywire, we just start liquidating assets and paying people off would be worst case. Right? Grant And that's how that's very interesting. Because, you know, I, currently, my wife and I, we invest in properties, but we do it where we're the first in other words, it's, it's, it's on us, right? So we have property managers that certainly take care of it, because we really like that, you know, cash flow kind of idea, right? And you want to be sort of passive with it, for sure. But at the end of the day, it's it's my name on it, right. And I think that's a unique thing about what you're describing there. I'm listening to a gun, though, that would distribute the risk, right, and spread it around there a little bit as as the individual investor. Heather Very cool, right. And that's what you know, a lot of people like, I have a large percentage of my clients have done real estate in the past, they've owned rentals they've owned, they've funded notes, they've fixed and flip, they, you know, developed and they're kind of at the point where they've, they've got capital now, and they don't want they want to retire. They, they want to, they want to golf, and they, they don't want to hear from a tenant that their toilets plugged in the middle of the night, you know, even though you're using a property manager at the end of the day, it's still you paying for it. That's right. That's right. You know, that's where I think there's benefit there. And then also for our clients that have smaller dollars that, you know, our husband and wife both working full time with kids, they don't have time to be rehabbing a house, this is a way that they can start to put that money to work for them. And it's shocking how quick it grows, especially your tax deferred accounts, self directed IRAs, and self directed 401. K's are like, people don't talk about it enough. It's, you know, you can do traditional, you can do Roth, if you're not working currently, for the employer that provided the 401, you can roll that over and start deploying that capital. So there's just so many other options out there that I just don't think people are aware of. Grant What's your take on potential impact to this model with, you know, interest rates slowly nicking up there from the feds and such any any concerns there? Heather We do. We talk about it. We meet multiple times a week, there's three fund managers to include myself, I think what we're already seeing, I mean, our market is a little different. It's still very hot, but we're starting to see it take longer for houses to sell. I think prices are going to stabilize. I don't know that we're going to see a I don't think we're going to see a downturn like 2008. I don't think that's going to happen. banks aren't lending at 120% of value. But at some point, I would I think homeownership is going to be affected and there's going to be a huge opportunity in the rental market. Grant I think it'd be a soft landing, right? Heather Yeah, yeah. Well, there has to be right. Yeah. You you can't have interest rates as low as they were in values increasing at the pace that they were. Grant No, it's crazy, right. Especially what the last. I think you're saying before we started chatting wasn't like you've seen it just in the market year and 40% valuation increase just in the last two years. Is that right? Heather Yep. Grant Yeah, boy, that's in the Wall Street. Yeah, that's better than this. Heather We bought a little house close to my office down here. My our younger son was going to call it there's actually a small junior college here. And three and a half years ago, we bought it for 200. And I was sweating bullets. I thought, oh my god, we're just overpaying for this house. The house next to it just sold for 450. I was like, Why didn't we buy like five of five of these? Yeah, I was thinking but you know, it's but but again, that shows you that a lot of locals in this market are selling because it's a huge opportunity, right? They'll never make as much money but now they're displaced. And that's why as a as a fund management team, we're saying hey, multifamily, small multifamily. We have several duplex projects going and for plexes because people have to have somewhere to live and at the prices that have increased here, you know, most of the locals can't afford it. And so they're going to be you know, looking for housing. So we think that that the housing market right now, is it the opportunities in rentals? Grant Yeah, that makes sense. Quick question about your clients in terms of preparing them. So given that some of these models are new to a certain group of people, what do you guys do in terms of getting people up to speed or educating them? Is that some of the things that your organization does? Heather Yeah. So the interesting thing about our company is we actually are an education company. First and foremost, we put on so it's interesting, we used to put on live events, like maybe at most for a month, usually two to three will COVID hit. And as everybody else in the education space that did live events, quickly, you're either closing down or you're pivoting saying how do we still pull this off? So what's really cool about that is we went live with all these these events, webinars, podcasts, online live events, and now we put on anywhere from three to four a week. So yeah, so and they're everything. They're from a grant, you want to buy a piece of real estate, we're going to teach you how to rehab it too. Hey, Grant, you want to be a loan broker? We'll teach you how to do that. I am mostly involved with a lot of webinars where I just did one last week, and it was all about how to buy a note how to educate people about how to buy a mortgage, or what some people call a first lien. How do you do your due diligence, what do you look for? And so we just, we really believe in helping our clients be the most educated they can to make the best decision, you know, and and so a lot of webinars, we have one tonight, we do every first Monday of the month, called our CEO, fireside. And that's all going to be focused about around the rental market. So education, just tons of online, tons of education. Grant Perfect. And where would someone go to find those webinars? Heather So they if they're want to learn more about the passive investing side of things, I would send them to our main website, which is at secured investment Corp, no plural in their secured investment Corp. There they're going to learn there's tons of webinars, different podcasts I've been on. And that's more your education about passive investing. If they are a more active real estate investor, they want to go find the deals they want to make offers, they should visit our website at Lee Arnold System of real estate. Grant Sorry. Did you say Lee Arnold? Heather System of real estate system real estate? Got it? Okay. All right. Yeah, that's awesome. Kind of two different paths, it kind of just depends on what they're really trying to accomplish. I mean, that's what I talk with my clients is like, what are you trying to accomplish? How much time do you have? And how much capital do you have to actually commit to that? And then that typically dictates what path they're going to take? Grant Is it pretty evenly split? Or do you see more people going passive? What's What do you observe? And Heather I'd say it's pretty evenly split. Honestly, you know, I think for a long time, it was heavier on the, you know, everybody, you know, you got HGTV, and they make rehabbing look like it's I've I've rehabbed and I never looked like anybody on HGTV, and my husband and I almost divorced each other. So I said, I thought we were going to be like, Chip and Joanna, like, this was a thing. I was like, Oh, it was good. Like, that was a big buyer, you know, through COVID, you know, you couldn't get into buy houses, you couldn't get the county out there. Now you're having supply chain issues, you can't get materials. So I think it has started to shift a little a lot of people have left employers through COVID. And now they've got these 401k sitting there. And they're going What am I going to do with this? So I think that's created a lot of opportunity and interest in the passive side of it. So I think it's right now it's probably 5050 5050 Grant Awesome. Okay, while Heather, you've just been incredible sharing these, these experiences that your organization's had and the value that you're bringing, I have a quick question. Are you still doing CrossFit? Heather I am I'm getting back into it. I kind of fell off the CrossFit wagon. Last year we moved we were, you know, again, developing five acres like it was going to be the big glamorous thing. So yeah, I not as much as I was but I'm getting back to it regularly. Grant That's awesome. I love your story and what it is you're doing for the market and for people changing lives and given them a livelihood to build on all the like you said all the nest eggs they've developed in a way that's got a sounds like a lot of risk taken out for the people. So that's amazing. Heather We're here To educate people and give them a pathway to create, you know, generational wealth for themselves. And we just strongly don't agree with the fact that you have to be a high net worth individual to take advantage of this. So we pride ourselves on the fact that we we have a real estate fund with people with as little as $1,000 can invest in. Yeah, people have to start somewhere, they really do.  Grant Well, and you may have a free, you may use the phrase there that I love, which is around generational wealth. And sometimes that gets in the popular culture that's not talked about much. But for people that are being intentional with what they're trying to do for their families, those kinds of strategies mean a lot. So the fact you've made it accessible to the masses, and you're helping them solve that generational wealth problem. That's that's really awesome vision that you guys have any last comments that you'd like to share? Heather No, I mean, I guess I what I would like to share is if you have any interest, or you want to become more educated about self directed IRAs, and 401, K's just for clarification, we are not an IRA custodian. I'm not a tax professional. But I know a lot about those accounts. And I would encourage anybody, if you even have a small interest in it, get a hold of me and my team, you can get a hold of me at our website at security investment corp.com. And we'd love to give you some references for you know how you can set those types of accounts up if there's one thing people take away from this this presentation is that there is opportunity for investment through tax deferred accounts. Grant Very cool. Wow, what a great, great service you guys are providing Heather, thank you for taking the time with me here today. I appreciate that. All right, everybody. Thanks for listening to another episode of Financial investing radio and until next time, go check out Secured Investment Corp. Thank you for joining Grant on Financial Investing Radio. Don't forget to subscribe and leave feedback.  

Hey everybody, welcome to another episode of Financial investing radio. So in this episode, I have the opportunity for a second interview with Jerremy Newsome. What's interesting about this with Jerremy is he's got this organization called Real Life Trading. And he's got this. I love his mantra. It's basically they're there to enrich lives, and he gives away tons of free financial investing and trading courses, just gives it away. He feels like like this should be available to everybody. And he told me and it wasn't part of the interview afterwards, we were talking about it. He told me about an upcoming deal he's got I think it's on May 5th. If you go to reallifetrading.com/funding. What he's doing is he's given you an opportunity to get $200,000 to invest, you got to prove yourself, you got to follow some rules, otherwise, they'll they'll fund your account. Kind of an interesting, interesting deal there. Today, though, in this conversation, Jerremy and I are talking about crypto trading, adding it to your portfolio. Everybody welcome to another episode of Financial investing radio. So I went and got on my knees and begged and pleaded to one more time to get in the house. Mr. Jerremy Newsome first of all, thank you, Jerremy, for taking the time to be here. Jerremy Man, my pleasure. You're so welcome. And I'm honored. This is my privilege as well. So thank you for having me. Grant So you and I spoke, I don't know, few weeks ago, and since we spoke, I went and I checked more of his stuff out, you guys get to check his stuff out. I want I checked out his stuff. And I went on to his site real life trading.com. And, and he's got all this free content out there. It's incredible. I've spent 1000s and 1000s of dollars for that kind of training before I was amazed you had that out there. And I've knocked out your first three courses on it. And I have to tell you, first of all, you're an awesome educator. And second of all, you really know the markets and you teach them well. And third, I've been applying as techniques and it's made a direct impact on my on my trading activity. So okay, there you go. That's my hero worship. Jerremy Thanks, dude. It means a lot to me that you did that because you're a man of your word. You said you're going to and you did it. Yeah, that's phenomenal.  Grant Yep, sure did. Sure did. Okay. So what happened then is after Jerremy and I talked last time, I said, Gosh, that went really well. Are there other topics that you cover? And we're texting back and forth? He's like crypto and like, serious. Okay, of course, that's before I gone through your training courses. Now. I realize oh, yeah, you definitely are gonna, you know, knock it out of the park and crypto. So we wanted to discuss crypto in this world that has so much controversy around it. Some are like all in on it something it's you know, the devil's mother. It's just got all sorts of different views on it. But bottom line, let's start with what's attracting people to crypto. What are your thoughts there? Jerremy You know, that's a good question. And I think what attracts most people, and this is probably a slightly unfortunate answer, but that's okay. It's just like explosive games. I think that's what attracts people to it, because they're like, well, this thing is brand new. They heard about Bitcoin, they probably saw it somewhere around five to $10,000. Back in 2017. It's all up to 20,000. And now it's at 34,000. And so they're just there, they're doing the math right. People are like wow, there's a lot of opportunity. There's a lot of returns to be made. And same thing with NF T's are same thing with just active crypto trading like there are ridiculous ridiculous numbers and some huge success stories that have happened in crypto for sure. Grant Yeah, absolutely. So when you think about how much money I have to have in order to trade you know, options or or to be buying stocks or Doing futures, they each have their own requirements. What does it mean for crypto? If you want to get into that world? What do you need to have in your bank? Jerremy So that's what's cool, I think probably later down the line, but certainly, an aspect that will attract people is you can start with any amount of money, there's no regulation on how much money you have to have. So for example, as you mentioned, you have to have $25,000, the day trade, right at least, if it's if it's in a margin account, and if you're here in the US, but you have to have a certain amount of money to place a certain amount of trades in a certain period of time. Well, with crypto, you don't any amount of money, what 100 bucks doesn't matter. And what's incredible about that is you do have the opportunity, in my opinion to then offer new insights to new individuals, right? Maybe their children, young adults, maybe their 1415. Kids can trade this stuff, right? You can give, you can create your own account, put three 400 bucks in there and let a child go wild and see how they can trade because there is no if you don't use margin, which is pretty hard to do actually in crypto. So if you don't over leverage yourself, which again, is very difficult to do, the chances of you losing all of your investment are pretty small. And the chances of you losing more than you invested. If you don't use margins are pretty much zero, at least to my knowledge. I haven't heard anyone doing that yet. Therefore, it allows really an experienced individuals to get experience through the through the school of hard knocks, you know, why not? But with any amount of money, man, that's incredible. Grant In fact, on that point, just real quick. So when when we talk crypto, what are the what are the main instruments that you're referring to? Jerremy Man instruments I would say like the the decentralized, peer to peer currency that most people will probably heard about Bitcoin, you have a theory of them. There's a very popular website called coin market cap.com, coin market cap.com. And any individual can go there's free and they can kind of scroll through and determine and visually identify what are some of the larger coins, tokens projects? What are people doing? And how are they doing it. And it allows them to really just see what's out there because from a market capitalization standpoint, it's a market capitalization, meaning how big is this asset, I usually stay in the top 30. So if I'm trading something, if I'm putting my own currency into it, my bitcoin or US dollars, whatever, if I'm buying a token or a project, I'm usually staying in the top 30. So that I can assure myself of liquidity, which means the ability to sell, right, so there's almost always a buyer on the other side of the screen. Grant  In fact, I got involved real early on some of the Bitcoin stuff, but there wasn't as much liquidity back then. And that turned out to be good and bad, right? Those those a lot of that's overcome, certainly, obviously in the Bitcoin area. But that's great advice to stay on the top end of those is I've come to get to know you and your mission, which is to enrich lives, right? I love that about your mission. In the fact that you're you're taking Kryptos away to teach younger people about this, right? These are things that are not taught in school, right? How do you actually go about making money? Right, and how do you go about building wealth and growing it? Those are critical lessons learned? And I know that you've done some I haven't seen it. But I've heard that you've done some things around education for younger people on this. Can you talk about that? And have you done it in the crypto area as well? Jerremy I have. So I came up with a book not too long ago called a stock stock market journey. How to make sure kids win at life every day. And in that book, actually kind of blend in the nuances of currency. And talk about money is a fluid discussion because I mean, it is right it's a universal language. It's something that it's borderless, money is and it changes from border border, oftentimes, but allowing kids to realize that this is like a video game, you have to learn it, you have to play it, you're gonna lose, just like in a video game that you're not always gonna pass the level or whatever you might initially. But as it gets harder you get to determined you increase your skills and your capabilities through that difficulty to come out on the other side proficient at this game. So my take is yeah, I've created a live course that I did in March. So March is kids month, every month in my company. So I did a live educational event for children. And after that, it was a week long and at the end I today an offered a mentorship program for children and their parents to go through with one of my mentors so that they can learn together. It's kind of like a like a spring break at night. Learn and just understand more of it. because my thought Aussie grant is not going to change your life, and are they going to become full time stock traders? Probably not. But they will start to see the world differently. And they'll believe different beliefs now, because they will see that money can be created easily doesn't mean that you always have to choose that route. But it's at least a choice that is available to you. Grant Yeah, I love the fact that you're you're providing that for for these people. You got an awesome mission. Okay. All right. Let me get back to successful crypto trader. What does that mean? How do you do that? Well, like what does it mean to be successful? crypto? Jerremy Yeah, I mean, valid question, I think, I think success. For me personally, I define the word success as the opportunity to recognize or did I recognize an opportunity, like, That's it, if I recognize opportunity, I'm successful, I made it easy for myself to achieve success. But from a monetary standpoint, I mean, like, if you're actively trading, if you're successful, you haven't blown out your account, right, you haven't lost tons and tons of money, you still have capital around, but you are able to increase your currency that you're trading with, to better your life or the life of others around you. That's essentially it. Right? That's, that's the, I would say, an ulterior motive of success is for people to figure out a way to help others with their success. Either teach it, provide it, give it away, donate it, expand it, offer it something. So if you're a trading and you're able to carve out metal sell something of the day, if you made $600 Extra, or you made a $600 profitable trade, so 600 US dollar increase when you do 200 days a year. That's $120,000. Extra. Like, that's bomba, that's the number three significant, so tour data of the year. So we're not even talking. This is a 24/7 365 job. There's like no 200. So you're doing it's a third of the week that you can sit in and look at a good trade every other three days you find a good setup, make 600 bucks like $120,000 extra year, that's magnificent change you have as life changing and up and people do have the opportunity grant all over the world to participate in crypto, because you can do with the euro, you can do it with the Japanese yen, the Australian dollar, the British pound, any type of currency can be transacted and use through crypto. So it's a really fantastic tool. Grant Do you have a favorite that you typically pursue or trade? Jerremy I do? Yeah. So one of my favorites. It is a theory a theory was one of my favorite Kryptos for sure. But also there's another one that's a little bit less known called Cosmos, which is, which is Adam and so Adam ATO himself spelled, Adam pays a 5% yield to kind of like, is it through Coinbase. So Coinbase is a publicly traded FDIC insured company at this point. And Coinbase allows traders to have a position and Adam and just receive 5%, just like it would in a bank well used to write well, you can just kind of leave your money in the bank and just get like a free 5% return on your interest or a CD or whatever. Pretty fantastic. And that's because it's a very volatile asset class and moves up and down a lot. And so when you're leaving your money on the exchange, the exchange of the broker is able to go back on their on their side, and trade and transact with it. Just like a bank will take your money entering transact with it's really cool. Grant With 5%. Really? Yeah. 5% ROI. Okay, so now you can take all your money out of your CDs where you're getting point 0.002%, right. Oh my gosh. That's crazy. That is That's amazing. So in terms of crypto platforms, crypto trading platforms, do you have I mean, you mentioned using Coinbase? Or what do you what do you use them? Jerremy Yep. So I can definitely give you a few, Coinbase is absolutely usable. It's really easy, super effective. I like it. A lot of traders like it. A lot of individuals use it, but it is more of a what I would refer to as like a longer term. For crypto, you don't want to actually try it. The fees are a little bit higher. It's a little more clunky. clunky in the sense of it doesn't have a lot of advanced features to use. So I do use additional advanced features and other cryptos trade platforms, ones called Coinbase Pro. So it's their upgraded version of their main platform. So you can actually trade you can set limits, you can set stops, you can do all the advanced features that you want and Then if you want to level up from Coinbase Pro, there's a platform called Kraken, K R A K E N. And all these exchanges are very easy to transfer the money back and forth. Kraken is working on an IPO right now, which is they're gonna come out in the market, hopefully in the next few months. So you have a lot of opportunities to anyone, right? This is here in the US. I mean, you can open up an account been an account and like four minutes, if you're bout technology, three minutes, if you're fast, it's really easy to use. It's super easy. You can download on the app store you can use on an iPhone. I mean, it's, it's available everywhere now. Grant That's awesome. What a great, I'll check out crack and I've used the Coinbase stuff. But it wasn't the Pro. And yeah, I came back going oh, clunky, slow. And I couldn't couldn't control my trades very much. And this isn't the Yeah, yeah, but it's good for longer term.  Jerremy Like you said, the CD thing that's kind of what I tell people is like, hey, just treat like, essentially treat like a CD. You know, look at it, play with it. Like, have a have a long term, just you're buying slowly over time. Right? You buy and just sits there you treat like a savings account. Just keep buying, don't touch it. Don't buy anything. Don't do anything weird with it, just slowly buy over time. Let it accumulate, treat it just like a savings account of money come out of your bank into Coinbase great way to use it put into Adam get a 5% yield. Good times. Grant Awesome strategy, talking about strategies. And of course, your training material you do awesome talk talking about different trading strategies, do the typical sort of S&P 500 trading strategies that lots of people talk about? Do they apply in the crypto world? Jerremy Great question. Not fully. I mean, depends on what you want. Obviously, we're talking about but so crypto doesn't have very fluid option trading right now. So options are essentially insurance contracts where they protect your downside, help you leverage your upside things that nature. They are offer, but it's really more for international individuals right now. So the US is getting on board eventually with that process. However, long term strategies like buy the dip, buy long term, like take some money and accumulate over every few months. Absolutely. That works like so long, the longer term strategies that can definitely be effective and certainly be useful to think about some other ones. Just try to think of other like really aggressive strategies. I mean, I personally don't trade Kryptos on a small term timeframe. So I say small term, I mean, like, on a 15 minute chart or less. So I don't day trade Kryptos. And here's why Kryptos can very easily move 10% a day. Like that's simple for a cryptocurrency to do which is wild. But as I mentioned earlier, that's why people are interested in them. I don't go less than 15 minute chart because I don't want to capture a 1% gain or 1% Return on something that could move 20% When 10% On Tesla or Advanced Micro Devices or Apple, I'll trade those on a daily basis, but they're not going to move 10% A day 99.99% of the time. So I'm looking for one to 2% pulled out the market, right capture that cash flow, but on on Kryptos. I am more of what's called a swing trader. So I'm usually in for three to six weeks is like kind of like my preferred timeframe. Grant Oh, awesome. Awesome. Okay, so what about alignment with the s&p, do you see anything there happening between crypto and s&p alignment? Jerremy Ah, that's a good question, man. Yes, I kind of do actually. I've been noticing or at least it seems to me that Kryptos actually kind of leading the stock market a little bit. And the stock market as of right now has been a little bit of a laggard or a little bit of a weaker position. Not overall but in general, or specifically. So to come up with an example Aetherium and Bitcoin are down about 40%. Pay Pal is down 70 squares down 65 Facebook is down 45. Right. Netflix is down 75% Boston Beer Company is down 80% You have some companies are down 85% like Roku. I mean, some individual companies names are getting absolutely decimated. But big coin in theory, we're only down 30%. It's like well, relatively speaking, that's not terrible. And that's actually started before that wasn't like early November. And most of the stocks, the bigger names at least big broader market, s&p, NASDAQ they started selling off late November. So I kind of see Kryptos as almost as almost a leading approach like if I see strength in crypto, I'll have ventually assumed literally strengthened the stock market, sometimes at the same day. And I think that's because people are going to, again, slowly peel some money into crypto, the total market cap of cryptocurrencies on a like, again, accumulative basis right now is almost 2 trillion, 2 trillion now really? I'm sure Yeah, but but that's not a lot, right. That's the market cap of Apple, Apple. Yeah, that's one Google. That's one apple. Like, that's nothing essentially. So if you take, I think, I think there was a stat that said, the overall retirement savings in all of us, like, if you take the US, you take every single dollar in retirement accounts right now and average them together. It's like 36 trillion, which is a lot. If we take 10% of that 3.6 trillion, and put it into the crypto markets, I mean, crypto market triple, essentially overnight. So that's the grand your larger term I get at actual sizable money starts flooding into these Kryptos, they likely will go a little bit higher. And so that's why I kind of think that the crypto market almost right now leads the stock market. Grant It just looks amazing in the fact that it hasn't pulled back on such high weakness, I was reviewing some of the various stocks just last night that you were mentioning, and I was looking on the monthly charts, the the massive pull backs, and I started thinking, this is starting to feel like buy time. You know, when it's down this far back, you're like, wait a minute, this is probably what what's your take on that? Jerremy You're not wrong, man, you're not wrong. I do. I think that the way, there's a very popular term, I don't don't try to catch a falling knife. But my my definition of a falling knife generally is if you are trying to catch it, right, don't try to catch it with both hands. So keep keep one of your hands alive. So throw a little bit of money at it don't get super super aggressive. Take a little sizeable chunk so let's let's say have 100,000 our portfolio and unit put some money in the Netflix throw five grand at it over a week or over a month I don't take five grand go okay. Oh man go by Netflix spread it out. Because right now Commission's are free generally, in 98% of trading platforms like your Commission's are free. So you can buy and sell by 500 bucks on Monday by 700 bucks next Tuesday by 800 bucks, the third Thursday of next month, and slowly tiptoe into some of these positions. So it kind of allows you to a lower your cost basis a little bit. But really, and also be adjust. It does, it allows you to kind of understand and see and accumulate without being fearful and upset and scared. Because most people they're used to buying something and once they buy it, that's the bottom right, that's the lowest price ever. So if you buy candy bars, like you're not really worried about the candy bar you buy a house is like very, very frequently, house price goes up, if you buy a car, they feel like the price isn't going down because they don't notice that. If you buy a stock, you're you're not gonna get in at the low, like it's gonna go lower than wherever you bought it always 99.99% of the time, you're never gonna press the button near the bottom. Yeah, it's not gonna happen. Grant So tough to find the find the low I love that counsel, the spread it out, distribute the risks, try to get control the cost basis or lowered as much as you can. That makes a ton of sense. Okay, so final comments on crypto for helping someone to get started. Any thoughts on that? Jerremy Um, to get started, so I do offer education on crypto. I know right before we talked on the webinar, you mentioned that you might dive into that program as well. It is a valuable program, it is extremely useful. I teach people a little bit about what I look for when I'm trading crypto and like what what patterns I'm looking for and how I get in and how I pyramid into a position. But the good news is, I mean, go to YouTube University, right, which is just youtube.com. And any questions you have typing a how to and then how to trade Bitcoin, how to trade and how to trade, you will find so much information because education shouldn't be free. Right? The information I give away in crypto isn't necessarily education is more like how I do it and my perspective, my opinions which are valuable because I do quite well in crypto trading. And so I want people to kind of understand and see the way that someone can approach it. So for me, yeah, just go study, like spend 10 hours over the next year. Hour month learning a little bit more about it. Because it's knowledge right applied knowledge is power. If you just sit there and say I don't have the time I don't have the money. I'm not smart enough. You're leaving a lot of games, both monetary financial emotional on the table and that's not what you want. Grant Yeah, I love that guidance, because one of the things that you've pointed out some of your trading area, your trading training that I went through and by Either way, Have I mentioned everyone yet? It's got good material? If not, I'll just say it's worth your time to study because the approach is very balanced. It's, you know, get some into some long term. And here's an approach on how you do some long term investing. And then, and then maybe there's some medium term stuff. And then okay, you're going to go after some cash flow stuff, which is where I do some of my day trading activities there. I think you have a great balance on that, Jerremy, and I think my audience will benefit from that as well. So it would be real life trading.com. And then on there, there's going to be some links, what links will they follow to get to the crypto stuff? Jerremy Yeah. So just go through all the education, there's if you click on trading, and you'll see courses, just click on courses and start growing and going through all the courses there's tons of, there's tons of them out there, there's a lot available, one of my favorite ones, which you're like a lot grant as you get to it is hedging with options. So the hedging with Options program is just about how to essentially create cash flow from stocks using options and how to protect yourself on the downside, especially if you want to be in a company long term. But yeah, the crypto program, just under courses, check it out. It's very valuable, it's extremely useful. And you will get really great insight just not only on the future, and what applications and what systems can be disrupted, right, with Blockchain technology, but also how you can take advantage of any individual here can take advantage of it without being a coder, or a weird nerd or programmer, if that's one of your fears. You don't have to know any of that, to take advantage of this market, or just be in a space, just like you, man, just like me, we have to study. We're dumb. Put yourself in front of educational platform, videos, books, YouTube, podcasts, whatever it is, and study, spend a bit of time. So you could apply knowledge because at this point, my friends as we go forward, if you have internet, you have income, you have access to income, and that is a global endeavor that can change the world for the better. That's a great statement. Grant If you have the internet, you have income. That's a good one. I got to put that on a plaque. Okay. All right, baby. Jerremy, thank you so much for joining me here today. I appreciate that. I know you made some adjustments to your schedule to make this possible. So thanks for doing that. Any last comments before we wrap? Jerremy You know, the best one is, it's never the perfect time to start anything. I think a lot of us and I'm 100% guilty of this everyone isn't mature should be. There's never a time you're like alright, march 15 of 2027. You know, I'm gonna sit down, I'm gonna take four hours, I'm gonna learn this stuff. Five years from now and I'm a little bit more wealthy when I'm a little bit richer, when whatever is happening. There's never a perfect time is going to be a little bit painful, it's gonna be a little bit scary is essentially irrelevant of what it is that you're doing or why you're doing it or how you're doing it. What I can say is this, if you start something, consistency is what pays off, consistent and intentional wins the race. And if you stay consistent if you stay intentional, if you really focus on your personal growth and your personal mental prosperity, absolute game changing life will become incredible. Grant I love that. Thanks. Thanks for taking the time to go over that. And for joining us here today on Financial Investing Radio. Thanks again for joining. And until next time, everyone go get some crypto from Jerremy Newsome. Thank you for joining Grant on Financial Investing Radio.  

In this episode, we take a look at the seven pillars to grow your wealth. Grant Everybody, welcome to another episode of Financial investing radio. My name is Grant Larsen. And today I have in the house, one of those unique people that understands some of the fascinating ways to build and protect your wealth. I'm excited to have with me here today, Seth Hicks. Welcome, Seth. Seth Thank you so much Grant, glad to be here. Grant So when you reached out to me, and you started to say, hey, could we talk I started to look into what it was you're doing. I mean, I'm hearing words like private banking and asset protection expert, you hear some of that stuff? And you think, Oh, wow, do I have to have an advanced degree, right in financial management to understand this stuff. But what occurred to me is that I've seen some of these principles before, they don't seem to be well known by most. And so what I'm excited about is the opportunity through this channel here for you to continue to get your voice out there and say, here's a way that you can build and protect yourself. So first of all, how did you get into this? Seth Well, I practice law for about 25 years now, and have structured transactions, commercial real estate transactions, business, acquisitions, and sales. And kind of help people keep what they make, so to speak. And when I met my now partner, Vance Lowe, the principle of private banking strategies, it floored me to find how easy it was to make a few changes, and effectively do 100%. better job. And so what I mean by that is private banking strategies, we use whole life insurance policies that are structured in a way to have a high cash value, and in the appropriate structure and appropriate jurisdiction. They're statutorily exempted and protect it, much like a homestead in certain states and many of the same state. So, for example, in the southern states, you've got a post Civil War air legislation where... Grant It goes that far back post Civil War? All right, absolutely. Seth Yeah. So private banking goes back as far as Civil War era. And even before that precedes branch banking, it precedes the the type of current culture banking that we have. And the post Civil War era statutes protected their their citizens, the state citizens from Northern carpetbagging. So for example, yeah, for so for, like example, in Texas, and Oklahoma and Florida. And a lot of those states, south of the Mason Dixon Line, you have laws that protect homesteads. So in the event that there's a liability, and someone has a homestead that they've declared, it is 100% protected from being taken from them. And that was a product of the Civil War. Grant So let me ask you this, when you talk about how you know protection from having it taken, I'm assuming you're talking about scenarios like maybe bankruptcy scenario, or something else where you owe other people but you've got this protective layer that no one could actually come in and take that foundation from you. Is that right? Seth That's right. A lot of our clients, you know, higher net worth, some of some are ultra high net worth, and many are blue collar, but they have created strategies to keep what they make. I mean, no one wants to effectively work hard to earn money and then and then lose it. So those type of folks who gravitate towards structures where they're able to keep what they make, so for example, if you've got a homestead You're in Texas or in Florida, and you want to use it as a vault, and you don't have any debt on it and you're able to pay the property taxes, year after year, then it is 100% exempted from creditors or from outside taking. Grant So that's an important baseline is that it does need to be debt free, you have to have no mortgage on that or any liens against that. That'd be right. Seth Sure, yeah, you've got to if you've got a, you know, a loan with a traditional bank, they have a right to the mortgage payments or, and so they will effectively if not paid, foreclose on that, and those rights are obviously superior. But if you're if you're in a position where you're able to, for example, use your own private bank, through the cash value in your own policies, and purchase and acquire your home, or other assets through that entity, you would do the same structure, you mean, obviously, your bank and part of the cycle is getting the money back. And that's something that the Vance prides himself on his teaching people how to get the money back, you've probably heard some of that, and your private banking, that's one of the reasons that people do it, they effectively take the banking equation back into their own law into their own become the bank. Grant So as the flow is something like this, you get one of these Whole Life policies, it takes some time for you to build up some cash value, but then that cash value becomes something you can leverage and use for either purchasing other assets or leveraging it and other investments, so to speak. And that has some protection wrapped around it, is that what you're describing? Seth That's exactly what I'm describing. And like I said, a lot of our clients are higher net worth or even ultra high net worth. And when they capitalize their bank, they are, they're able to do a lot more with it right out of the gate. But for the blue collar guy, you're right, it's a, it's a steady increase that you use. A lot of folks use this as a retirement strategy, because the ins and outs are not a taxable event. And if any of the audience wants to dig on that it's internal revenue code 7702. And what that basically outlines is that your whole life policies, your your cash in and your cash out, are not taxable events. So compare that with like an IRA or a 401 K, that someone's been socking money into. When you take those distributions. Well, if you take them too soon, you're penalized you penalized if you take them too late, you're penalized. Yeah, and it would take them right in that the right time. You're still paying taxes, I'm still paying taxes on it.  Grant So every single cash transaction on the cash value, no tax, no taxation on that, right. That's, that's amazing. How blue collar person or someone that's not old truck, how do they get started then Is it is it I hate to say as simple as but Is it as simple as getting started with your whole life policy earlier in your life than later? So you can begin building out that cash value is is that the number one thing are what else would you do? Seth You know, I wouldn't say age is the number one determined to factor. In fact, we've got an article and a podcast that we've produced that says, you know, you're never too old to start private banking. And here's why. And we go through the outlines the benefits and values, which include asset protection, tax free growth, financial privacy, no taxation on the legacy value. So if you're leaving high value to heirs and benefits, beneficiaries, don't pay any taxes on that transaction, even if it's ultra high. So there's some value there, depending on what your primary motivations and focus are. And the age of course, if you start earlier, you're going to accrue a much greater and higher value as you you know, as you go year after year, but let me give you an example. We've got one of our favorite clients is as a woman in Texas, who was a single mom, and she started out with a $5,000 annual whole life policy and she made she made that contribution for a few years and and then use that cash value to as a downpayment into an investment property. Oh really? So she purchased this investment property as and then she also had third party financing of course, she began to develop cash flow from that and she paid her bank, her private bank back and as that cash value increased in a crate increased, she did The exact same thing, she rinsed and repeated the process with the second investment property. And now she has a million dollar equity portfolio in real estate from where she started at $5,000 leverage. Now, we've been, you know, she's had the benefit of an appreciating real estate market, she's had good investments, but it illustrates the principle that you can actually start in that small of an amount and and multiply that seed into something that really brings a large harvest. Grant That's fascinating. One of the things I noticed from you was, I think you call it the Seven Pillars of private banking strategies. Can you speak to that for a moment? What are those? Seth Sure, the first, the first pillar we've been talking about is asset protection. And the second pillar is tax free growth, which is we also referenced that compare that to a 401 K, or an IRA, you may have tax free growth inside, but you're going to pay taxes when it comes out. And we've got some illustrations that kind of compare those two things and show you you know, which comes out ahead, and it may look like a contributions from an employer and other matching proceeds will come out ahead. But in overtime, they really don't. So you've with inside the policy, you've got compounding growth, and you've got a tax free growth. And you've got a financial privacy. third pillar is financial privacy. Whereas compare that to a bank, for example, who has to KYC know their customer, know your customer, they want to understand, you know, every aspect of money in and money out, you going to try to take out or put in a large cash, for example, a 510 $1,000 Cash, I'm into your Wells Fargo or Bank of America account. And they want to, you know, cross examine you on 50 questions about why you're using cash, where, you know, that doesn't happen in a private contract with the life insurance companies, we use it, it's totally private, and they don't raise their hand and go, Hey, there's a large transaction in or out, and they're not required to by the IRS Code 7702 Grant And it's just not part of their business model, right? Seth It's not part of their business model. No. And so it's interesting to point out this is kind of a little sidebar, but the largest players are the largest clients of the life insurance companies, or the centralized banks, like Wells Fargo and Bank of America. I think the last time I looked at Wells Fargo has a 20 plus billion dollar annual premium for life insurance policies that they hold on employees and, and others. So if, you know, gives you some insight. Grant That's huge. Okay, so right, so asset protection, tax free growth. Seth Tax free growth, financial privacy, privacy, the big one is velocity of money. And once philosophy of money, we describe that a little bit and in the the example that I gave our audience with the woman who started with a $5,000 premium, and then when she had enough to make a down payment on an investment property, she did so and so she she paid a premium dollar into the whole life policy, she borrowed that same dollar out to make a downpayment, she purchased a piece of real estate with that dollar, she got a rental dollar back from the tenant, and she paid her bank back on the note and deed of trust. And that's the velocity of money. It's the multiple touches within your own economy of the same dollar. And I mean, I'm simplifying it there with $1 but that's effectively the transaction. Grant Now that like you said earlier, it's the rinse and repeat principle right meaning absolutely cut it out. She's liquidated it used it acquired some capital back repaid herself and now she's she's reset to do again, right? That's absolutely. When every Seth When every dollar that she pays back into her bank, Grant, it increases the cash value, dollar for dollar. So you've got that that loan from your bank coming out. And when you recycle that rental cash flow back in or that business cash flow, or that cryptocurrency sell, or whatever your investment might be back into your bank, your cash value goes right back up to whatever you've put in. And so you and I both know that banks they make money by lending money. So Wells Fargo with and Bank of America orca Chase and these large centralized banks, they put their money to work by making good loans. They make loans that are secured, they make loans that are collateralized. And they, ultimately they want that cash flow with an interest rate. Well, it's the same principle with your own private bank. And you want to make a good loan to the borrower, whether it's your business, whether it's your brother, whether it's whatever a third party, you want to make a good loan, make sure it's collateralized and secure in the chief got an investment, cash flow, and an ROI on that loan coming back to your bank. And there's that cash flow increases again, you do the same thing. So you begin to think like a banker, you think like a banker? Grant Yeah. Because that's so liberating, right to people to be able to be on that side of the table. Right? making those choices. Alright, and then what's the fifth? So there were seven? So I was four. What's the fifth one? Yeah, I'm looking at the seven pillars. Seth So guaranteed financing. Yeah, it financing. So let's say that you're that you've you've you've done like our our hypo example with a woman there. And she's gone through a number of years, but she only started with 5000. Remember, now let's say that she's got 100,000, in total cash value. And she's in a state like Texas, where you can buy an investment property for 100,000. Or she could lever into multiple properties on like an 8020, split, for example, you know, she could buy five properties with 20%, down and put 20,000 down on five properties that cost $100,000, financed the other 80%. And she's building cash flow on all five of those, and actually getting a much higher ROI. And in that example, what you what she would be doing was effectively using leverage to increase the ability to invest in multiple assets. And when her cash value stacks up high enough, she could take out the third party lenders, or she could continue to use that strategy of leverage. And that really depends on someone's their own risk tolerance, their own investment strategy, some folks, they you know, that they're going to eliminate those third party loans. And they're going to take that cash value and just totally take out the third party debt. And so the only debt that would remain on that particular real estate asset would be their, their own private bank. So the guaranteed financing part means you don't go to the bank, and you don't have to qualify, you don't have to go through any type of you know, yeah, because you're the bank. Yeah, you're the bank. Yeah. So you make sure you look that guy in the mirror, and you make sure that you're making a good loan on a good asset. And you do that. So but I described the principle of leverage, because a lot of times people get ahead on that concept of leverage, as opposed to just buying one property for $100,000. And let's say you're making 2000 a month, you got 24,000 in gross cash flow, versus, you know, if you spread that across five properties, and you got 24,000 times five life and cash flow, so you know, and you're able to just knock those debts out a lot faster. That's the velocity of money and guaranteed financing working together. Yeah. Grant And written replenishments faster. Okay. All right, number six, and seven, what are those on your seven pillars? Seth So guaranteed compounding it tax free growth is the part inside your policy that that cash value and your premium dollars, they are compounding inside the policy annually, and there's no taxable event. And so I think it was Einstein who said the, you know, the compounding interest is the eighth wonder of the world or something along that line. And if you're not, you're not getting compounding interest, then you're making a mistake. So you don't get compounding interest in your centralized banks. You don't get compounding interest in various other investments or formats. But in this these policies you do. So that's, that's something that is very distinguishable and it also takes out the market risk with your policies and the values in there, you're not subject to market risk. So this is not universal life. This is not indexed. Universal Life or any type of risk transfer. To the the owner of the policy or to us, you're not taking on market risk. But in those types of policies Universal Life or index, Universal Life, ual  Grant You, you are taking on market risk and one of the things? That's right, so being in control of the risk, right, that's absolutely mental aspect. Seth Absolutely, if you're going to use your cash value and put it to work and investment, you should be the one that's able to identify that risk and not have it subject to equity market risk. So it never goes backwards, you're going to only see a steady prodding forward with this compounding growth. And after a certain number of years, it starts to go more parabolic. And that's, that's really the beauty of this. And the magic of it. Some folks, they they locked this stuff up for retirement strategy. And you know, some are using it for the leverage. Grant Yeah, you know, it's interesting, I've seen some financial people describe that risk control paradigm with a with a pyramid, right, and they'll describe it, you know, in the, in the manner that you want to have more control. So you start, you start, you should start these sorts of strategies first and get that established. And then and then over time, as you go up the pyramid, you have less control over it higher risk, potentially higher returns, but that might be where you're doing some you're, you know, trading or investing or self directed activities. And a lot of people invert that pyramid, right, that's a well, they'll start with that self directed trading or investing. It's, you know, high risk, low control, and then blow out what capital they have, when instead, turn that the other way around, start with these foundational approaches that you're describing, and then build on top of that. Does that make any sense? Seth Amen, absolutely does. Sometimes will, will describe that as, you know, Hare and tortoise paradigm. And some people go, Well, this isn't, you know, I can make this much here. And I make 12% Over here, I can make 15%. Well, no, you really can't over 30 years, and likely there's going to be a risk factor there that may blow you out. Totally. Grant Yeah. And the loss of control that absolutely, yeah. Now. Yeah. Seth I mean, you've got this third party risk, whenever you've got, you know, a transfer of your money to someone else. That's, you know, you've got that risk that counterparty risk, whereas this, these insurance companies, they don't fail. I mean, they've been paying dividends, since before the Civil War, year after year, through the Great Depression through the Civil War through every economic upturn and downturn that there is. And it's, it's just one of the reasons why grant is because there's a cash reserve requirement of one to one, as opposed to a cash reserve requirement at a Wells Fargo of maybe 10% or less. Yeah, so they take they take $1 In deposit, and they're able to lend out 10, or perhaps even 50, depending on what their total asset bases and that's, that's funny math. You just print money out of thin air, and then they're able to loan the printed money at an interest rate, and they're making money on something they never even received a receipt. Grant Fascinating, right? The before I ever heard about this approach of this technique, one, I have to tell you my origin story of learning about this for the first time, it was my wife was driving our minivan. It was when our kids were little. And she was backing out of the garage and kids were bouncing around everywhere. And you know, I would have made the same mistake, but she wasn't watching. And she was turning around and talking to the kids. Hey, kids sit down, she backs out and just wax the mirror off of the side of that house right on the minivan. And so you know, I come home from work. She's like, many of the mirrors hanging off the side. So I look at it go well, it was a really old minivan, really old minivan. And I was like, Well, okay, let me go get it fixed. And so I took it over to the dealer. And I had this thought goes through my mind. And the thought was wait, rather than because at the time, I think auto loans were going to like 4% or 5% or something like that. And at the time, our house had been paid off, but I decided to take out a home equity loan to do some fix ups on the home and it was running. The interest rate at that time was like half a percent on this home equity loan. And so I'm in there They're looking at getting the car fixed. And I'm going to dealer and all sudden I go, let me go look at the floor, showroom, and I walked over, you know, I pull out my home equity checkbook, and I just pay for it right there, boom, and I get this car course still today it's a joke if dad goes to fix the mirror comes home with the new car. So I come back with the, with this car. And oh, by the way, I'm driving back thinking, I'm a banker, man, I just, I'm a banker, I just, I just floated this thing myself, and got home. And of course, guy, you know, paid that off at a much less interest rate. A few years after that. I heard this principle you're talking about you've been discussing here. And it clicked, I went, wait, wait, that's kind of what I did. Right. But it wasn't using a whole life. But the whole principle is, let's put the people in charge. Right? Not not some other policy or program that larger organizations are bestowing upon you but rather put us the people in the driver's seat, so to speak, and be able to make those decisions themselves. And I think that that's really liberating.  Seth That's absolutely, yeah, that's absolutely right. And that that's exactly the same principle is you're you're taking back the banking equation, you're becoming you're operating a private family bank that has generational value, and and has you where you are able to touch the same dollars that you make multiple times like we described in one of our examples and and you're that velocity, really accelerate your wealth curve. And without the taxation issues. And without the the asset protection risk, you're able to transfer assets generation to generation and take a whole nother opens up a whole nother doorway. So that brings us to our seventh pillar, which is legacy value, and the tax free transfer of these policies and the death benefits to the next generation or Asian officials. Wow. Yeah, tax free. So think about this, for example, there's a guy who most people know named Prince, and the or the artist, formerly known as Prince, he was a pop rock, yeah, seeing are pretty pretty well known. And he died not too long ago with an estate value of about $200 million. And he was a resident of Minnesota, ironically, and he had no private banking structure in place, he had really no estate tax planning structures in place. And between the federal government and the state of Minnesota, they took over $100 million of that 200 million, and in taxation and estate taxes, and his beneficiaries and heirs, you know, are left holding the short end of the stick, that none of that would have occurred with proper planning, or that same money in a private banking situation. And then, I've heard, I was reading some articles on Suze Orman who's a supposedly financial guru. And she talks about private banking on occasion, and she, she really has no concept of what it really does. And in this interview article with the guy from New York Times, she says, You know, I'm so worried or concerned about my, my partner, being left with less than half of my estate. And I think at the time of the article, she worked about 65 million. And so her partner, she said, is going to, you know, have have to, you know, take 30 million or whatever, instead of 35. And she didn't know how to overcome that problem. And I thought, this is really unbelievable, in the sense that it's such an easy solution. And we kind of we talked about this kind of off off recording about it's literally the stroke of a pen that you can accomplish these values and these benefits the Seven Pillars without having to be, you know, a black belt. And in any particular one one realm. Grant Financial genius, you just have to know that that's available that it's there. Absolutely. Seth Yeah. So you enter the policies, you fund your policies, you keep funding your policies, and you enjoy the these benefits. It's really not rocket science. It's more just of learning that it's there. And it it it blew my mind. It was an epiphany to me. Yeah, having practice law for decades and then and then seeing this was available. I thought it can be that easy. It can't be that easy to with the stroke of a pen to protect assets, but it is I mean, it's it's codified law and these contracts grant or it's worth mentioning that there they are regulated state by state. So each state has their own statutes that govern the the law, the protection, you're gonna need to protect it right.  Grant So some states better than others are worse, right? Seth Absolutely. And it's, it's kind of like the post Civil War era statutes in southern states. They protect their citizens, life insurance policies, they protect their citizens homesteads many times in comparison to other northern states or western states. So it is, Grant wow, that's huge. Okay, so, all right, I've really enjoyed the conversation, if you were to point people to a place to go to learn more about this, Seth, where you're going to point him to? Seth It's really easy, you go to our website, https://privatebankingstrategies.com, that's https://privatebankingstrategies.com. And there in you're going to find a an offer. And you can read a book that we wrote that that tells you about secrets that banks don't want you to know, effectively. And I like to call it a red pill book. And it spots issues that people may or may not be aware of. And it's it's amazes me, how many folks don't really understand what the banking folks are doing to them. You know, and with regards to mortgage rates, with regards to all sorts of issues, you just so this red pill book is something that pops up there for you. And you've put your contact information, your name and your email, and, and you can listen to the book on audio, or you can take it in a written form. And that's really the where we start. On our website, Grant, we've got a pretty wide volume of resources from podcasts that dive into particular pillars, or how to how the banking operates, to blog articles, and then our emails that will come to you also address certain issues like the Dodd Frank Act, and what how why does that matter to you? Are, are your are your, you know, is your cash safe? And and it's centralized bank, why or why not? You know, our, there's simple things that you can do to protect yourself. So we try to add value. And those emails that come out to folks, we try to help them make a decision that this is, you know, for them or not for them. And it's really that simple. So you just hit the website, private banking strategies.com. You can have the book for free, all the podcast, all the emails for free. And if those things resonate with you, then you can schedule an exploratory call with Vance and start to get into the nitty gritty of it into what it means.  Grant Wow, Seth, thank you so much for taking the time here today with us and with our audience here. Very enlightening. It feels like we're popping out of the matrix right with with red pill. I love the analogy. Thanks again for joining and for going over this today. Everyone. Take a look at what it is that Seth is talking about https://privatebankingstrategies.com Thanks again for joining in everybody and until next time, become your own private banker. Seth Thank you, Grant. Thank you for joining Grant on Financial Investing Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.

Grant Welcome everybody. In this episode I speak with Jerremy Newsom who has a system for giving back Everybody welcome to another episode of Financial investing radio. All right today we have in the house. Mr. Jerremy Alexander Newsome coming to us all the way from Nashville, Tennessee. He is a long term successful investor and trader and he's been gracious enough to spend some time with me today, talking about some of the unique things around what he has done and how he's benefiting the market and investors and traders. So first of all, welcome Jerremy. And please take a moment to introduce yourself. Jerremy Thank you so much, man. It's always an honor to pour into like minded individuals I love that everyone here is interested in investing and growing their minds and therefore growing their bank account. My name is Jerremy Newsome. It's spelled with two R's. My dad's name was Jerry. So it stands for Jerry and me. That was Oh, that's awesome. Thanks, Mom. Yeah, man, I love the stock market. I love trading. I do love investing, right? Everything from long term buy and hold to day trading options and everything in between. I think that the liquid markets are extremely fascinating. And one of the things that I spend the majority of my time doing is helping other people learn how to do it themselves, right how to generate cash flow, using the markets, so that they can live the life that they want to live and help me people they want to help. Grant I love that. So one of the things that triggered me to want to be able to talk with you is when when I had the opportunity to interview I was looking at something about you in your background that sort of caught my eye in terms of what you started with. I'm not gonna give it away but would you back up and just talked about how this all got started? How did what got you thinking Let me do trading. Let me do investing. What was it that can get us that sort of the beginning? Jerremy Yeah, dude. The craziest thing it was a movie, a Tom Hanks film 1994 Academy Award winning film Forrest Gump. And I was watching that at six years old. And I was watching with my dad and my oldest brother, Jerry, Roger. And by apresenta way through the movie, Forrest Gump says that him and Lieutenant Dan invested into a fruit company and they no longer had to worry about money anymore. So I asked my dad, I was like, what is the fruit company was investing because when I mean we were poor, financially, we were very happy family had a great incredible childhood. But we were very poor. We we were at the time living in a single wide trailer tree house. Doesn't. It's not nearly as glamorous as it might sound. What I mean, okay, a tree house. A bunch of single wide trailers thrown into a tree house. Yeah, really? Grant Wow. That's a story right there. Jerremy That was a story. My dad was a creative builder. And I lived in there for from age three until 12. And so anyway, we're watching this film. I connect with that because I was like, Well, I would love to not worry about money anymore. I know a lot of my family members or somebody that we stressed about and worry about. I would like to not have that be an issue. When I asked my dad what's investing what's the fruit company? My dad goes on to tell me about Apple and how the stock market works I was like let's do this the Forrest Gump investing model Dad Come on, let's do it. And you know, he did the normal dad routine, right? Like yeah, this is a movie. Yep. It's but I asked my dad was like, Is Apple a real company? Can you actually invest in it? I begged him and begged and begged him and he said, Listen, if you bring me some money, I'll match it dollar for dollar and we'll buy some shares of Apple. So I went and picked blackberries, door to door and made 1500 bucks in the summer of 1994 some blackberries. And so true to his word. We bought $3,000 worth of Apple in 1994. Grant Man you bootstrapped it. I loved your initiative. Uh, you know, the fact that you really wanted it you went after sold blackberries, right? Like, we just want to pick them out of gardens kind of thing sold and door to door. Jerremy Yeah, yeah, just exactly. So they grow wild in South Georgia. And so I just walk around up and down the streets and find BlackBerry pouches and put them in plastic bags or buckets or whatever. I could find them and just wrap them up and then go to go to Door Door and sell them. And the thing is, though, I sold them $1 bag, organic handpicked blackberries. 35% cheaper than they sold stores and had a six year old no shoes, no shirt jean shorts at a high closing rate. Yeah, grandma's would give me 10-20 bucks. You know, I think I was cutest thing in the world. And that was it. Grant You know your audience? That's awesome. Yeah. But you know, your dad's brilliant to where he, he saw that. Hey, you know what, I'll support you in this. But I also want you to take some ownership as well. I think that's a great, great learning, right for your kids. Jerremy I love that you brought that up, man. You're 100% Right. And I'm assuming that you're a father. I can probably I can make... Grant Six kids six grandkids actually two more grandkids, almost eight grandkids. Yes. Big family. Jerremy Good for you, man. Good for you. That's incredible. And yeah, so you're absolutely right. It was an ownership thing where it's like, Listen, you want this bad enough? Prove me how bad you want it? And I did. And then he Yeah, he was true to his word. Grant Big time. I love that. And I know, I know, you've mentioned this before we got started. But I feel like this is this is the right time to bring this up. You just released something for kids phone is that? Jerremy Ah, yeah. No, thank you, man. We talked about that just a few minutes ago. You're absolutely right, I have a brand new book that just hit Amazon. That's the only place you can buy right now. I'm sure it'll be in bookstores eventually. But it's called a stock market journey, how to make sure your kids win every day in real life. And so if you just type in stock market journey into into Amazon, you'll see a pop up and a man. So that's it's a stock market education book for kids, which is very rare. And it's more or less like it doesn't exist. I was told by a person forever ago, like a business mentor. Don't do that. Don't waste your time. There's no demand, like kids aren't trading stock market. Like why teaching? Yeah, I was like, Well, I want to know. I mean, there's someone else wants to know. Grant Yeah, you're absolutely right. Absolutely. Yes. Yes. Some of my own kids have done that as well. So I love the fact that you created that book. Okay, so So backing up here. So you have this experience you you got the got the edge, got the bug, committed your time, got the apple investment going. Alright, you've got this sentence in your headshot piece, who shared to me I love it says retiring on $3,000 how to grow a modest investment into a comfortable retirement. But that tells me as you're thinking about us as people in general, right, you're not looking at Gee, it's gonna take lots of money to pull this off. If you do it right. You can bring you can lower that bar to get going with this. Can you talk about this? Jerremy Yeah, so essentially, I teach people how to leverage their funds to do day trading, and to be in a position where they're comfortable, where they're not risking insane, insane amounts of money. Because you can leverage money very easily, right? Money is the easiest asset to come by. Now, most people don't think that's accurate, especially if you're not extremely wealthy. But it is like like, if you buy a house, you didn't have to come up with all that money. You went ask the bank and the bank gave the money. And so now you're paying the bank back plus interest, but you are living in a house and 100 or 200 or $3,000 that you didn't have to come up with up front, you got the money, right? That's a resourcefulness going out and getting a bank. But there's other ways to get houses, owner financing, asking your parents, whatever, right there's a million ways to buy a house. So for me when it comes to actively trading the market, most people don't know that leveraged funds exist. And when they hear leveraged funds, like I'm about to lose all of my money. There are mathematical formulas. Second grade math, I'm not very smart at math grant, I'm like a, my IQ is a high 93. And I use second grade math over and over and over to ensure that I never lose more than 1% of my account on any trade. So for me, you can take $3,000 And you can actually get access to $100,000 in day trading, buying power right using the leverage funds and only lose 30 bucks per trade and 30 bucks per trade is 1% of $3,000 Even though you have leveraged $100,000 available so it's it's incredible what's available out there and I wrote a book also called money grows on trees, teaching people these concepts and letting people know that this does exist items and processes And then, you know, companies out there know how to use their money and how to leverage it and give it away for people who might want to grow into a substantial sum of money. Grant  So that's interesting that, you know, you identified putting that risk management in place upfront that that seems to be sort of the common theme across a lot of traders that are real successful and having the discipline to say, You know what, I'm not gonna go past that $30, and I'm gonna diversify this, is that the basic strategy across a lot of different trading positions in different markets? Or how do you approach it? Jerremy You're not incorrect. That's absolutely one way. Another way that I do it is you create the calculation ahead of time, so you have your 1% of your portfolio. And for me, I'm taking this from a active trading perspective. So I say active trading, I'm a full time legit, professional stock trader, and I'm in front of the computer, trading maybe 10 hours a week, maybe during the time I'm studying, I'm reading I'm listening. I'm doing podcasts I'm, I'm talking people much smarter and much wiser than me like you asking them, Hey, what are the macro economic challenges are going to face America because of Ukraine or because of COVID. I'm asking other people things that's using them some spending, most of my time doing is listening to veterans, people have been doing this for 20 3040 years, getting their their knowledge and their expertise. So the math, for example, is let's say you have a $3,000 investment. 1% of $3,000 is $30. Well, the math behind the risk is if you buy Apple at 180. And you're like, Okay, if Apple goes down to 179. Get me out. So you're risking $1? Well, if you know your risk is 3030 divided by that dollar equals you can buy 30 shares of Apple. So even though your investment in this scenario is well over $4,000, you're only going to lose 30. If it doesn't work. Now, let's say Apple goes from 180 to 190, you make $10 per share risking $1, right you your risk reward is 10 to one you just made $300, even though you only risked 30, that 300 bucks. So it could be cash flow grants could be huge for somebody, right? That could be a car payment, that could be an electricity bill, that could be all forms of items that they might need. That could be groceries, and that could happen in an hour. Right? Like that tray could happen in an hour and or 15 minutes sometimes. And it's amazing how quickly the markets can move. Grant Yeah, it is amazing. And with that sort of control risk, and that potential for upside being much more than what the risk is you're taking on. So you're leveraging options, It would be my guess to do something like this, is that right? Jerremy You absolutely can leverage options. And I do and I know how I'm very versed in options. But I do generally 99.9% of the time instruct most of my clients to start with stock because it's easier. And stock margin and startup stock leverage is super, super available to anyone who wants it, essentially. And yeah, it's amazing what's available out there. Grant That is amazing how long then you typically in positions as you help people do this. So so your your coach student would be one that would come in take your system, they're in front of the computer, they're leveraging their their $3,000. And they might be in a position for like you said 15 minutes or an hour. It's certainly shorter timeframes, right? Not they're not holding anything overnight, or do they? Jerremy Very good question. Right. So the leveraged accounts to leverage trading, this is a day trade system. If you're going to hold overnight, I almost never suggest using leverage, though. If you have $3,000 to your name, like you're going to put $3,000 in you're only going to invest, right what you can afford to lose if you're going to hold for long periods of time. And so yeah, absolutely, man, this is a day trade system. But I do I love long term investing love long term. It is an incredible way for wealth generation and for asset appreciation. But I think day trading is really cool for cash flow, right? Because investing is like Well, it's great, but now I'm 65 What now? Well, what if you're 33? Or what if you're 29? Or what if you're 48 and you got a few $100 or 10s of 1000s or a few 1000 And you're like well I would like to make $400 Next week though Can I do that? I was like yeah, that's that's day trading there are systems available that can do this. Grant That's That's fascinating. So so with the the talked about the difference between fear and greed, those doggone emotions, alright, what's the secret? sauce here. Jerremy, how do you control those? Jerremy Awesome question, man. So good. I mean, it's, it is controlled to an extent, but it's also a lot of self knowledge and introspection. Because, for me, I don't believe that most market participants are greedy. Now I know that's a very, very popular term, right? I mean, Warren Buffett says buy when others are fearful and sell and they're greedy. And so on and so forth. There's a lot of the markets are controlled by fear and greed. There's tons of fear and greed baked into the system. But here's the challenge with that is inherently, the majority of market participants don't want to be greedy, right? They're God fearing church, attending people that are 57 with, you know, a few kids maybe a grandchild or two on the way, and they want to give to charities, and they want to donate and they want to help build orphanages and, and hospitals. They want to donate money to the zoo and protect pandas. Like, if you give money away, Grant, by definition, you're not greedy. Mm hmm. Yeah, I mean, I'm sorry. But that's like just the exact opposite of greed. You can't give money away and be greedy. It's impossible. So therefore, what we do is if people tell themselves, I'm greedy, and greedy and greedy, they're not going to want to make any more money. Because nobody wants to be greedy. So we have to remove these really weird, crazy subconscious beliefs that we have internally about money and about growth. I call it financial optimism, or monetary zealousness rather than greed. Yeah. Or money. Grant What about on the other side? Jerremy, what are your thoughts there around fear the management of that? Jerremy So fear? I mean, that doesn't go away. And fear comes down to having more and more certainty. That is a piece of it. So you do not have education? Thankfully, there is a company out there that teaches you for free, which is my company, I don't charge anything for these systems, or this education for this knowledge is I just gave it away. Grant I mean, you don't charge? That's amazing. You don't? Jerremy Yeah, no, it's totally free. Because I want people to be able to experience it, and to understand it, and to learn it, so that they can really figure out they want to do it or not, or to what level because if I'm being honest, I've made a lot of money, millions of dollars in the markets. I'm good. You know, I don't need anyone's money. Specifically, I love money, I'm not pushing it away, I'll absolutely charge you for things. But education is not one of them. I feel like I feel like education should be free. And so I create a free platform, people can learn my system entirely for free. And the piece of the real massive distinction that you're giving about fear is it is a knowledge thing. A lot of people are afraid of I'm just gonna use guns as an example. Okay, well, I grew up around guns, I don't have many, but I'm very comfortable knowing that if I hold the gun, how it works. Yeah, and I know how to put a safety on, I know that it by itself isn't harmful. Just like a car by itself is a harmful you put a drunk driver behind that vehicle. Now it's a weapon. So weapons aren't. Money is a tool, right? Money is a it's like a brick can be thrown through a window or it can build a church. Same thing with guns into the car are all tools. And once you understand more, you have more certainty, you have more education, that fear will slowly start to dissipate. And it won't go away entirely forever. But you will have and you'll replace your replace that with different fears over time. Grant Oh, that's awesome. I love the altruistic aspect of this. It's sort of a gift back and feels like what you're doing right to the community. Right. Okay. Let me let me improve the education of people. I actually think that Well, let's talk to the other side of that. Then, with that giving back can you speak to what are the outcomes been to your audience and the people that have gone through the systems? Can you speak of successes and things where people have had their lives changed? Jerremy Yeah, man. I mean, the first one that came to mind is a really good friend of mine. His name is Matt DeLong. He's actually my business partner, mentor of mine, when my best friends he's a little bit older than me. 45 years old, has two kids. They're both in college and he was a business owner. A little bit of money, started day trading and losing his face. I mean, he was hemorrhaging money, because he had no idea what to do. Just just losing money all you click the button, boop. He's gone by because there was a siphon. And he didn't know what to do. And so he pumped the brakes. He said he sought out assistance, a coach and mentor and he met with me and pay me for my time said hey, man, I'll buy some food or whatever. Let's let me Cafe this out. So come full circle. That was four years ago, four and a half, five years ago, he now can trade profitably consistently and have an income. And now that his kids are out the house, he is fully financially sustainable, where he doesn't have to draw off of his retirement or he, you know, he does not working right, he sold his business. Him and his wife are able to foster children well, and to be a real force for good by going on mission trips, and by donating to charities and him and I ended up creating a foundation together called Real Life Foundation. And so one of our main goals is to end human trafficking. And so we donate hundreds of 1000s of dollars towards that foundation, and we just we work on Yeah, man. Yeah. So he has time to pour into the world using his gifts and his skills tests with your technology, because he doesn't have to be working 70 hours a week anymore behind the desk, which that was that was the case. That's what he's doing years ago. Grant Yeah. So it's interesting. You mentioned what's called Real Life Foundation. Yep. It sounds similar to as it Oh, you are like operation Underground Railroad? Jerremy Yeah. Yeah. So that's, that's our main as who we support as one of our main go to www.reallifefoundation.org . Like we do the in human trafficking by connecting directly with Tim Ballard. Grant Yeah, that's awesome. Wow, Jerremy, that's, that's really, really cool. Okay, so you're going after leveraging the benefits from this investing and trading strategies, you're tying it back into, I see two huge givebacks from you guys, man, obviously, the free education but going after that human trafficking problem. That's what's our massive problems to pursue? Oh my gosh, that's, that's incredible. Okay, so, so real quick question. You mostly do this in stocks? Is that right? Are you doing things like futures and stuff like that? Jerremy Very valid question. I have traded futures. I probably do four or five futures trades a month. Nothing too ridiculous. I do trade cryptocurrencies. I know how to trade foreign currencies. I know how to trade bonds and how to trade commodities. But I mostly trade stocks and options. I would say crypto but it's almost identical. They move very, very, very, very similar. And for me, I tell all the people like It's like Lebron James. Right? He plays basketball. Really? Well. Yeah. I don't know about his football game, or his golf or his baseball, but he's not making money doing those things. So I picked a career path that I understood, I focused on that career path exclusively. Grant With stocks and you doing more and more with crypto. And that's such a popular instrument nowadays. Do you do much there then? Jerremy I do. I do for numerous reasons. But one of them specifically being that people can do with any amount of money from anywhere in the world. And it really does open a lot of doors to let's say underserved countries that are they're still developing these developing nations, you can give anyone their $10 or let them earn 10 $20. And they can trade crypto and might make $1 a week. But in that world, or that country, that location, that could be a huge return for them. And those opportunities exists. And that's something that I think the crypto markets have allowed to, to really pour pour into other people. Grant So for people that are listening to this podcast that are brand new to trading, what tip would you give them to get started? Jerremy Most people are brand new, that's the good news. You don't meet many full time day traders who actually make money it's it is a little bit of a rare career path, but it is actually a legit career. And if you are new to it, number one trade companies that you know, know exactly how they make money. So don't trade like random biotech, pharmaceutical companies traded Apple trade, Google trade, Tesla trading normal companies that you know, how they how they interact and what they are. That's the first step for sure. Second step, while you're doing it, start with a small amount of money or pay per trade, right, you can trade virtually, and just practice without any real money at risk at all. Just so you learn a little bit more about it. That's always a very widely accepted form of education. And then yeah, number three, educate yourself right? It doesn't have to be my company. There's tons of websites and articles and YouTube channels that will educate individuals on how on how to trade but the very first star is have a have your mindset and your understanding that it is is achievable and is possible for anyone and yes, it might take 10 years. But what doesn't what doesn't take a long time to be really really, really amazing at it. If you see a professional basketball player, yeah, they make millions of dollars a year, but they pin planks and six. Yeah, now they're 20. Right?  Grant Yep. So with regards to a call to action, where would you send people? someone's like, hey, I want to learn more about this. I want to get connected with Jerremy in his system, where are you going to send them? Jerremy Absolutely the best call to action, go to https://www.reallifetrading.com/ and click on the beginner's tab. And again, you'll notice that it's free. There's no sales pitch in the presentation. It's just like, hey, man, here's the content, enjoy, go through it, and start there. And I have my email everywhere. I have contact pages everywhere. It's a very easy, user friendly website, and we have a connection teams or reach out to you just to see if you need any help. If you have any questions, we offer free coaching and it's an amazing opportunity for people to learn. Grant It is amazing. Thanks for your time today. Any final comments for we for wrap up? Jerremy Oh, final comments. This was my dad told me, I asked him what I should be when I grew up when I was about 14 years old. And he said son, study money, study money, study money. I didn't know it at the time was like I don't know what that means. But looking at it now that's that's good advice. Because I think the four most widely under underrated and least talked about but most important subjects on planet Earth are sex, money, religion, and politics. And the least divisive of those four is probably money. So study that one and the other three will start to slowly work themselves out. And you'll start to get into a position where you can hire help and coaches and mentors and, and assistance. You can be in positions where you can change your life dramatically and very quickly using money for the tool that it is, which is an expansion tool of your heart. Grant That's awesome, awesome advice. Jerremy, thank you so much for taking the time with us today. Really appreciate that. All the insights you shared, everyone thanks for listening to another episode of Financial Investing Radio and until next time, get some real life trading. Thank you for joining Grant on Financial Investing Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.  

Welcome, everybody. In this episode, we learn the secrets of systematic growth for your portfolio. Grant Okay, everybody, welcome to another episode of Financial investing Radio, I'm excited to have in the house with me today, Mr. Adrian Reid coming all the way from Australia, which is fascinating to me that I have this opportunity to get connected with this professional trader on the other side of the planet, literally from where I'm sitting at the moment. First of all, Adrian, welcome. Adrian I'm thrilled to be here. Thanks so much for having me on the call. And isn't this just a fantastic part of the world? You know, we we can communicate from the other side of the world we can we can help traders from all over the world. And there's just really no boundaries now. Grant There really isn't. I love that. And what I love is your willingness to get on and share tidbits and insights, right to help the everyday trader who's really trying to figure out how can I grow my portfolio in a way that's going to manage risk? And I know you've got some excellent insights on that. But before we get into that, would you mind stepping back and sort of thinking, what's your origin? How did you get into trading? What even brought you to that part of the world at this point? Adrian Yeah, absolutely. Sure. So I've been trading for about 20 years or so now, plus or minus, let's say, six months. And What first got me interested in was actually well, before that, well, before that, my family had a board game, in, in our holiday house, called the stock market game. And I have these amazing, fascinating memories of playing the stock market game becoming a paper billionaire by going around the board and buying lows and selling highs and collecting dividends and stock splits and all of these things. And it was just fascinating. Like, oh, this is this is the best thing ever, was my recollection as an eight year old. Now, I didn't trade or invest for a long time after that. But that was sort of my first memory of the stock market. Wow. And when I started work, I pretty quickly came to the realization that I didn't want to be in the corporate world forever. You know, I was commuting. I was working extremely long hours, I had a lot of high stress right from day one of my first job. And I said to my dad at the time, oh, it basically is this one. It's like, Aha, yeah, I don't want to do this forever. So what do I have to do to not have to do this forever. And we talked about investing, and we talked about stocks and the stock market and real estate and other forms of investing. And so I just started down that, that journey. And when I first came across some money, you know, from from work, I started, I started buying stocks. And I did the typical thing. You know, I didn't actually know anything, but I didn't know that I didn't know anything. So I bought stocks in companies that I recognized that I thought were big names, good companies.  Grant And now this is gonna go up, right?  Adrian Yeah, yeah, this is gonna go up. This is a great story. And there's a big company, um, you know, I recognize that this is gonna work and I'm smart. So therefore, I should make money. And of course, what happens? You know, I bought in exactly the wrong time it went down. I lost money. Very, very cool at the peak, right? Yeah, yeah, absolutely. And so I look, I played around with that for a period of time. And it took me three years of trying lots of different styles. Before I actually started making money on it in the corner. I tried that fundamental animal. Grant First of all, that's impressive that you were able to, so you didn't trade to the point where you lost all your capital, right? You are at least wise enough not or did you at times, say hey, I blew it all out. I gotta rebuild. Adrian Oh, I had no look. Absolutely. I have never blown up an account. So when I think one thing that I've really got, that I did well, from the very beginning was always very cautious about risk. And this is critical, right? Because if you blow up your account, the problem is you've got to then go back to work, you know, save money, and that takes time and You've got to build up the courage and that takes time and you're gonna put the money in the market and and then try again. And then chances are you blow it up again. So as soon as you blow up and lose your money, you've just wasted months and months and months. So from the very beginning, I took small trades, small positions, low risk. And I had learned a little bit about risk control from some of the books I read. And for three years, I was able to survive and on my account sort of went sideways and down a bit and sideways and down a bit, but never really made any gains. But the thing that turned the corner was I, I read a series of books called Market Wizards, which no doubt you've read or seen. And Market Wizards is fantastic, because it's a series of interviews with professional traders who have done extremely, extremely well. And as I was reading through it, I realized that all of these traders have their own unique style. And some of them really were like, really resonated with me. And some of them were like, it was an alien from another planet. And what I realized by reading through those interviews is I had to find a style that really fit me suited my personality. And the one that resonated, the ones that resume the most were the systematic traders, the ones that have rules and did tests to check those rules worked, and then followed those rules. And the reason I resonated is because they didn't spend every waking and sleeping hour staring at the screen. Mm hmm. They spent 15-20 minutes, 30 minutes a day running their rules, placing the trades and that was it. And that's what I needed. Yeah, because I was working a job I you know, I didn't have enough capital to live on my trading back then. Yeah. And so I started trading systematically. And you know, I took three months off work to develop my first trading system. Grant Really? Yeah. So you took some sort of sabbaticals? Adrian Yeah, I just I just said, Look, this is, I want to do this. And so I took three months off, and they my boss basically laughed at me and said, Good luck with that, you know, trading. Okay. Yeah, right, right. So I didn't get a whole lot of support them from from anyone else apart from my wife and my family. But you know what, when I implemented my first trading system, my results basically turned around on a dime. So if you look back at my equity curve, you can pinpoint to the month where that trading system went live. And that's because all of a sudden, I was consistent, and profitable, I had an edge. And from then on, it's just a matter of patience, of continuous improvement, and adding capital, and then eventually to get there. Grant So let me ask you this, in terms of the trading system that you've developed and matured over time, what's your general timeframe? And are you in your positions for a few days? Several months? I mean, what's, what range do you trade? Adrian Yeah, this is a good question. This comes back to the idea of it's got to fit your personality. So I'm a fairly patient sort of person, I don't like frantic activity, I don't like you know, really fast paced decisions in high stress. So I'm, I'm a longer term, sort of hold. My my favorite style is long term trend following. So that could be I could be in a position for many months. But I'm quite, I'm quick, systematically to cut my losses. So if I if I, if I enter a trade and hits my stop loss on day one, I'm out, I don't care. I'm very comfortable with that. So trend following is my natural kind of place that I gravitate to. But I have shorter term systems and mean reversion systems that are quite short term, because now having evolved as a trader and got comfortable with what the markets can do, and how they move, I can trade other styles outside my sort of natural center, if you like.  Grant So the key for us is to establish that main style of trading get good at that make that consistent, once you've got that producing now look at considering other styles that I hear you right? Adrian Yeah, absolutely. Yeah, absolutely. Absolutely. Because I think as humans, we, we have a natural sort of center or personality or style that will fit best with us. And let's face it, trading is kind of hard, emotionally. And so if you're, if you're challenging yourself with a style that doesn't fit, and the market is challenging you with all of the, you know, the volatility, the ups and downs, the uncertainty, it's pretty difficult to stick to the rules. But if you have rules that are somewhat natural for you, they fit you and you follow them, then you can learn all the emotional essence from the market deal with those and then branch out. I think that's key. Grant Well, so earlier, when you made the comment, hey, you went to your corporate job, and it was stressful. The thought that went through my mind was trading can be stressful. So what is it that protects you, right? It's a different kind of stress. How do you equate the two stresses? I've got corporate job, let's say and their stress is there. But wait a minute, I've got training in the stress. How are those different In your mind, Adrian I find that different, the types of stress are different in terms of the duration, intensity, and how much control you have over it. So in the corporate world, what I found was, I was just under constant pressure to do more and more and more and more and more and more, and anything that I could produce, the corporate machine still wanted more. And if I wanted to perform even better next time and get a bigger bonus, then I had to produce more. So it was relentless. Whereas in the market, and it's also externally imposed, you can't do much about it, apart from resigned from the job, if you no longer like it, you know, it's sort of outside your control is what I think you don't feel. I felt that way. Now, in the market, the market is also outside our control, we cannot control the market. But we can control certain things which are really, really important. We can control whether we are in the market or not. And we do that through our system rules that we trade with, we can control how big our positions are. So that whether or not the volatility causes stress or not. So if you position size very aggressively, you're going to have more stress and more stomach acid in your life. If you position size conservatively, you're gonna have less. So we can also control what strategies we use. If you're long, lonely, and you're highly leveraged, it's going to be amazing and pretty stress free in the middle of a bull market, that the end of a bull market as it's turning, it's going to be extremely stressful. But if you're long only, you don't use that much leverage. And you've got some some short term systems and some long term systems, and you can make money from a bear market. And you can make money when the market turns because you've got different strategies, then some of that stress is really dealt with, because you're diversified in a way I saw. Grant So if someone's new to trading, does it make sense for them to approach it that way? From the beginning? Or do they still need to get really good at say, their first initial personality style related system? And then over time, then build that more equitable approach to trading? Adrian I get really good question I, I think the answer is you've got to start somewhere. And in order to start, you've got to make sure it's not too overwhelming. So I would say start with one system, or one strategy that you can quickly build confidence with. And it really helps to have a guide to kind of step you through that. But if you've got a like someone to model or someone to follow, you can quickly move from one simple strategy to a diversified portfolio. And so, you know, I teach traders and I teach a lot of brand new traders, and I've taken people from knowing absolutely nothing to a very diversified portfolio in a couple of months. So it really is just, oh, wow, a couple of months yet. Because when you when you are given a set of rules that work, and you're given the tools and knowledge to test and evaluate those rules for yourself, so you can build confidence in them, then you can start trading. And then you just you start trading one set of rules, then you get another set of rules, and you test and evaluate that. And then you can start trading now, and then the next set and the next set. So you can pretty quickly build a portfolio of strategies, if you're not trying to figure everything out on your own. Right. Grant Okay, so you started to address some of the questions I had for you, which is how long does it take for someone to get proficient using your system? And it sounds like the first part is they could begin some level of proficiency within two months. Is that accurate?  Adrian And for both a completely new trader who is motivated and willing to learn and willing to ask questions can be up and running in in with a system in a month or less, then with a portfolio of systems within two to three months. Grant And when you look at that person going after a portfolio, what sort of capital do they need to bring to to this trading style. Adrian So my style is all systematic, and each system is a diversified portfolio of instrumental positions that are hold so what let's say one of my trend following systems in stocks, that makes money as stocks just continue up on their their long trend and then when stocks turn around and go down, it gets out and takes you to cash, that system might hold 20 simultaneous positions. Okay, now, if you're trading the Australian market, the Australian market has this unfortunate rule where each trade has to be $500 in value. You can't place a new trade for $200 It's five Under dollar minimum, so for that system on Australian, you need $10,000 to trade it properly, but in the US not so. And in the Asian markets not so so. So you really, you can start with just a couple $100 Or a couple $1,000 If you choose the right market. And this is one of the reasons why the cryptocurrency market is so great, because you can you can trade a diversified portfolio in the crypto markets with very small positions, and there's no minimums, and the costs are very, very small to trade it. So you really capital isn't really a barrier to entry anymore. It's it's really the learning that is the barrier to entry, you've got to have rules that give you an edge. And you've got to have absolute confidence in those rules. That's what you need to win really. Grant So the biggest challenge that seems over the years in the years that I've been trading as, as the discipline trader mentality, right, the ability to stick with those rules. So what tips or techniques do you have for, for sticking with the rules, right? Not letting the emotions overcome saying, Hey, I'm just going to take the loss, not doing revenge trading, etc. Right? What What are your tips for sticking with the rules? Adrian Yeah, this is this is great, because when I before I, before I became a systematic trader, I was a discretion. I did discretionary technical analysis, and I did fundamental analysis, and I really struggled emotionally. And systematic trading really helped. Because there was no, there's no ambiguity. If you have a system, which is objective that tells you if A and B and C happens you buy if D or E happens you sell, you don't have to wonder, is it a buy signal? Or is it a sell signal? So I think most people will benefit hugely by trading systematically, rather than with discretion. That's the first thing. Second thing is your system won't make money all the time, and it won't be profitable on every trade. It's the the your account is gonna go up and down gradually on a path to building your wealth. So you've got to be comfortable as the account swings around. And the biggest trick to getting comfort is building confidence in the rules. So we have our rules, like I can give you the best trading rules in the world. But if you haven't tested and evaluated them for yourself, then when they start losing money and going into drawdown, it's gonna be really hard for you to keep following them. Right? If you've tested them, and you know, okay, for this set of rules, a 15% drawdown is perfectly normal, or three or five losses in a row was perfectly normal. And if I just keep falling it, I'll come out the other side, if you've done that work, not me telling you that's the case you actually doing the testing, then you can sit comfortably knowing this is perfectly normal. Grant So do you provide the traders with a certain level of back testing tips and techniques to prove that out? Adrian Yes, absolutely. I think this is the the most important thing, because what what most people ask is, what stock should I buy? What crypto token should I buy? The next level of question? It's a very basic question. Next level of evolution is what rules should I follow? Yep. And if someone asks you what rules should you follow? They've evolved beyond keep trying to get tips, right? They're trying to get a process. The Evolution above that is I these are my rules. And I have confidence in them, because I've tested them. And I've evaluated them. So the most important part of my program, my teaching is actually the the back testing, the evaluation, the optimization, improvement of systems, yes, I give systems, but I don't want you to believe and trust me that those systems work. I want to give you the skills and tools to make that decision for yourself. So you can actually follow them in good times and bad. Does that make sense? Grant Oh, total total sense. Yeah. That way that you have a greater probability of being disciplined of sticking with that system and not not bailing when you shouldn't? Or not being able to get back in when you should? That's, that's right. Yeah, yeah. critical aspect for them. So when you think about the portfolio's you've talked about you doing stocks? You doing crypto? Do you do have an approach where you separate the portfolio across those? Or is is crypto always part of it? Or do you just focus in crypto with some of your trading strategies, but different ones in stocks? I know it's a long question. And how do you distinguish between the two trading? Adrian Yeah, it's a good question. It's a good question. And one of the one of the things I realized when I started researching into systems is that different markets move and behave differently. So you know, gold moves differently to oil moves differently to the stock indices moves differently to the Australian stock market. So my rules are developed for each market. Now a great set of rules will probably work on several markets, but you design it for a certain market. So I have rules for the Australian stock market rules for the Hong Kong Stock market rules for the US stock market. And I have rules for the crypto markets and then different. What I have that sits above all of those systems is a capital allocation model. And I say, okay, for my portfolio, I have this X percent in system one y percent in system two, Zed percent in system three, and so on. And each day, when I'm calculating the size for my positions, I go all the way to the top and say, Okay, how much money do I have in my accounts? What are the percentages for each system? And then within each system, how do I size each trade. And by cascading it down like that, you do a couple of things, you make sure you stay diversified, you don't get concentrated, you make sure your risk on each trade is low, and red linked to the amount of capital you've got in that system and in in total in your account. So the systems are all separate, but they essentially trade a common pool of money and have an allocation to each system, which keeps everything safe and balanced. Grant Okay, so. So having that in place, then it makes total sense to, to organize it that way. And it sounds like because I've traded lots of different markets like you having the rules that are that are unique to market makes total sense. Because each one of them has different personalities, even with the same let's say it's us base stocks, then even even each of those have their own sort of personality and characteristics. And so the question is, you know, trading apples gonna behave differently than trading IBM, right? Or whatever, right, that they all have that? So my question is, what in your trading system in terms of your rules? How far down do you get? Do you get to individual stock sort of rules, or you keep it to sort of industry or sector rules, how far down you go with that. Adrian So for me, personally, I usually go down to a market, I trade one set of rules that would be applied to a, an entire market or a subset of that market, like, I have a set of rules that would be applied to stocks in the s&p 500, I have a set of rules that would be applied to the entire US stock market. So NASDAQ, Amex NYSC, not pink sheets, for instance, I have a set of rules that isn't applied to the entire Hong Kong market, and the entire Australian market. So I sort of play at that level, you can develop systems at a lower level. But the trouble is, the lower you go, the less trades you get in your back tests, the less confidence you have that the rules really work. Grant Yeah, that's what I was wondering about your comment about back testing, I started thinking, Oh, wow, because I know the personalities of some better than others, I obviously don't trade all of them. And after a while you find a few that you feel like you start to understand. So you focus on those, I started like, wow, if you're gonna do that back testing against each of those different types of personalities, that's that's a lot of work. To do that back testing. Adrian The back testing is probably not as much work as you think. Because the the way, the way I do it is I have a subscription to a data provider, which gives me all of the stock history for all of the stocks on all of the markets. Okay, I put that into some trading software, I use Amy broker, there's other software, but Amy broker is probably one of the best, best value trading sort of back testing software's. And in the software, I codify the rules and the portfolio management principles and rules in the position sizing. And then I can back test using those rules on the entire market all at the same time. So once I've coded to run a backtest, over 30 years of history, over 1000s of stocks listed on the US market takes about a minute. Oh, really? Wow. Okay, so we're not doing this manually? Oh, guess. It's not just a matter of pressing the backtest button and saying, oh, it's profitable. There's steps you need to go through in the analysis to validate Oh, it's profitable, it's stable, the rules are all significant. I haven't over optimized or fine tune my system to particular trades or the past data. So back testing is really a process. But the labor intensive stuff is taken out of it by the software. Grant So that makes it it makes total sense. Because I've both written code on as well as use multiple back testing systems. And I know they can become quite involved. My question is now with that back testing strategy that you have, as you then go to apply it, is it the human that's clicking the button, or do you do leverage software that actually says Oh, well, hey, here's the point to actually execute based on these rules. And therefore it executes that for you. Adrian Yeah, it can be both. So on the crypto side, my trading is 100% automated, I literally, I don't do anything, I just monitor that the program, the algorithm is working. And I get a report at the end of each day saying these are the trades you placed. And I just make sure that that's all, you know, in line. On the stock side, it depends on the style of the type of stocks you're trading. So if you're trading extremely high liquidity stocks, like let's say you only traded stocks in the S&P 500, it's pretty easy to fully automate that one. It's less troublesome to fully automate that. And I won't say too easy because there's a bit of tech involved in the automation, if you're trading penny stocks, or smaller caps, where there's a little more risk of slippage, and you want to be a bit more careful with your entries, probably less wise to trust that to a piece of software to place your trades for you. So I do most of my I do my stock trading manually. Okay, but I'm moving to automation on that side for selected systems, not for every system. Yes, but I think the advantage is, on the stock side, I'm only trading once a day or once a week. And so it still takes very little time, even though the execution is not automated, I literally spend 15 to 20 minutes a day, placing orders. And that's all I do on my trading, it's really quite quick when you trade systematically. Grant That's amazing. So if I pull it back then to you got a brand new trader, they're gonna put in about two months of effort before they can maybe get a portfolio in place. I'm assuming initially, that's probably manual trading activities, but over time, then they take on more and more automated capabilities. Is that fair? Adrian Yeah, they could if they wanted to, if they were comfortable with the technology to do so, as some people are not, you know, they're not programmers by background. And some of those, you know, the API's or that is it's not straightforward. So you don't have to do that. I've traded for 20 years, and I'm still doing some trades manually, that, you know, 1520 minutes a day doesn't stress me out too much. So I'm okay with that. Grant Yeah, yeah. If you are stressed out, Adrian, then yeah, I'd say well, wait a minute. No, that's not stress 15 to 20 minutes. Um, so quick question. When you talk about the profile of your, you know, the students, the people that you take through this, can you describe a few profiles of people that tend to do well at this,or I have literally taken all sorts. Adrian So, you know, I have on one extreme, the people who gravitate most naturally to systematic trading, and typically, typically analytical types, engineers, scientists, accountants, people who are comfortable with computers in code. And they will typically pick it up very quickly, they'll they'll see the value in the analysis, they won't be stressed out by the the software, the analytical work or the coding. And they'll be off to the races very, very quickly. But at the other end of the extreme, I've taught artists, people with adult someone with a dog walking business, some people who are stay at home moms stay at home dads, who with with no analytical background, so it works for all sorts. But the common the common drive or factor amongst all of them, is you is desire. You've got to and fascination, you've got to really want to learn to trade the markets. What I say to people, when they come to me say can you teach me to trade say, Well, yes, but why do you want to learn? And what do you think about the markets and people who say, Oh, look, I just think I, I should invest? I don't really know anything. And stock seems easier than property because I've got no money. I say, You know what, it's probably gonna be a hard journey for that person. But for someone who comes to me and says, Look, I just, I just love the way the markets move up and down. It's, it's kind of fascinating to me. I've tried, but I lost money. And I don't know why. And I just want to learn, you know, that person will succeed ultimately with the right guidance. Grant Awesome. That that's wonderful. Okay, so if someone wanted to find you, how do they find you? What's what's a call to action here so they can get Adrian Yeah, absolutely. So the the business in the website is enlightened stock trading and for your listeners, Grant, I've put together a page with some kind of free free content and courses just to get people started. So EnlightenedStockTrading.com/FIR, register there and they'll get a bunch of cheat sheets about how to avoid losing how to make money and how to build confidence in your systems and a course on the millionaire trader code which gets your mind right and shows you the path to success. Grant Excellent. Wow. Thank you for doing that. I didn't know you're going to do that. I appreciate that. Adrian Yeah, no pleasure. It's exciting because you You know, what I want most of all, is to stop people from getting into the market uninformed, and just losing their money. Because a little bit of education will keep people alive. And a little bit more education will actually show you how to make money. So, you know, what I'm aiming for is, first of all, make sure people who get into the markets just don't lose everything in the next two weeks, because that happens more often than you'd think. So absolutely. With that do people can be in the game. Grant And with the increased volatility in the market today, does the systematic trading, you still fill fill work? Well, I mean, it's obviously not. It's not that secular bull market that we've experienced over the last, you know, plus, you know, decade or more, right. So that's change. Any thoughts on that? Adrian Yeah, the key here is diversification. So you don't want to as you evolve as a trader, you'll become more and more diversified. And you can make money for more different market conditions, you can make money from quiet up markets, from volatile sideways markets, you can make money from volatile downward market, you just need the right set of rules. So none of the market conditions need to be scary. It's just a matter of finding rules that work and applying them consistently. And that can those rules turn themselves on and off. Well, at least in my world, I create the systems to turn himself on and off when the time is right. So my long side trend following turns off when the market isn't in a bull phase. So I'm not going to keep taking signals all the way down in the bear market, because that's just silly. It's low probability. But I will sell stock short when the indices, rollover, and everything is clearly going down. And I'll hold the short positions until the market comes up, or until I've got a decent profit and then hits my profit target. So it doesn't matter what the markets doing, you can have a systematic approach that works. Grant Great. I wanted to make sure that point came out that this is not just about because earlier we talked about, oh, well, I put it in I think the long term, the reality is for the different market conditions, you have the set of rules to handle what the current market conditions are. So that's key. Adrian Yeah, absolutely. That's really, really important. You've got to diversify. No one method doesn't matter what you're trading or investing in no one thing is your path to wealth. You need to have different strategies, different approaches, and systematic trading is no different. Grant Adrian, it's been a pleasure. Thank you. I know it's, it's already tomorrow. It's Saturday. From the future in Australia. It is from the future. What's the market look like? Yeah, just let me know. Right. Adrian I'm fortunately markets closed in the future because it's Saturday. Grant Ah, that's right. That's right. Well, Adrian, thank you for taking the time, especially on your weekend. I appreciate you doing that. And I look forward to chatting with you again, because I'd like to do a follow up with you in some time. And everyone please take a look at that call to action that you shared on that URL. What's that URL? One more time? Adrian   It's EnlightenedStockTrading.com/FIR Grant Thank you very much. Okay, everyone. Thanks for joining. And until next time, get some systematic trading. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com.  

In this episode, we take a look at the question Can AI Be Used to Automate the Mundane? Grant Hey, everybody, welcome to another episode of ClickAI Radio. So welcome, I am excited to bring into the show today someone that it's taken me several times trying to get to this, busy busy guy. This is Evan Ryan. Evan, I hope I said your name right, right. Because I've said some people's names wrong. It's not Yvonne right or not right on? Is it Evan Ryan? Right? Evan It is Evan Ryan. Yeah, you got it in the right order. Most of the time, it's in the opposite order to flip it around. Grant Right. Um, founders CEO, King, of Teammate AI, right. Evan I just tried to go by founder. Okay. Grant Awesome. All right. Tell us a bit about yourself, Evan, what's brought you to this point. And anyway, thanks for joining the show. All right. Let me stop talking. Go ahead. Evan Well, Grant, thanks for having me. We were talking before we started recording, I just hit it off from the first second, at least in my opinion, I'm having a wonderful time. So really with me, I didn't really notice this. But throughout my entire childhood, I really had zero tolerance for boring stuff. I was somebody who just really likes doing fun things. And I like doing things that are that are entertaining, that are challenging, that are fascinating, that are motivating. But I don't like doing things that are boring. And I will basically do anything that I can to avoid doing boring stuff. And of course, now Hindsight is 2020. But that's sort of how I view a lot of my childhood. Well, in 2016, I went to a conference where they had this guy by the name of Jeremy Howard. And at the time, or a year or two prior, he was the number one ranked AI researcher in the world. He was talking about all this stuff that you could do with AI. And he, he mentioned that he had this company that could correctly diagnose cancer from MRIs and CT scans. Cool better than a team of board certified doctors. Oh, this isn't 2016. Yeah, so I was just about to graduate from college. I was thinking that oh my gosh, like, this is just the coolest thing I've ever heard. I basically went into AI the next day. Did you really? Wow. Yeah, it was it was really sort of powerful and transformative. Over the next couple of years of sort of iterating around well, what what do I really want to do inside of AI? You know, how do I want to define AI? First of all, there's sort of no definition that everybody can agree upon. I really found that, you know, I hate doing boring stuff. I bet a lot of other people hate doing boring stuff, too. Yeah, yeah. So and so now we all know the pattern there for sure. Yeah. Yeah. And what I've found is really interesting is, you know, one of the biggest conversation topics around AI right now is will AI take all of our jobs? Grant Oh, my goodness. Yeah. I hear that so many times. Yes. Evan I mean, there are like so many ways to answer that with the answer, in my opinion, being No. But what I found is really exciting is when I bring people into the creative process about how can they automate the mundane, the boring, repeatable on their days, they're so excited, like the first time in a while that they felt like, wow, the future is so much bigger than my past. So for the last several years, we've been automating, we've been automating tasks and automating processes for small and medium sized businesses. Grant So let me back up. So you hear this guy speak. You're like AI is the place to go. Your passion is let me take boring out of life. So you're leveraging AI to do that to remove the boring. I remember my dad is really what got me into the, into the technology industry. He was in it if you can believe it, right? So he was like one of the early early pioneers in some of this stuff. And he I remember him using the phrase Boolean shift a lot. He would always talk like, oh, technology is a Boolean shift. And I'm like, What are you talking about? What's a Boolean shift, you know? But this is kind of a Boolean shift with AI something, it's going to totally replace us. But in fact, I think it's a shift to the right, where it starts to automate more and more capabilities. It does require us to re retool. But that's good. Because every big shift, you know, throughout our whole economic history, right, as a country, and as innovation continues to grow, we have to keep shifting to the right terms of the skill sets that are important and critical. So I don't see it as, as this big day AI is gonna come take over, and we're all gonna sit around and become people like on E. What is it Wally, that movie Wally? Right, where they sit around doing nothing? Right, I don't think is that, but I do think it is a shift in terms of the capabilities that will bring and therefore we'll get skilled up in other areas. What are your thoughts on that? Evan Well, I have a question first before, before I answer that, do you have a memory of when you were a child, where you first saw technology, do something and you're like, holy, holy smokes. Grant I do it was that this will highlight the age difference between you and I, it was my, my dad worked in this. So there used to be a competitor to IBM. They're called Control Data Corp, and my dad worked for them. He'd worked on some of the big mainframe systems. And I remember one time, he took me into one of their big mainframe centers, right, and we walk into this room and the fans are running. And the big computers are, and the disk, you know, the tape drive is going, you know, like, just like you see on the old movies, right? And the big disk drives worrying. And my dad sits down and starts coding some things on it. And the computer starts interacting with me saying my name and helped me, you know, asking me questions. And I remember leaning up to and going, this is what I'm gonna do. I don't know everything that just happened there. But I want to be a part of this. So yes, that that was my technology origin story. Yeah. Evan Yeah. For me. So outside of seeing this, like this was years ago, when I was like a small child. I remember my best friend was sending out invitations to a fourth of July, a fourth of July party in the United States. And, and he mailmerge at like age eight, e mail merge, like 500 contacts. Oh, that's everything that is just insane. Like, as incredible as mail merging. And AJ at AJ. Also, like, what is this thing? mailmerge? Oh, my gosh. Yeah, it's so fun to like, kind of think about, you know, there's this like, one time where at some point, you're like, I wonder what else it can do? This is just the beginning. Yeah. But to answer your question, you know, will we will we all end up just kind of like sitting around or being artists all day? I don't think so. The example that I use is, you know, right now you and I are talking over zoom. It took me three clicks in order to get into our zoom meeting. Right? And so what if I went, what if we went back to the 1950s. And we said that in 70 years, you're going to be able to talk to virtually anybody in the world, you'll be able to see their face, the audio quality will be crystal clear. And I'll cost you essentially nothing. But all 1.5 million telephone switchboard, switchboard operators will lose their jobs. Grant Yeah, yeah, it's it's true. Yeah, that Yeah, right. Yeah, no, people like, like, There's no way. Evan Right, and, and so there's always been this sort of technological destruction that has taken place. Even back when we humans created the waterwheel. And now you didn't have as many people moving water to generate power generate electricity, or when we created ladders, and, you know, there are all sorts of different pieces of technology that we've had, over the last several 1000 years that have changed the way that we worked. This is just another one, I think it's a bigger shift than then some of the other ones. But, you know, the printing press did the same thing. There are all sorts of people that were just writing books, they were writing the Bible over and over and over and over again. Well, they lost their job pretty darn fast. Yet we have more people on the planet than ever before, and more people are employed than ever. Grant To do this. I remember one of the startups that I was involved with earlier in my career in Silicon Valley, we're trying to solve the problem of we want to be able to allow software engineers and designers to design systems remotely over the Internet, right. And so in order to do that, we have some some design tools, but we got to make that accessible and you need to be able to manipulate it remotely. Right. This is before there's a lot of big fat network pipes. You know, you know, everyone's got high bandwidth, right. So we were trying to figure out how to get this to work. We ended up getting it to work, but the cost was prohibitive. Right Just so dang grim. So when I think about the progress of technology, it's lots of trying over and over again until you get to this point where enough of the complexity can be abstracted away so that ultimately the end user sees this simplicity enough to say it's adoptable for both ease of use as well as cost perspective. I think we're right there. They I, I think we're doing similar thing, right to go through the same round, which is, can I take some techniques that are advanced that have some value, but you don't have to have massive data science background in order to get the benefits from that, and I see that shift taking place where more and more of it can then become accessible to others? As the cost drops dramatically? Evan I've seen similar things, I I couldn't agree more. You know, I think AI is sort of one of those things that for a decade or two was promised but under delivered, no, yeah. And now it's so it's like it's coming in with a vengeance. And the tools that are being made are super intuitive. The use cases that are being documented, and copied and repeated are ones that affect a lot of people. And I think people are really opening their minds to the idea that they don't have to do things the same way that they always have, just because that's how it's always been done. So I think it's kind of a combination of a huge mindset shift. But also just the tools are flat out better than they've ever been before. And they're finally usable to the point where you can automate things and you can create API's with button clicks instead of with raw code. Grant Yeah, yeah. Which is, which is fascinating. I think it's bringing an order of magnitude capability, and will continue to do that, to the kinds of problems that we can solve it. And it's just, I think, at the crux of it right in others is you look to the future, if you can continue to provide this time to this kind of computational power. And add that in the future to things like quantum, when we start blending both of those, I think the kinds of problems we can solve becomes quite large. But let me pull it back to tell me about the problems you're specifically solving with teammate AI. So So you got into this space, you're looking to go solve some problems with AI, you want to make things simple, what are you making simple? Evan You know, we really love to help entrepreneurial companies, and primarily companies where the executive team really wants to grow, they really would love to grow, they'd love to grow five or 10x, and the next decade or two decades, or even shorter, but they can't bear the idea of doing it while radically increasing their payroll. Right. And the most common complaint that we hear is not you know, I have the wrong team or my team doesn't do great work. It's that my team is underutilized because they have too much stuff on their plate. And so what we really try to automate the problems that we try to solve, are really helping these team members helping these employees identify what are the things that you do in a day that you hate the most, let's not automate the stuff that you love, let's automate the stuff that you hate. And then we'll figure out together how we can automate this so that you never have to deal with it again. And so we do that with with companies, basically, across every industry. The the question, the second question that I get the most often is, what industries does AI work in? And my answer is always well, you know, AI is a little like electricity, like, sure there's the electric company. But every every company was benefited. And every industry was benefited by electricity. And it's kind of a similar thing. Yeah, you know, there's sort of no gift that you can give people that's more valuable than their time, in my opinion. Grant What would be some use cases that you're applying it to? Is it? Is it things like bookkeeping, is it, you know, mundane, you know, administrative tasks? Or is it in other areas that you're applying AI? Evan So every company is a little different? I mean, of course, there's like bookkeeping and accounts receivable, and how can we send invoices out of our ERP smoother or submit website forms for invoicing smoother. But there's also a lot of reporting, sort of data collection, data crunching and then putting that into a human readable format. That way, people aren't in charge of trying to figure out what the data says. Instead, the computer tells you what the most important things are to look for. We use AI to write newspaper articles, write and publish newspaper articles for media outlets across the country. Regardless of your feelings of the media, it costs too much money in order to be able to write an article especially for info journalism, like what happened in the local sporting events. So we're doing that all across the country and all across the world, all the way to multi day processes where maybe We're using machine vision to look at images and see, well, are there any defects in the products that we're working with right now? If there are defects, what are the defects and let's report back to the supplier, let's report it back to whoever's responsible in order to be able to get that quality control, like up to speed and up to where we needed to be. So we do the boring in the mundane like accounts receivable, we also do the really sexy and complex machine vision. And we're reporting back with here. Here's the percentage of products that you shipped us, for example, that that were manufactured properly or are up to spec. Grant That's quite a, that's quite a wide range of use cases. That's, that's amazing. Are you building your machine vision work off of the Open CV material or your you did this all by hand? Evan We use Fast AI's library, which Fast AI was the not for profit that was founded by Jeremy Howard. So basically, Jeremy was telling me in this conference, you know, here's all the great things that you can do about AI, by the way, I have a free and open course. And we found that their library is just absolutely unbelievable. So we'll try as hard as we can to be able to, to be able to build it from scratch. And the reason for that we originally did not try to do that we originally tried to use a lot of the open source. Yeah. But the reason was really interesting. It was that, especially when, when the process that we were automating wasn't related to customer acquisition, lead generation. Yeah, what was happening a lot of times was our was our clients who would get this new capability called this AI that saving, you know, hours or days per week in some cases, and they'd say something to the effect of, you know, everybody else in our industry faces the same problem. Could we license this to everybody else in our industry? Wow. Well, now they had this old legacy business, that they flipped into a software as a service business. And we realized really fast that we that we had to be able to make sure that our solution scaled to more than just one user. Mm hmm. Grant Amazing. So what's been some of the outcomes, you've noticed now that you've been out doing this across a range of companies, what, what's been the impact to them? Evan What's most exciting for me is that all companies are unique, yet all companies are the same. So they all have product delivery, they all have accounting, and bookkeeping, they all have sales and customer acquisition, they all have customer service. And so they all have these sort of functions, that interplay really nicely together. But largely, they're the same. The real differentiator, among a lot of companies is things like their supply chain, their product and their product delivery. So being able to help a wide swath of companies get clarity surrounding, you know, AI isn't just for Silicon Valley. It's not just for Tesla, and Mehta and Apple and Google, right. That's been really exciting. I think that there's a real market shift going on among employees, I think employees really have a smaller and smaller tolerance for doing stuff that's boring and doesn't move the ball forward. And companies are really incentivized right now, to outsource a lot of the work to AI in order to be able to retain their best talent. And so that's been one of the really, really interesting things. I think that's come out of the last six months. Grant Now, I guess I could imagine it would be something like removing them in the mundane so that you can tap into their creative, right, I think that's really sort of what you're after, right? It's try to exploit opportunities to get more creativity from your people. I would imagine in today's market, too, with a lot of attrition taking place and the challenges with hiring, that this also can be beneficial. It's is it part of a play to help people stay in their jobs where you could take some of the mundane out of it, and therefore allow them to do more creative and enjoyable things in their work? Evan It absolutely is. That's actually one of the biggest reasons why a lot of companies decide to work with us right now is because they know that they if it can create a work experience, that's five times better than what it is right now. That key employee might not go looking for an extra $10,000 A year or an extra 20 grand, or for new opportunities just because it doesn't feel like a right fit anymore. And so we're seeing that all the time. We're also seeing on the flip side of it, a lot of companies that are having problems hiring, or they're having problems retaining employees, just overall, maybe they have a 3040 50% attrition rate on new hires in any particular role. They're starting to ask the question, Well, I wonder why. Like, maybe it's us. That's the problem. And it's not them. That's the problem. And so all of those tasks that used to be hired, are now being automated instead. That way they can hire for those more creative and fascinating and motivating roles. I mean, I don't know anybody I don't know anybody who wakes up on a Monday morning looking forward to doing the same stuff they've done for the last five years. Grant Yeah, if they do well, yeah, that's another conversation. But sounds like you use a range of things from RPA to some custom built AI work, you guys have developed is your sort of toolset is that, right? Evan Yeah, whatever it takes to get the job done. A lot of times customers will have specifications. But, you know, Microsoft Power automates a really powerful tool. And there's a lot that you can do with 1000 lines of Python code, right, and with a great AWS or Azure suite. And so we do use, we have a handy and a really kind of wide tool belt. But what we find is that we're using the same tool sort of over and over again, which is really handy, I think, overall, and it makes it so that the tools are getting better over time. Grant They are Yeah, they're definitely getting better. Okay, so for our audience, what would be a call to action for them? If they were wanting to learn more about you and your organization and what you're doing? Evan Yeah, so I think the biggest thing would be head to automation secrets that teammate ai.com, there, you can get a free copy of my new book, AI is your teammate. And really what it does is it kind of helps distill down what are the mindsets necessary in order to be able to use AI in your world, whether you're a business owner and entrepreneur and executive or an employee? And how can you make it happen? You know, there's a lot of sort of how to guides for how do you make automation happen, but I've tend to find that they're all either way too high level, like, AI can only be used for chatbots on your website, or cybersecurity, or they're literally showing you lines of code. And so how can you make it happen, no matter what your level of technical capabilities are? So I would head there, no matter what, get a teammate, or you can get it on Amazon or Barnes and Noble or anywhere where you find books. Grant Oh, that's cool. So when you think about a bell curve in terms of the amount of time that someone would commit to working with a group like yourself, what does that look like, you know, the bell curve is or the efforts or the projects you do with them? Is it a two week four week, eight week? What does that look like? Evan I you know, for for relatively small automations. If you're if you're using a tool, like Zapier, for example, that would be two to four weeks. So very, very short, you could wake up this month, you have a lot of stuff on your plate that you hate. And by next quarter, you could have saved 510 20 hours a week, depending on what your job description looks like, all the way to six months to a year depending on if you have a if you have a really complicated sort of project that you need to automate. Grant Gotcha. Gotcha. Very cool. Okay. What questions do you have for me, you said you wanted to ask me some questions before we started? Evan I do want to ask you, yeah, what do you see? You know, so you do it a different type of like a eyes and really what we specialize in? We don't try to do a lot of the predictive the predictive modeling. Yeah. What are you seeing in the marketplace? On the predictive side? Grant Yeah. So on the predictive side, I'm definitely working in that space. Not so much in the CV area, but more in terms of predictive analytics itself. So you know, taking things like oh, how can I? How can I address stockout? problems, right, my supply chain? Or, oh, what can I do to increase sales? That is probably the number one use case that I see. Right, which is, hey, we're just trying to grow the business? And what are the conditions that are driving the best sales situation? Or how can I take costs out of my business? So efficiency plays? That's probably the second sort of style of problems that organizations need to solve. And similar to what you're describing, in all cases, it's Can I do it with the same amount or fewer resources? Right, I can't be adding more resources to this. In most cases, there's this FOMO aspect, which is there's this fear of the unknown, what is it that the AI can see that I can't write, because lots of times our brains are wired to see only just a few factors or variables. And then once we get too many dimensions out, our brain sort of gives out AI really exploits that well. And so casting as wide a net as possible, that makes sense for that business outcome. You're trying to target where it sells or whatever, and letting the AI help you to see all of those all those far reaching variables and pulling that in and saying actually, it's, it's the combination of these other factors as well means that this is when your sales take place. If it's this salesperson, during this time of the month to this particular market. You know, when the weather is clear in San Diego, whatever it might be, right? Those conditions tend to drive higher sales, whatever the situation, it's that watching business owners have that aha moment to go. Oh, okay. And that's, that's real satisfying, because then they go in and start tweaking their business, right, just enough to say, hey, it was worth the effort to discover them. One more thing, while I'm monologuing on this, there's that part. And then there's the other part, which is I find that AI, it can bring so many predictive insights, that it cripples the organization, right? It comes back and says, here's all the drivers, and here's all the factors, but it gives you, you know, 20 of them, and you're like, oh, okay, what am I gonna do with 20? Right? How do I figure and so that's the other key part of what we do, which is, we then say, oh, let's prioritize these into a series of incremental steps that moves the organization one step at a time. Otherwise, people get changed fatigue, right, it's too much to keep trying to, you know, do it all at once. So we take the insights that are predictive, go after those that have the highest probability as it relates to the business outcome, and then just go do one or two of those, and then rebuild, because contacts, you know, business shit, and business drift occurs, data drift occurs. And so you then refactor the the model again and gives you fresh insights. So how's that for a long answer? That's what I'm saying? Evan Well, that's actually you kind of touched on one of the follow ups that I wanted to ask, which is, we spend a lot of our time with the end users with the end employees, it sounds to me like you spend a lot of your time with the C suite. Is that correct? Grant We do, but it depends on the organization. And who's been tasked, in many cases, will we start with a C suite. And I'll tell you why. One of the challenges, I believe, with a lot of the AI platforms today is is the over over focus on model accuracy, right getting a 90% accurate, now, don't get me wrong, the model has got to be, you know, really accurate, but when it's done outside of the context of your business operations, then it means I could end up producing an AI model that's so efficient, that my business is not actually able to deal with or handle it may be bringing me too many deals, such that it actually increases the cost of goods sold, that it actually ends up hurting my business. And so it really needs this combination of a sufficient, efficient model connected to what are my business costs, my operations, my you know, the the amount of resources I have available, and that's why it needs to go a step at a time, right, you just keep going one step at a time to improve or grow it. So sometimes it's with the data, people. But if you do it outside of the context of those business questions, then it tends not to be as effective on the ROI. Evan That's a that's one of the things I was gonna ask, are you seeing that there are sometimes negative consequences where the AI is so good? Yeah, you know, that people either you have a change management problem where people's preconceived notions of why things happened was actually incorrect. And now you have to retrain, or something like that, where, you know, the like what you said, the cost of goods rises so much, because it's so efficient at acquiring new customers or getting more sales, that that the business wasn't ready to scale to that level? Do you see that that happens more often than not? Or is that a sort of a corner case? Grant I don't know, I don't think it's corner case. It's, it's a fair, fair amount of the cases, though, enough to be a worry, right, that if I don't take change management into consideration, as I roll out AI, then then my probabilities of success dropped dramatically that just because I have the insights from Ai, in my opinion, is only 70% of the way there, then you got to get that, you know, last mile and and the last mile is the successful rollout and adoption of this, and sometimes it's a cultural thing you're running into, people are worried, oh, I'm gonna lose my job. Others are like, Oh, this is gonna change my job. And then others are, well, we embrace it. But now we run into a money problem. And the money problem is that our business operations can't handle this adjustment. Or maybe the AI got it wrong, and the business can't handle that adjustment either. Right? Doesn't mean that it's always right. And so in either case, it can have that financial impact. And if we're not, if we're not taming the AI enough in the context of business operations, then it ends up creating a problem. So there's several hurdles after you get just those those predictive insights. Evan Yeah, one of the things that's interesting about hearing hearing your world which your world is just is so radically different than mine, I mean, with us, we have a pretty set, you know, this set of criteria. We're going to automate this process Is this process we've mapped out exactly what the steps are in the process, and then we build a computer to do it. with you what I think is interesting is, I hear all the time, that this concept of what we want to use data to make better decisions. Oh, yeah. And, like, there, I always think, you know, there's part of that that's true. But there's also part of it, that's like, you are thinking that the human should be making a decision right now. Grant I like to view this more as augmented intelligence. I know we say AI. But I think a should be augmented. It's really the state of where the practice is, I think in AI, to say that we're going to give all decision making rights over to some AI model and just blindly trust that I think that's naive in today's AI. Now, you know, they're getting better and better. But I work a lot of organizations where the majority, the AI model is early. And so it's growing, the need for a lot more cognitive support from the humans, to ensure that this thing is naturally moving in a way that is reliable, and truly predictable. And otherwise, I think you could just hand it off and say, I give all all decisioning rights over to the AI, I think that's foolish, you have the ability and need the ability, even after you've deployed an AI model to come back and vote on the impact of that insight. And that's important, because we want to continue to refine the training and retraining of the AI. Hey, what you just shared with me that predictive insight actually didn't pan out, that guidance really needs to come back into the models. Evan Yeah, I think, you know, the AI at the end of the day, like the algorithms, they make a prediction and they make a recommendation, but they never, they never make a decision. Now, humans either a prediction or recommendation, the human needs to make a decision. So the AI can provide all sorts of information, and it can provide recommendations. But But yeah, I don't it's not ready yet to to just understand how the world works and understand where you're going, what your objectives are, and then just say, this is right. It's not it's not quite otherwise. I know. I know, a lot of senior managers who are going to have really bad days. AI can do that. So what are some of your what are some of your sweet spots? What are the things that were the projects where you know, you know, that you can hit it out of the park? Grant Yeah, it's, like I said, it's in medium sized organizations typically trying to solve, you know, a revenue sales problem, right? That's definitely a sweet spot and supply chain areas, right? That's where they're looking to say, Hey, I'm trying to make sure I can, can keep inventory coming in at the right pace or the right rate, which is a serious problem now. But you know, we've also seen it in even in the current talent management shortages that's going on, which is, can I use it to help me understand the probabilities of, you know, certain groups or individuals who are candidates for leaving early right, and the cost and impact to an organization when that happens? Those are the types of use cases where typically we get involved. Those are, those are great questions, for sure. Okay. All right. This has been awesome. Yeah, you haven't. Thank you. Evan This has been great Grant. Grant Yeah, thanks for your questions. Any final statement before we wrap up about Teammate AI? Evan You can grab AI as your teammate on Amazon or at automationsecretsteamai.com. But mostly, I just hope that everybody has a future that's far, far bigger than their past, and far better than their past. Thanks for having me, Grant. Really appreciate this one. Grant Thank you for your time, everybody. Thanks for joining another episode of clique AI radio. And until next time, get some Teammate AI. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.

Grant Welcome everybody. In this episode, we take a look at the four pitfalls to AI ethics and are they solvable? Okay, hey, everybody. Welcome to another episode of ClickAI Radio. So glad to have in the house today Plainsight AI. What a privilege. Elizabeth Spears with us here today. Hi, Elizabeth. Elizabeth Hey, Grant. Thanks for having me back. Grant Thanks for coming back. You know, when we were talking last time, you threw out this wonderful topic around pitfalls around AI ethics. And it's such a common sort of drop phrase, everyone's like, oh, there's ethics issues around AI. Let's, let's shy away from it. Therefore, it's got a problem, right? And I loved how you came back. And it was after our episode, it's like he pulled me aside in the hallway. Metaphorically like "Grant, let's have a topic on the pitfalls around these some of these ethical topics here". So I, you hooked me I was like, Oh, perfect. That's, that's a wonderful idea with that. Elizabeth So typically, I think there's, there's so many sort of high level conversations about ethics and AI, but, but I feel like we don't dig into the details very often of kind of when that happens, and how to deal with it. And like you said, it's kind of the common pitfalls. Grant It is. And, you know, it's interesting is the, in the AI world in particular, it seems like so many of the ethical arguments come up around the image, style of AI, right, you know, ways in which people have misused or abused AI, right for either bad use cases or other sort of secret or bad approaches. So this is like you are the perfect person to talk about this and, and cast the dagger in the heart of some of these mythical ethical things, or maybe not right. All right. Oh, yeah. Alright, so let's talk through some of these. So common pitfalls. So there were four areas that you and I sort of bantered about, he came back he said, Okay, let's talk about bias. Let's talk about inaccuracy in models, a bit about fraud, and then perhaps something around legal or ethical consent violations. Those were four that we started with, we don't have to stay on those. But let's tee up bias. Let's talk about ethical problems around bias. All right. Elizabeth So I mean, there's really there's several types of bias. And, and often the biased and inaccuracies can kind of conflate because they can sort of cause each other. But we have I have some examples of of both. And then again, somewhere, some where it's it's really biased and inaccuracies that are happening. But one example or one type is not modeling your problem correctly, and in particular, to simply so I'll start with the example. So you want to detect safety in a crosswalk, right, relatively simple kind of thing. And, and you want to make sure that no one is sitting in this crosswalk. Because that would be now generally be a problem. It's a problem. So, so you do body pose detection, right? And if you aren't thinking about this problem holistically, you say, All right, I'm going to do sitting versus standing. Now the problem with that is what about a person in a wheelchair? So then you would be detecting kind of a perceived problem because you think someone sitting in the middle of a crosswalk but but it's really just about accurately defining that problem. And then and then making sure that's reflected in your labeling process. And and that kind of flows. into another whole set of problems, which is when your test data and your kind of labeling process are a mismatch with your production environment. So one of the things that we really encourage for our customers is, is collecting as much production close as close to possible, or ideally just production data that you'll be running your models on, instead of having sort of these very different test data sets that then you'll then you'll kind of deploy into production. And there can be these mismatches. And sometimes that's a really difficult thing to accomplish. Grant Yeah, so I was gonna ask you on that, you know, in the world of generative AI, where that's becoming more and more of a thing, and in the app, the appetite for sort of generating or producing that test data is the premise that because I've heard some argue, wait, generative AI actually helps me to overcome and avoid some of the bias issues, but it sounds like you might be proposing just the opposite. Elizabeth It actually works both ways. So um, so creating synthetic data can really help when you want to avoid data bias, and you don't have enough production data to, to do that well. And so you can do, you can, you can do that in a number of different ways. data augmentation is one way so taking your original data and say, flipping it, or changing the colors in it, etc. So taking an original dataset and trying to make it more diverse and kind of cover more cases than you maybe would originally to make your model more robust. Another another kind of way of doing that is synthetic data creation. So an example there would be, you have a 3d environment, in one of these, you know, game engine type things like Unreal or blender, you know, there's, there's a few, and you have, say, I want to detect something, and it's usually in a residential setting, right. So you can have a whole environment of different, you know, housing types, and it would be really hard to get that data, you know, without having generated it, right, because you don't have cameras in everybody's houses, right. So in those cases, what we encourage is, pilots, so you before, really, you know, deploying this thing, and, and letting it free in the world, you you use that synthetic data, but then you make sure that you're piloting that in your set in your real world setting as long as possible to, you know, sets out any issues that you might come across. Grant So let's go back to that first example you shared where you got the crosswalk, you have the pedestrians, and now you need to make sure you've got different poses, like you said, someone you know, sitting down on the road or laying on the rug, certainly using generative AI to create different postures of those. But But what about, hey, if the introduction, is something brand new, such as, like you said, the wheelchair or some other sort of foreign object? Is the generative AI going to help you solve for that? Or do you need to you need to lead lead it a bit? Elizabeth It absolutely can. Right? So yeah, it's, it's basically anything that you can model in a 3d environment. And so you can definitely model someone in a wheelchair in a 3d environment. And, and Tesla uses this method really often because it's hard to simulate every kind of crash scenario, right? I mean, sorry, it's hard to have real data from every kind of crash scenario. And so they're trying to model again, they're trying to model their problem as robustly as possible. And so in some of those cases, they are like, you know, all of these types of things could happen, let's get more data around that the most efficient, and kind of most possible way of doing that is with synthetic data. Grant Awesome. Awesome. Okay. So that's a key approach for addressing the this bias problem. Are there any other techniques besides this generative, you know, training data approach? What else could you use to overcome the bias? Elizabeth Yeah, so. So another type kind of is when you have, like I was saying a mismatch in test and production data. So a lot of people even you know, computer vision, people sometimes don't know how much this matters. When it's things like, for example, working with a live video. So in those cases, bitrate matters, FPS matters, your resizing algorithm and your image encoding. And so you'll have, in many cases, you're collecting data in the first place for your test data differently than it's going to run in production. And people can forget about that. And so this is a place where, you know, having a platform like plain sight, can really help because that process is standardized, right? So the way you're pulling in that data, that is the same data that you're labeling, and it's the same data that you're, then you know, inferencing on, because you're pulling live data from those cameras, and it's all it's all managed in one place and to end. So that's, that's another strategy. And another thing that happens is when there are researchers that will be working on a model for like, two years, right, and they have this corpus of test data, but something happens in the meantime, right? So it's like, phone imaging has advanced in those in that time, so then your your input is a little different, or like the factory that they were trying to model, the the floor layout changed, right. And they didn't totally realize that the model had somewhat memorized that floor layout. And so you'll get these problems where you have this, you know, what you think is a really robust model, you drop it into production, and you don't know you have a problem until you drop it into production. So that's another reason that we really emphasize having pilots, and then also having a lot of different perspectives on vetting those pilots, right. So you, ideally, you can find a subject matter expert in the area outside of your company to, you know, take take a look at your data and what's coming out of it. And you have kind of a group of people really thinking deeply about, you know, the consistency of your data, how you're modeling your problem, and making sure that kind of all of those, all of those things are covered? Grant Well, in reducing cycle time from this initial set of training, to, to sort of validation of that pilot is crucial to this because as you're pointing out, even even if you even if you keep that cycle time short, and you do lots of iterations on it, some assumptions may change. How do you help? How to me what's the techniques for, you know, keeping someone looking at those assumptions? Like you said, maybe it's a change in camera phone technology, or it's a change of the layout? Like I said, as technology people, Einsteins we get so focused on oh, we're just pushing towards the solution, we sort of forget that part. How do you how do you get someone? Is that just a cultural thing? Is it a AI engineering thing, that someone's got a, you know, a role in the process? To do that? Elizabeth I think it's both. So I think the first thing is organizations really need to think deeply about their process for computer vision and AI. Right. And, and some of the things that I've already mentioned, need to be part of that process, right? So you want to research your users in advance, or your use cases in advance and try to think through that full Problem Set holistically. You want to you want to be really, really clear about your labeling, right? So you can introduce bias, just through your labeling process if humans themselves are introducing it, right? Exactly. If you have some people labeling something a little bit differently than other people. So like on the edge of an image, if you have a person on the edge, do you count that as a person? Or is it or you know, or as another person? Or is it not counted? How far in the view do they have to be? So there's, there's all a lot of gray area where you really just need to be very familiar with your data. And, and be really clear, as a company on how you're going to process that. Grant So this labeling boundaries, but then backing up, there's the label ontology or taxonomy itself, right, which is, yeah, that itself could just be introducing bias also, right. Elizabeth Yeah. And then back to kind of what we're saying about how to ensure how to really think through some of these problems, is you can also make sure that that as a as a company, you have a process where you, you have multi passes, multiple passes on, on that annotated data, and then multiple passes on the actual inference data, right. So you have a process where you're really checking. Another thing that we've talked about internally, recently is you know, we have a pipeline for deploying your computer vision. And one of the things that can be really, really important in a lot of these cases is making sure that there is a human in the loop that there is some human supervision. To make sure that you're, you're, again, you weren't servicing bias that you didn't under your you didn't anticipate, or your your model hasn't drifted over time, things like that. And so something we've considered is being able to kick off just in that process, have it built in that you can kick off a human, like a task for a person, right? So it's, it's just built in. Grant And so it no matter what you do that thing is this, it's just as a governance function, is that what you're getting? Elizabeth Kind of so it's like, it's like a processing pipeline for your data. And, and so you can have things like, Alright, at this step, I'm gonna augment my data, and at this step, I'm gonna, you know, run an inference on it, or flip it or whatever it is, right? And so, in that you could make sure that you kick off a task for a human to check, right, or, or whatever the case may be. Yep. Yep. So there's several good, so good process maturity, is another technique for how do we help overcome bias as well as inaccurate models? And I'm assuming you're, you're almost bundling both of those into that right? In Yeah, both right. And, and like you said, they're the another way is reducing that time, and also making sure that you're working on production data whenever possible. So reducing the this, this is where the platform can help as well. Because when you you know, you aren't off in research land, without production data for two years, but you have a system where it makes it really easy to connect cameras, and just work on on real production data, then two things, you're, you're reducing the time that it takes to kind of go full circle on on labeling and training and testing. And then also you you have it all in one place. And that's that's one of the problems that we solve, right? Because, in many cases, computer vision engineers or, or data scientists, they're kind of working on the they don't have the full pipeline to work on the problem. So they have this test dataset, and they're working on it somewhat separately from where it will be deployed in production. And so we try to join those two things. Grant Yeah, I think that's one of the real strengths of the platform of your platform, the plain side platform is this reduction of the cycle, so that I can actually be testing and validating against production scenarios, and then take that feedback. And then augmenting that with the great governance processes you talked about. Both of those are critical. Let's let's talk a little bit and talk about fraud is, you know, certainly in this in computer vision, holy smokes, fraud has been probably one of the key areas that, you know, the bad guys have gone after, right? All right, what what can you do to overcome this and deal with this? Elizabeth You know, it can really become a cat and mouse game. And I think the conversation about fraud boils down to, it's not clear, it boils down to is it better than the alternative? Right? So it's not clear that just because there could be some fraud in the computer vision solution, it may or may not be true that there could be more fraud and another solution, right. So so the example is, technically, you used to be able to and I think with some phones, you still can 3d print a face to defraud your facial detection to unlock your phone. Yeah. And there is and so then they've, you know, done a lot of things, advancements, so this is harder to do, which, like there's a liveliness detector, I think they use their eyes, your eyes for that. And then you know, there's a few but you could still use a mask. So again, it's it's this cat and mouse game. And another place is is you know, there are models that can understand text to speech. And then there are models that you can put on top of that, that can make that speech sound like other voices, right? So the the big category here is deep fakes. But it's, you know, you can you can make your voice sound like someone else's voice. And there are banks and other things like that, that use voice as a as a method for authentication. Right, right. Grant I'm sure I'm sure we've all seen the the Google duplex demo or scenarios right. says a few years from now, right? I mean, that technology obviously continues to mature. Elizabeth Exactly. And so, so then the question is Okay, if I can 3d print a face and or a mask and unlock someone's phone, is that is that is that harder than actually someone just finding my, you know, four to six digit phone, you know, numerical code to unlock my phone. So, you know, so I think there it really becomes a balance of which thing is is harder to defraud and in fraud in general, you know, if you think about cybersecurity, and, and everywhere that you're trying to combat this, it's a it's a cat and mouse game, right? People are getting, you know, people are figure out the vulnerabilities in what exists and then and then people have to get better at defending it. So well. So the argument is, if I if I can say back to the argument is, yeah, it exists. But hey, how's this different from so many other technologies or techniques, where again, you got fraudsters trying to break in? This is just part of the business today? Right. That's where it is? Grant Yeah, I think it becomes a, an evaluation of is it? Does it cause more or less of a fraud problem? And then it's, it's really just about evaluating the use of technology on an even plane? Right. So it's not it's not about should you use AI? Because it causes fraud? It's should you use any particular method or technology because there's a fraud issue and what's gonna cause the least fraud? Right, a more specific use case? Elizabeth Yeah. Grant Yep. Okay, so So fraud. So, uh, you and I had talked about some potential techniques out there. Like there's a Facebook Instagram technology algorithm. Right. I think it's called seer. I think it came out not too long ago. It's a it's an ultra large vision model. It takes in more than a billion variables. P believe that. That's, that's a lot. A lot of massive. I mean, I've built some AI models, but not with a billion. That's incredible. So are you familiar with that? Have you looked into that at all SEER itself? Elizabeth Yeah, so So this, basically, this method where you can look, basically to try to address bias through distorting of images? Yeah, yeah. So I can give you a good example of something that actually we've worked on, I'm going to chase change the case a little bit to kind of anonymize it. But so in a lab setting, we were working on some special imaging to detect whether there was a bacteria in, in in samples, or not, right. And in this case, we were collecting samples from many labs across the country. And one thing that could be different in them was the color of kind of the substrate that the sample was just in, it was essentially a preservative. Wow. And so but but those, there are a few different colors. And they were used kind of widely. And so it wasn't generally thought that, you know, this would be a problem. But so the model was built and all the data was processed. And there was a really high accuracy. But what happened, and what they found out was that the, there was a correlation with the color and whether the bacteria was present or not. And it was just a kind of a chance correlation, right. But if you had had something like that, that image distortion, so if you took the color out automatically, or you mess with the color, then that would have taken that bias out of that model. And then as a second thing happened, actually, which was when the, the the people in the lab, were taking the samples out of the freezer, they would take all of them at once. And they were just kind of bordered. And so they would do all of the positives first and all of the negative second. And machine learning is just it's a really amazing pattern detector, right? Like that is that is what it is about. Yeah. And so again, they were finding a correlation just between the weather it was hot, more thawed or not. And that was correlating with whether it was positive or not. So, you know, some of this really comes back to what you learn in science fair and putting together a really Your robust scientific method and making sure you're handling all of your very variables really carefully. And, and, and, and clearly and you know what's going into your model. And you can control for that as much as possible. So, so yeah, that I mean that Facebook method is, can be really valuable in a lot of cases to suss out some of these correlations that you may just not know are there. Grant Yeah, I think what's cool is they open source that right, I think it's called swag SwaaV. Yeah. Which is awesome. The they figured that out and made that open source so that obviously, the larger community needs something like this course help deal with some of this, this bias challenge. Interesting. Okay, that's cool. So all right. I was I was I really wanted to ask you about your thoughts on that approach. So I'm glad to hear you validate that. Elizabeth Yeah, no, it's great. I mean, there really has to be a process, especially in a in a model like that, where you try to break it in any possible way that you can, right, there has to be a whole separate process where you think through any variable that there could be and so if there's a model that's, that has, you know, so many just out of the box, that's a really good, great place to start. Grant Yeah, awesome. Awesome. Okay. And then the last category here, around ethical violations, any thoughts on that? Elizabeth Addressing that overcoming that, you know, I think that really just comes down to when you need permission to be doing something, I need to make sure that you're doing it right, or you're getting it. And that, you know, obviously that happens in cases where there's facial recognition and making sure that people know that that's going on, and that's similar to being kind of videotaped at all right. And so that one's fairly straightforward. But sometimes people need to, you know, when you're putting together your ethics position, you need to make sure that you're really remembering that that's there. And you're checking every single time that you don't have an issue. Grant Yeah, permissions. And there's this notion, I'll come up with a term that feels like permission creep, right. It's called scope, right? It's like, well, you may have gotten permission to do this part of it. But you kind of find yourself also using the data stuff over here right to maybe solve other other problems, and that that's a problem in some some people's minds for sure. I was very good point. Yeah, various articles, people out there talk about that part of it sort of creeping along, and how do you help ensure that what it is I gave you the data for what we're using it for? Is just for its, you know, you know, permitted intended purpose, right? That was a challenge for sure. Okay, so you've been more than fair with your time here today with us, Elizabeth, gay, dry, any conclusions? What's the top secret answer to the overcoming the four pitfalls here of AI ethical? Elizabeth So one thing I have to add, we would be remiss if we didn't talk about data bias without talking about data diversity in data balance, right. And so, you know, obviously, the, the simple example there is fruit. So if you are looking at if you have a dataset with seven apples, one banana, and seven oranges, it's going to be worse at detecting the banana. But the more real world example that happens is in hospitals, right? So they, in the healthcare system, in general, we have a problem with being able to share data, even even anonymized data. So when a hospital is doing is building a model, there have been problems where a can be they, they have bias in their dataset, right. So in in a certain location, you can have something like if you're coming in with a cough in one area, it may be most likely that you have a cold, cold, but in another area, it may be more accurate to start evaluating for asthma, right. Grant So that kind of thing can come up so it if you if you take a model that's done in one hospital and try to apply it elsewhere, then again, that's a place where you can visit, is that kind of like a form of confirmation bias, meaning, you know, you have the same symptom, but you come into two different parts of the hospital and, well, this person's coughing and you know, you're in the respiratory area. So they immediately think it's one thing but now you go to another part of the hospital. Well, yeah, a cough is a symptom for that to suddenly you know, that's what they think you have. Elizabeth That's a great point. It really it's sort of the machine learning version. that? Grant Yeah, that's right. Yeah, it's a confirmation bias sort of view. It's like yeah, oh, this is, uh, but it how many variables does it take for you to actually have true confirmation? Right? But with this example from Facebook a billion, but how many do you need to have? Elizabeth I think it's really it's less about the variables. And it's more about your data balance and making sure that you're training on the same data that's going to be used in production. So it you know, it's less of a problem, if you are, you know, only deploying that model at one hospital. But if you want to deploy it elsewhere, you need data from everywhere, right? Or, or wherever you're, you're planning to deploy it. So So again, it really comes back to that data balance and making sure your test data and your production data are kind of in line. Grant Are there any of these ethical biases we've talked about that are not solvable? Elizabeth Um, that's a good question. I think Ah, maybe dancer, are you? Are you running? I think there are definitely some that can be really hard. So, so something that we touched on, you talked about, you know, is there inherently a, are our supervised models more inherently more biased than unsupervised? And like, the answer there is, is probably yes. Because you're T you're a human is explicitly teaching a model what's important in that image? And so you know, that that thing can be exactly what you're looking for. Right? You want to make sure there's not a safety issue or whatever it is. But, but, but just it's a human process. So there can be things there that you don't catch. Grant Yeah, yeah. Yeah, that's that's been a question on my mind for a while, which is the implicit impact of bias on supervised versus non supervisory, or work with another group called Aible, have you run into Aible, they're one of the AutoML providers out there. And more on sort of the predictive analytics side of AI, right. They're not doing anything with with computer vision, they have this capability, where they'll look at, but it's always supervised data, but what they're trying to the problem you're trying to solve is, okay, you got a lot of data. Just give me tone, give me signal. In other words, before I spend too much time, trying to, you know, do some training and guiding the model, just do a quick look into that data set and tell me, is there any toner signal where these particular supervised elements, they can draw early correlation to outcome or predictive capabilities. And the idea is that as the world of data keeps getting larger and larger, our time as humans doesn't keep getting larger and larger. So we need to reduce what's the total set of stuff we're looking at, dismiss these other pieces, they're irrelevant to, you know, being predictive. And then you can focus on the things that are important. Anything like that in the computer vision world? Elizabeth So So I was thinking I was trying so unsupervised learning is less common in, in computer vision. But, but, but one of the things that can happen is just the data that exists in the world is bias. Right? So So an example is say you want to predict what a human might do at any one time. And you want to use an unsupervised method for that. So say you want to scrape the internet of videos. If you look at the videos on YouTube, the videos that people upload are inherently biased. So if you look at security view videos, they're like, almost all fights, right. So your model, because that's what humans think, is interesting. And as you know, uploaded it in a security video. And so I mean, not almost all but a lot of Yeah, yeah, he's inherently what humans think are interesting. And so there are places like that where just inherently your data set is kind of biased because we're human. So So again, it's another place that you have to be pretty careful. Grant Yeah. Okay, so sounds like the problems are I'm gonna say I'm doing Air quotes. These are solvable, but it takes some discipline and rigor. Elizabeth Yeah, okay. And and it's just so important for organizations to kind of sit down and really think through their, their ethical use of of AI and how they're going to approach that and get a policy together and make sure they're really kind of living those policies. Grant Excellent. Okay. Elizabeth, thank you for your time today. Any final comments? Any parting shots? Elizabeth Um, no, I think I appreciate you having me on. That was a really fun conversation. And yeah, I always enjoy chatting with you. Grant Likewise, Elizabeth, thank you for your time. Thank you everyone for joining and this episode. Until next time, get some ethics for your AI. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.  

In this episode, we take a look at how to get AI signal on your business. Hi Everybody, welcome to another episode of ClickAI Radio. So you are busy running your business, right? And of course, you often wonder, is there any way for me to work smarter, not just harder? Well, years ago, and one of my startups I was working 100 hour workweeks absolutely crazy. And I was trying to keep everything moving for right, we had a very challenging client, that really took up 80% of our energy and the other 20% of the energy was spent on other clients, as well as all the administrative things right that it takes to run a business. One night, while we were writing and testing a bunch of code. I looked at my teammates and exclaimed, ah, there's got to be a better way. And they agreed. So we decided to step back and we looked at the assumptions on which we're operating. Number one, could we adjust the clients expectations, and number two, perhaps we could look at all the information at our disposal and look for insights, things that we weren't seeing. So we did both of those. And of course, at the time, we did not have AI, but we perform something that you might call AI signal. So the takeaway from the story is that by addressing both of those items, of course, we dramatically improve the outcome of the business. Number one, with regards to adjusting the expectations. This was, of course, one of those common sense functions. That should have been done months earlier. And of course, I'll chalk that one up as a life lesson. But number two, looking at our data and searching for insights, we found some clues that told us how to improve. So what is AI signal? Well, it's, it's similar to tipping your toe in the water before fully committing. Alright, so that reminds me of a backpacking story. When our kids were younger, we were backpacking, in the flat tops wilderness area of Colorado, and it was still early in the summer. And so there's plenty of snowpack melt, you know, coming down through the streams, and, you know, AI signal, we had to cross the stream, right, we couldn't find a way across, it was much too wide to jump. But we knew we had to get in and get over to the other side. So AI signal would be similar to testing the temperature of this fast moving water before hopping in, as well as getting a sense of how deep the stream is, and how fast the water is moving. Well. Did we do any of that? No. And so you know, we would have saved ourselves some emotional as well as physical heartache, if we would have tested out the conditions first. So instead, we took a run at it and jumped as far as possible, right. Meanwhile, the fast and very cold moving stream just switched, you know, with this downstream, right. So don't do it that way, obviously. Now, this, of course, applies to your business. And especially when it comes to AI, get some AI signal first on your business data to put your toe in the water. So let's look at an example. Imagine, imagine you have one or more systems as part of running your business, it might be a CRM, with sales data, you also might have a lead management system. And now they may or may not be connected to each other, when in fact, let's say they're not connected. Now, you only have so much time, obviously, and resources each week as a business owner. And so the question is, how can you improve the use of your scarce commodity of time each week? Well, one way to help you do this to fine tune quickly is to look for a signal. Well, what does this look like? Well, lets you know when you do an AI project. If you don't use a turnkey provider, like ClickAI, then you're gonna do the work of cleaning up the data and getting it ready for AI. This is is and can be a really significant effort on your part. And it interrupts Of course, all of your other business operations. But what if you could find out if there's enough predictive behavior in your data before you even spent the time to clean it up? So this is what AI signal is, right? It's an AI technique to preview your not so prepared data and run enough AI on it to determine if there's some signal in it that could ultimately be used to one, improve your efficiencies and to grow your sales. Now, in the example that I gave above, you know, you may decide to look for signal in your lead management data. And separately, you may decide to look for signal in your CRM data. But if there's enough signal in the data in one or both of those systems, you may want to knit those together, or have a turnkey provider and that those together for you. So for example, if your CRM CRM system has a history of sales and refunds and things like that, imagine the value of connecting your leads data to your sales data, of course, so you could see through the AI, things which could translate leads into either sales or refunds, you really want to know both right? The kinds of leads that is going to ultimately translate into refunds, the ones to ignore now, of course, it all starts with just getting AI signal. That's a, it's a powerful first step that lets you know if your data has AI potential, and if so, which data elements are the biggest contributors, that's incredibly powerful. This is, this is better than taking your car to the mechanic shop where the signal may be spend more money in these areas. It's more like having a gold mine and the mining engineer stating, hey, in this part of the mind is a vein of gold. And over here in this other cavern, there's another vein of gold. Alright, now you know where to focus and where to dig. So once you've course commenced with mining the material, but the real question is, what do you do with the material meaning with the goal meaning with the insights you receive? Well, getting an AI signal is exciting, of course. And it's dramatically time saving, it'll save you a ton of time just to know where the gold is. But getting the AI gold is even more exciting, and even more impactful now, using the quote unquote AI gold to grow your business and of course, improve the value of your business opens a lot of doors. All right, everybody. Thanks again for joining. Until next time. Check your business for AI signal. It's the fastest route to business value. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.  

Hey in this episode we take a look at how you can use the technology half-life to build your business. Everybody, welcome to another episode of click AI radio. So thinking about half life, technology half life, what does that mean your business? And how quickly are things changing? It's interesting in terms of the you know, different organizations have different views on how fast things are changing sort of the, the sort of the pop culture, if you will, of technology tends to treat it like things are changing quickly. And that and that that certainly feels that way for lots of us. It's interesting, though, to see the different viewpoints on it. Let's let's take a quick look at this. So here's one and that this comes from culture.io. Right, they talked about how digital transformation is more successful with a positive shift in culture, in fact, goes on to say 80% of enterprises will change their culture as a way to accelerate their digital transformation strategy. That's actually a Gartner statement. So the point here is that, hey, if if you are if technology is changing a lot, and it's, you know, impacting your business, you better change your culture. Alright, let's check off the box for the culture statement. Let's move on from here. So how fast is technology changing? Or what pace or rate are things changing? Singularity hub.com has got this interesting viewpoint where they they show the growth in the DNA sequence data over years, right, where they show the Internet backbone bandwidth over you know, over the years, all of these are course up and to the right in terms of you know, what we see on the graph, right? Or the number of bits per second, you know, over the years, right, or the number of bits per second per dollar, right? So you end up getting more and more, more and more data for your dollar. Alright, so these logarithmic charts come from Ray Kurzweil Neil's 2005 book, The Singularity Is Near right, and it's attempting to show that certain things are growing exponentially. All right. However, there's other views. This comes from itI, f.org. They've gotten an opposing view, they're they're making comments like, Well, wait a minute, is a really changing that quick. They make this comment, they say, Look, if technological change actually were speeding up, one would expect to see an increase in patents and productivity, but we don't. All right, they also go on to say from 2006 to 2015, US utility patents increased just 3.8% per year. And then they go on to talk about productivity, growth, so the productivity improvements of labor. So from 1980, in the US productivity, annual annual productivity growth in US was about 1.5% 1995 was up 2.5%. And, and from 2007, and 2017. It was only 1%, which is interesting, showing that the actual productivity, even though all these new technological wizardry, cool things are around us, as our productivity actually increasing. Well, they go on to make this interesting comment. I'm just going to read this right here. They make this point right here, they say, look, many technologies today are nowhere near half patent or penetrating half of the American homes a decade after they first commercialized. So they go on to point out that Fitbits came out in 2007. You know, consumer 3d printers in 2010, VR 3d goggles, 2015, none of them are anywhere near 50% market penetration. So the question is, well, so Alright, maybe maybe there's this increase of many new different types of technologies, but in terms of their adoption, and the impact in our lives and making us more productive. These guys are arguing that it's not really making a difference there. What is changing or growing fast, and this comes according to hosting tribunals. dot com, they have an interesting set of stats, he pointed out that what is growing is things like, like the number of smart devices, right? As well as the number of hosting services and, and and the number of websites, right and the amount of data and those are things that all tend to be increasing and growing, what up to almost 2 billion websites now. But some say that, that can only 75% of those websites aren't active. And so while there's still a lot of stuff out there and a lot of data growing, the real question is, is it making us more productive? And is it doing more for us and helping us to become better? What we are getting is a lot more data. So here's something from MIT, MIT said, Look, when it comes to changes, right, in terms of the amount of changes that are taking place, from their perspective, they felt like it's varies based on the industry. So it says from a low 2% per year for the mechanical skin can you know skin hair treatment, skin treatment, hair removal wrinkles, to a high of 216% per year for the dynamic information exchange and support systems. Their point is this. The rate of change from 2% to 216% varies across different industry. So one industry might see tons of new technologies, new changes, whereas the other hardly sees anything changing or it's changing very slowly. And even if things are changing, are they really making things better? Well, here's one of the challenges. With the increase in terms of numbers of technologies and things that are out there again, things have to be adopted to really make a difference. The one thing that folks are seeing is that there's a half life, that's taking place, there's a half life of the technology because of the numbers of things that are changing. So in general, we see that their half life is going down. And the other thing that's that's happening in terms of Half Life is around the skills of the people. And so this comes from emeritus.org blog, there talks about the half life of employee skills. I'm just going to quote here, World Economic Forum research in 2017, said that the half life of a skill is about five years, that was in 2017. And then by the end of 2021, was estimated to be down to four years now that those were just employee skills in general, some feel that the that that number is lower for technology skills, I've seen some folks say it's down to even three years. So if the half life of your technology skills, let's say that it is around three years, so you spend your time as an organization, you you're investing in the technological skills, so you're going to use technology to help compete, but the half life is, let's say three years for that. What that means is of course got to keep keep ahead of that curve. And as a small to medium business owner that starts to become fairly expensive value proposition. Here's, here's another piece right here, it says of the existing 25 million software developers 50% will witness the half life phenomenon to their skills. So means that without additional upskilling, only about 12 point 5 million of them are going to be suitable for jobs that we need to have them do by 2025. So that's a shortfall of about 18 point 5 million jobs. Alright, let me say it another way. So you're small to medium business owner, you're trying to use technology to be competitive, but the half life of technology is reducing whether or not it's making you more productive. That's another argument, as well as the half life of the skills of the people to bring the technology down to three to four years, that starts to become really expensive. So while it can be argued that the technological pace of change varies in different industries, it can also be argued that access to the skilled teams to leverage AI for your business is like fighting a drying or fight fighting around a drying waterhole in the Sahara Desert, right? It means that, you know, less and less water, you know, everyone's fighting for the last little bits or drops of it. As an SMB owner, though, I'm going to suggest that it's critical to not play the game of building your AI technology team using your scarce resources. So what I am suggesting, though, is that with the advent of democratized AI, and there's a bunch of platforms out there, like with ClickAI and able with Plainsight AI, who I've had them on here, before, you know you should take advantage of these platforms. And the benefit is that you abstracting the AI complexities away from you, while delivering the benefits and the values that AI has to offer. So the argument is this, given the democratization of AI that's available given the half life of skills for the people and the half life of technology itself, why would you do or take another approach without using democratized AI? So as always start with your business use cases and then of course, leverage democratize AI rather than investing in technologies with with you know, such a short to HalfLife Alright everybody, thanks for joining and until next time, spend your half life wisely. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com, now.

Hey, AI where are my results? In this episode, we take a look at some of the fundamental principles to getting results for your business using AI. Hey, this is Grant, Welcome to another episode of ClickAI Radio. All right, so I've been thinking about this issue around AI and the ability or inability of businesses to get the results from their AI efforts. So I'm gonna quote here again from Bernard Maher's book, it's called artificial intelligence in practice, he actually points out some interesting, he's got a series of use cases, or case studies, I should say, with their various use cases where AI has been applied, and what some of the outcomes or the results of those arms is going to pull from one of these here, this was a Kimberly Clark experience, where they were looking to produce some, some AI insights based on their customer data. Alright, so customer segmentation problem, how do we go after the market and try to improve our targeting with our marketing efforts and use AI to understand where to go with that. So what they did was, as they applied the AI, they ended up increasing their signup rates by 17%. And then they ran some other campaigns to optimize targeting of customers, this was for their dipende brand, and they saw a 24% increase in conversions. So how did they do that? Well, they they did it mostly by producing content, that more closely aligned with the customer profiles, that the AI predicted would be more responsive. So there's a great a great use case here, right in terms of applying AI to to this sort of marketing problem and customer segmentation area. Now, these customers, what they also found, was it, they ended up becoming more likely to be long term repeat buyers. So it's one thing to have an increase in conversions and sales, but to turn them into longer term buyers. That's a real bonus. Certainly, it also had the had the downstream effect of making them more likely to give positive recommendations to friends and family. So this is a great example of of successes for AI, right, it's a great use case for where we would apply it, and the kinds of sort of long term benefits that we want from AI. Of course, what we're finding is more and more market leading companies are transitioning into tech companies. And if we're not thinking of becoming a tech company, it actually is continues to move to our disadvantage. So it's essential that we do that, of course, we want to be competitive and, and even stay ahead of the pack, if you will. But turns out that AI driven Analytics is a lot more powerful than the you know, traditional business intelligence solutions are out there previously, and still certainly in use today by a lot of organizations that are focusing a course on customer segmentation. But the real point is, by applying AI ultimately into the space of doing custom customer segmentation, the AI is able to see things of course that difficult for our brains to get get get wrapped around. But it's also has another impact that we're seeing, and that is that the businesses that are competing, of course, as our businesses each compete in the pool and the talent pool and the challenges we face at this time of year not producing this at the beginning of 2022 when we've got a lot of not only supply chain challenges, but resourcing problems and a lot of competition for talent in our businesses. You know, building our business in tech savvy ways and leveraging the the technologies such as AI, of course to help us to be more competitive. Those also tend to attract a certain type of talent to our to our companies also. So it's a it's important part of building our representation, a reputation and our branding. But I want to talk about But the other side of it, right, so that sounds like you know, roses and apple pie and motherhood and so forth, right? Everything's great. Turns out, of course, as we all know, AI has had its failures. But I found that particularly interesting view of this from the I triple e.org site, they were describing some AI failures. And it's interesting, as you look at the different set of failures, typically, it's it seems to be a lot in some of these areas where, where there's, you know, AI is being used in these use cases that are, you know, pushing the edge and the envelope, which, of course, is what we should be doing for our r&d. But you know, when I think about AI, my focus is more on how can I apply it to benefit my business, right, to improve my customer service to increase my revenues, my profitability, my efficiencies, and so forth? Well, it turns out that a lot of these are fairly r&d centric, use cases, that IEEE sort of points out not surprising, given that it's IEEE but I'm just gonna point out some of these right, and then and then take, hopefully point out some takeaways. Number one was brittleness, they felt like AI is quite brittle, especially this was in a computer visioning use case. And that as as the as the world of the imaging kept changing, then they had to do a lot of a lot of AI rework so so brittleness of AI models, in some use cases, that's That's true statement. So is that a failure? Well, it might be, I guess, right, depending on your use case, number two, the embedded bias problem, I touched on that earlier, especially when I was having a conversation recently with a couple of the organizations that I've done some interviews with, so I'm not going to drill more on to that. This is something that we have to be mindful of, as we're building our AI models to help our businesses. Number three, catastrophic forgetting, that's an interesting phrase right there catastrophic for getting in here, they point out the double deep fake problem, right. And, and here, they're discussing how, you know, bad actors in the market, who are doing this deep fake work. And, you know, organizations trying to compete against that the constant retraining of AI models to include not only new deep fake techniques, but also the need to deal with previous and older styles of deep fake. This certainly is critical. Is it a failure? I don't know, I kind of look at that as the cost of doing business conceptually, right? That if we're in that world, where we got to keep figuring out which we do, how to address this deep fake problem. But nevertheless, AI models are, you know, if you don't include the data in your retrained model, then yes, it can, quote unquote, forget old stuff. So it's not like our cognitive brains were well, I guess depends how old you are. Then you don't forget things as humans, right. Okay, number four explainability. This is a challenge with AI. And in particular, it's a challenge as it relates to giving X explanations for results. And predictions can sometimes be be difficult to do, right, where there's some of these questions. And predictions that come out are hard to to explain how it is that this was arrived at. And as a result, I'm going to give a suggestion on this here in a moment to help address some of this stuff. I think explainability is a real challenge for so is it a failure? It's it's it's a challenge for sure. Number five Quantifying uncertainty. So hey, here we go. Once again, having sufficient data sets that deal with fringe or edge use cases is critical. This is a, I guess, maybe a failure in the sense that sometimes as humans, we don't do that. And we focus on the Happy Day scenarios. But turns out, our brains and real life have to constantly be dealing with all the other fringe cases as well. So it does put the onus on us as business owners to make sure that we're collecting as wide a set of data as possible. And that not necessarily means more and more data means more coverage of you know, the different use cases of data out there that we have number six common sense this was a failure, the I triple C I triple E was pointing out common sense meaning turns out AI lacks common sense. Sometimes us as humans lack common sense in any event, the ability, of course, to reach some logical, acceptable conclusions, right based on our, you know, vast understanding and context of everyday knowledge. You know, a lot of times we take that for granted and well AI doesn't really have that at least in our current current maturity levels of AI, we just don't have that. as such. I think one of the things that I've seen and how I tend to view it as I work with organizations is this AI stuff really should be looked at as augmented intelligence. So the bottom line is, Do not throw your brain away, right this stuff is, is AI stuff in our Yes, we should look at it as it's going to be bringing insights Bhutia, challenge it, right. And so when it comes into us with some insights, we should look at in terms of the realm of possibility, as well, turns out one more number seven terms of failures, what I triple E was pointing out was math. He said, Well, look, you know, simple number crunching tends not to be handled so well with AI. So in addition to not throwing your brain away, don't throw away your old calculator, right? Or, of course, all of your old, you know, sequential linear software, right? That's doing real stuff today, running your business in the economy and such. So AI's got its place, and these seven, quote unquote, failure areas, I think, have that I think there's some some techniques that we can use to get around some of it. Not all of it, but some of it, but what it does is it pushes us into a set of use cases, where, excuse me, we can still get AI value. So to get the best insights from your AI, I think the net net is somewhere in this area, a letter A, I decided not to do a number one, letter A define your Chris questioning, right, which drives focused AI model preparation. So it means we got to think we got to think about the business and not about the technology. As we're as we're organizing, what we're going to go do with AI that has has to happen. Be not number two, but be I guess, here we go be. So when it comes to data, be prepared to continually add to your data set and rebuild your models, this notion that you know, build it once and it's done kind of thing that that needs to go away. And then see, use AI like an augmented intelligence tool, as I pointed out earlier, right? So use an AI platform as part of this that can take your feedback to influence the AI model. You are the brains. Alright, and so as you're getting some insight or predictions on things to do, be sure to come back and inform what was the impact of that was a positive? Was it negative that do what we expected? We need the AI models to continue to be informed and learn from this. Alright, so AI does a great job identifying insights on correlations, of course that are difficult for our brains to see, but we should review it as business owners within the context of our business. Right, we got to keep that in mind and evaluate the veracity of applying that insight to our business. I had to get the veracity word in there just shouted intelligent earlier look at the very the can we actually take this insight and apply to our business? Right? That's really what we're after, not just to have the, you know, the aha moment. Oh, that's great insight. But hey, can I make some concrete adjustments, it's going to change my business and actually bring some bring some benefits about so a I want its approach approached right, using the techniques that I mentioned above my experiences, it really can't help our business. But the industry is also gathering context on the failure scenarios. And so of course, we're going to be best served if we take that into consideration, no doubt about that. So I was looking at something else here. medium.com, you can find some interesting blog, articles and such out there. It pointed out, okay, let's say that, is there a single point of failure for AI. And I found this to be an interesting point that was made here on this particular piece here. Is there a single point of failure for AI and there was this blog on medium.com. That suggests there is and as I reviewed it, I thought, Hmm, interesting. So this piece suggests that to get started with AI, you should not choose a company wide AI implementation, but rather a proof of concept that gets the company accustomed to the new normal, right in my experience, the new normal includes the following activities. These are going to sound like the ABC items I just mentioned. I'm going to use 123. Now just switch it up number one, alright, the new normal if you're going to go apply AI Think about doing these things. Number one clarity in the questions, the business questions or seeking from the AI. Sound familiar? Number two, data curation doesn't have to be perfect, but you got to have for thought and organization on your data. And of course, its potential relevance to the questions you're asking. And number three, a mindset of iterative AI model refinement over time. Alright, so I think I've said those three things several ways in this episode. All right, so let's get to the point of this particular piece here. Is there a single reason for AI project failure? And you hear all sorts of stats out there, right? Maybe half of the projects fail? Or some say 85% of the projects fail? Is there a golden thread? According to this particular bar, blog, or article? They stay? They say, yes. And the answer is expectation management. Right? So organizations have of course, said amazing things to me about what they expect from their AI. This has to be managed early in the process. So I agree with the sentiment, right? It's certainly not as quantifiable, right? It's not to say, Oh, the, you know, common point of failure is you didn't have clean data or enough data or whatever. Certainly, the best practices are we got to have that data and etc, etc. But expectation management is really an interesting point on that. And I agree with that. As I've reviewed this thought about this right, I've developed a guide for smart steps to business outcomes with AI. If you're interested in that, reach out to me click ai radio.com Just join my email list. I'll make sure you get get that to you. Hey, everyone, thanks for joining and until next time, then edge manage manage your AI expectations to achieve the results which are there to benefit your business using AI. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.  

In this episode, we take a look at some simple steps to protect the privacy of the data for your AI. Welcome everybody to another episode of click AI radio. Well, certainly data privacy has been on the minds of a lot of people and organizations and governments and governments and institutions, and so forth. No surprise there. One of the things though about AI is that, in general, it's not spent as much time if you will, putting a focus on that area. And it's been a bit of a problem and will become more of a problem if we don't do something about it. As we work with our application and use of AI itself. Now I was looking at several different groups and what they talked about and what they felt about it. At the end of this episode, I'm going to throw out two steps that I have seen that help us mitigate the challenges around this right to help to prevent some of the challenges of slippage, if you will, of getting the privacy of people's information out there that that shouldn't be now to frame that up. I want to introduce a framework for it. One of the one of the blogs, I looked at those, it was called beware the privacy violations and artificial intelligence applications it came from comes from is a see if I can say that is aca.org. There you go. A blog from there. Anyway, here, I'm gonna read an interesting quote here, right said, Look, artificial intelligence has been no different when seen through a privacy by lens design lens, as privacy has not been top of mind in the development of AI technologies. Yeah. All right. So end quote there. I agree with that that has been true. In fact, a lot of our efforts have been, can we just prove the viability of this technology in terms of helping people, individuals, businesses, certainly, there's been success with AI. And there's been some challenges. Now, they what what's introduced here is three interesting pieces to consider. When we're looking at privacy, one of those has to do with what's called data persistence. All right, so put your nerd hat on gonna be nerdy here for a moment, data persistence, that means the data existing today will last longer than the human subjects that created it. All right. And that's of course, driven by things like low data storage costs, and all the technologies that are available to allow our data to live a lot longer than us as people. So that creates a potential privacy challenge. There's another data privacy challenge. It's called Data repurposing, and that is, data that was originally created then gets used in ways that is beyond what it was originally intended. And AI is data hungry, we'll use that up, suck that up. Alright. And the third sort of area around privacy includes what's called a data spillover. And here's where, and this happens a bit in it actually drove a lot of the GDPR stuff, right, which is data collected on people who were not the target of data collection. And so of course, driving out of it was things like, you know, GDPR, out of Europe, certainly CCPA out of California, all of those things drive to or point to the need for having some regulation around it. Now, it's one thing to have regulation that's entirely different, to enforce it. And some of that comes upon us as business owners, it means that there are few things that we can and must do in order to protect the privacy of people's data and their information while still delivering value from AI. And that's that's certainly the balance that we're going for. One of the primary concerns, of course, with AI is its ability to replicate or reinforce or even amplify harmful biases, right? And this is a challenge because those biases can proliferate and then end up driving insights and and recommendations and predictions that of course, take you know, are wrong, right have have this human bias in it, there's there's another challenge to that we have with AI, let's say that we're going to try to fix that or solve for that. One of the problems, though, is that a lot of our auditing methods used today are based on the fact that something has already occurred, meaning in this case, with AI, that makes it even more difficult, right? Because it means that I've created an AI model, and I'm starting to then employ recommendations, insights and decisions, ways in which I work with people or deliver solutions, all with the incorporated bad behavior already. So it's kind of late, that doesn't mean that we shouldn't do the audits on the data. But they're post deployment by nature, meaning I've, I've already deployed it. So what we have to figure out is to find a balance, right, with privacy, as well as AI progress, it's finding the ability to say I'm gonna, I'm going to grow my my data usage in a way that that protects the privacy of the individuals involved. But I also need to allow AI to move forward and that right, there is, of course, a challenge that we're looking to pursue it some groups use consent today, right? It's a get someone to consent that they you know, you can be happy to share your information with them. That has some challenges with that those consents, not always as powerful as a tool as we might believe. And there's been examples where consent has been still misappropriated, right, what we thought originally what people thought originally was consent for certain use, then there was spill over into other areas, meaning people, you know, people didn't know their data was being used by AI for other purposes. So again, even though consent might be there, and organizations or people are well, intending, controlling, controlling the the, you know, the the boundaries of the consent, and enforcing that still is a real challenge, and relies on a lot of people to manually handle that, which means that there's more opportunities for us to mess up. I was looking at a report from the Brookings Institute, they were talking about AI Governance Initiative. And most interesting, there's this is some legislation that was pursuing this balance of how to pass privacy legislation, while while still allowing for AI to do the kind of work that it needs to bring about some of the benefits to humanity that we feel that we can do with this with this awesome technology. One of the techniques that that is mentioned here in this Brookings Institute report was, and you've heard it before, is all around what's called algorithm clarity, right? It's having clarity on how the algorithms using your information, right, that seems to be a useful piece to help dealing with it. But one of the problems with that is, you know, as an SMB owner, is that it ends up giving though, the burden typically on the backs of the of the SMB owner to say two things, one, I have to I have to make things transparent, so that my customers are aware that some aspects of their business information is leveraged by AI, right. And so that's, that's sort of the first incumbency is to say, I'm going to tell you, here's, here's what, here's what we're going to do with your data, it's going to be, you know, used in AI, and to draw the line on what information is not used. Okay? So that's what kind of comes out of this, right? It's, it's a, it's an activity or, you know, being forthright with, with our customers, on on what the intended use of the information that will or will not be used right in to draw that line. That's the first sort of consideration in algorithmic clarity. The second consideration, though, is explainability. Right. So that's, again, where you let your customers know what kind of algorithms are being used. Right. And this may include, you know, access to a human to provide that clarity, you know, okay. The thing I struggle with this, and of course, I that's all well and good. The thing I sometimes get challenged with here is what the heck does it mean to have one of the people in your team explain? Oh, yeah, you know, we use linear regression, or we use a particular classification model or, you know, a Bayes model, right, etc. We used all these different machine learning models and algorithms to that's what good is that right? 99% of people are gonna be like, What are you talking about? How did that help me understand any better? So this area of explainability, right, so the transparency Hey, we're going to use this kind of information or Not this kind of information in terms of the AI. So being clear with your customers on that. And then number two, coming up with a well Set Description for the kinds of algorithms being used, the, here's us maybe a simple way to think about it, you can break it into two buckets, right? When you're trying to explain AI to your customer say, we're going to be using this set of data for our AI algorithms. But we want to let you know that there's sort of two major areas, right. And what I'm gonna say here doesn't apply to all of AI, but for sort of vague, you know, AI for analytics, then then this applies. And it can be simply this. For those kinds of AI problems, where we're trying to determine yes or no answers, then we use what's called classification models, right? So it will be good for us to sell you this product or that product, yes or no? All right, classification kinds of problems. Then there's the other kind of problem, which is we'll use some AI algorithms to help us know, what might be the right price range, right? Now, of course, those are called more regressions, style algorithms. But you don't need to say that. It's simply we're going to use AI to help us understand yes, or no kinds of answers or questions to, you know, answer the questions. And the others, we're going to use it to understand, you know, proper pricing, perhaps right? Things that are not necessarily yes or no, but but degrees of difference, right? Those are two major buckets. And we can work on developing pretty simple language to explain this stuff. Otherwise, what good is it right if people can't, can't get it? Alright, I want to just point out one other thing here. So let me just summarize. So there's two things I think that an SMB can do to apply AI. Alright, so and to help solve for this problem of data privacy, after looking at and applying AI in multiple situations, or excuse me, over many years, come down to these two, two steps. They're a bit overly simplified, but I still think that if you print these out, put it on your wall, it could save you some real pain. Alright, so here's the first one. The first one is where I've seen a fair amount of pain. And this will sound really overly simple. First one is this. Start your AI journey with vetted questions and data oversight. Like what? Alright, I'll I'll come back to that. Number two, apply AI using smart steps. What? Alright, I'll come back to that. Awesome. Alright, so let me talk about number one here for a moment. So uh, number one here starting your AI journey with vetted questions and data oversight. It seems like an obvious first step, but when you examine the case studies of AI failures empirically, it looks like you know, this step has either been skipped or was not given the proper waiting look, a key technique here is to first vet what while you're doing this is to first bet by leveraging an independent party or going under NDA with someone, but what you want to challenge is the intended question you're trying to address with AI? Right, some of the biggest missteps with AI has been? No, that was the wrong question to be asking, this is the wrong use case to be pursuing right? There was inherently something that was either, you know, prone to lots of bias or was an unethical use of AI. Right. So in this step, the anticipated questions for AI. I know that sounds so simple, but it should be written down and evaluated in the context of the impact to your customers, and to other interested parties and to humanity for crying out loud, right? Just stop and do that simple stuff. I know, it sounds so doggone obvious. But alright, now, what does it mean to vet your AI questions? Well evaluate the AI implications to your customers, as well as look at some of those three elements that I introduced earlier. In other words, hey, will there be data persistence? What does it mean for the data that I'll be collecting to be in existence longer than the humans that created it? Right? What does that do in terms of in terms of privacy impacts or data repurposing? Wait a minute, are we going to be using the data beyond its originally you know, imagined purpose? And if so, you know, what obligation do we have to the people of all and number three data spillovers, right? Ask the question. Wait, are we collecting data on people that were not targeted those with whom we you know, initially intended? So stop and ask do I have the right question and then And then also, what's the impact of these two these three AI privacy areas of data persistence, repurposing, and data spillovers? All right, that's step one, stop and do a little vetted questioning, right and data oversight before you get too far. All right, number two is this. This in this one I call applying AI using smart steps. What this means is to iterate on the AI model and continue to refine and refactor and rebuild it as more as learned. What that does is it allows us to even adjust our AI models that might have some bias that we discover, right? So what it means is to build your vetted model, learn from experience, and then evaluate the impact to your business, your customers and then iterate. Right. So there's a book that came out not too long ago, it's by Bernard Marr. It's called "Artificial Intelligence In Practice". And what he's got in there is he's got 50 company use cases where AI was applied. Now I just want to pull something from that book, that's interesting. He goes, in that book, there's this one use case about Alibaba in China, right, who ultimately built a virtual platform that mimicked customer behaviors. And one of the reasons they did it was because it would take too long and too much effort to continually refactor their system. And so this virtual platform is used to allow the AI to continue to be refined and rebuilt and refactor. Now, you know, to do you know, to do lots of model rebuilding, in some situations, right, that's really heavy effort to do. So you either gonna have to put in the extra effort upfront to really ensure that you got the data privacy problem solved for or it well do that. And if you're able to then do the smart steps, which I found really helpful, which is, take the model Build it, try to apply it look for where a bias might be exposed, or privacy might be exposed when you didn't expect it. The lesson is this. Adjust your mindset as a business owner to refine your AI model over time, right take into consideration the changes in context, the changes in the economy, as well as lessons learned. And put in your mind that part of doing AI means that that will you know, continue to refine and improve and rebuild this AI model. Now when you combine these two steps, right, this first step is the vetted privacy aware questioning, right and looking at your data, as well as the mindset of smart steps where you simply refactor and approve you and model over time. What I found is that if you do those two things, you're in a much better position. For better privacy AI data privacy considerations. It puts you on a great path for both near term as well as long term viable business impact, you know, to your organization. Alright, everybody, thanks for joining and until next step. Until next time, use the two steps to ensure privacy for your AI that brings incremental business growth. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.  

In this episode, we take a look at how AI turns your sharing into business growth. Grant Okay, welcome, everybody to another episode of ClickAI radio. So I'm very excited to have here with me today ShareThis business development leader. I think it got that right. Michael Gorman, business development leader. But before I go any further, Michael, would you introduce yourself? Michael You bet. Grant. Yeah, it's great to be here. Like you said, I oversee business development, but also product and marketing at ShareThis. I've been I've been with ShareThis for a couple of years. In that role. I have a background in data, really, data and analytics has been my passion. Also media and marketing sort of themes. I've worked for big data companies like Axiom, I've worked for an email marketing leader, digital impact, they got bought by Axiom. That's how I got there. And I've also worked for big consulting firms. And for ESPN back in the earlier days of my career. Grant Oh, wow. Could you maybe give us a play by play? I bet you could write ESPN. Interesting. Wow. Michaels It was a fun period. I was like years eight through 11 of the history of of ESPN, which, so is a fun time to be there. Grant How fun. All right. And he did some some consulting roles as well. So data and analytics, huh? Yeah. Right. All throughout all throughout the career. So what led you into this work was ShareThis what was it was the journey there? Michael Well, one thing is that, that I've worked with our CO CEO on the past, at axiom, so we knew each other, but ShareThis is a really, really special data asset. In a lot of ways, and within the world of the of the advertising that I've worked in for quite a few years. It's it was well known. So when I had an opportunity to do a little consulting for them, I jumped into it. And that led to the to the role. It's a Yeah, sure this is, you know, it's Well, shall I tell you a bit about the company? Or is that? Grant Yeah, yeah. I mean, yeah, tell me a little bit about how it got started. And its purpose and sort of the vision of it. Michael You know, well, like a lot of companies, it started with one purpose and, and things evolved a little bit over time, it, it started off in the early days of social networks, when Facebook was still a new idea and mind MySpace was, was beginning to slow down, it was with the idea of making it easy for any website to make to make it easy for their users to share content to all the social networks that they might have an interest in. And so a developer with a simple, taking, you know, taking a piece of code and pasting it on their website that they could then have sharing. We and so it was one of two or three tools that really started in those early days and became a leader in the space. We actually have a how to still maintain a trademark on that little little V on the site there. Yeah, I mean, that's what you're known for. Yeah. So it's a sign if that's there, it's a sign that sharing is you know, sharing tools are present. It's essentially the balance value for the for the publisher for the owner of the site who doesn't have to does no work to have sharing available will get some analytics as a result, sharing is valuable because it makes it attracts more people to the site new users more more content. And, and so it's it's grown up naturally. And we're, you know, so really well established. But a number of business models were tried over the years, but but about five years ago, we started focusing, moving towards being 100% about our data, is that really as a special asset, we have around 3 million publishers using us sort of our live arm 3 million now, that's been pretty stable, you know, half to three quarters and in the rest of the world, a quarter in the United States, a little biased towards English language, but we have every language in the world represented among the users on the sites. And, and so that data and and we'll talk more about this when we get into things like, you know, the the technology in the AI. Yeah, but we're really just, you know, it's like a window into what, what people are what's on people's minds? What are they looking for? What are they searching about online, and we can, you know, discern trends and also, you know, make sure that advertising is more relevant for for users. Grant So I have a question for you on that. So you've, of course, are familiar with the terminology of neuromarketing, right. And, you know, as a way of sort of tracking, how are people interacting with a site, right, and where do they go? And where do they point and click and, you know, there's organizations that look at, you know, extracting what the user is doing on the site, this feels like this starts to come into that world right that day. I mean, I don't know that it's tracking every single movement, but it's tracking, obviously, the event of I want to share something. Any thoughts on that? Michael Yeah, that's really interesting. I mean, there's a lot of different ways to make inferences about about people, we tend to focus a bit more on the on the broad, the broader picture, that the thing that's that, I mean, there's, like you say, so many choices. But the thing about online content is, it's very rich. So when a person visits a site, there's a lot of things there, there's a lot of things on the page they're looking at. And so what we've really focused on is using the page as a source of clues about what a person is interested in, we also might look at the link in and out of the page, and get a clue from, say, a search term as well, that's a that's useful, and clearly when someone shares, you know, content that's that that sort of zoned in on exactly what they care about on the page. But we've opted more for the broad picture of focusing, you know, taking all that richness and attributing some probability of interest that for you, for user to the things that are on the page. And that way we can we have just such a broad, you know, broad palette to work with. And I think also from the point of view of, of, you know, user consent and user experience, it means that what we're actually collecting is is relatively light, it's just that this user was on this page at this time. And any inference we make is not based on what he or she did, or how are their eye movement, there's no no personal collection, we just have the that event, and we get all the all the power. Grant So it's when they were there. Is it anything about how they got there? Or where are they left? Michael Yeah, exactly. We do. We do use the inbound links and outbound links when we can get them. And that sometimes, as I said, yields a search term, those can that was sort of part of the of the link the part of the information that what came with the user, you know, the referring search term or so that so there's some some useful data there as well. Grant Yeah. So so when you collect this, and then that's got to be a massive repository, I think I saw somewhere else and I'm looking at, was it three terabytes of raw data and 100 million keywords in 200 languages a day? Is that right? Michael It sounds roughly right. I haven't counted it lately. But, yeah, you're right. But But yeah, we we see about half a billion, you know, unique, what we call events, something, you know, something happened at a point in time, visits a share per day. Grant This is a grounds for, you know, a playing field for AI, right, just you have so much data. So tell me what it is you learn from it with the AI, right? What kinds of problems are you looking to solve? As you and I know, when we pursue AI, we, it would tend to be better served if we're going after a particular question or thought in mind. Now, obviously, we get surprised with AHA insights from Ai. But going intentionally after something makes a lot of sense. Can you give a scenario the kinds of things that you're looking for? Michael Well, the I would say that the theme that has worked for us so far, is to try to do is to focus on being the able to represent and reflect human interest, what are people interested in? And yeah, and so. So we, we use, and I guess where the AI comes in is that we use the latest techniques of language analysis and language modeling. So we capture all of the linguistic content on the page and then we represent it in a number of ways. What are all the prominent keywords? What are the what are the entities that are you know more that are Unusual, you know, a brand name, a celebrity name, a business name? What are the what is this page about the concept? Or what are? What are some of the concepts that accurately describe what this page is about. And then we have some standard categorization techniques are basically a taxonomy of topic interest topics that we we screen for, you know, and and it's not, it's not a yes, one of the nice things about this is it's not a, a, it's not a, it, we don't have to decide one thing, you know, we were able to say, all of the prominent keywords, and all of the interesting entities and several concepts and all the categories that this page is about. So it could be a page, it's about, you know, mountain climbing and and what shall we say? And Utah, and the, or the American West and, and road vehicles? And, you know, and beverages, you know, skiing or whatever? Right, right. Exactly. Grant Yeah, so some form of an ontology there, right, that allows you to sort of connect these together? Michael Yeah, we used a number of techniques that you said, One is, we built a custom ontology, using relative and you know, we're, we're not a huge company. So we, we try to wherever we can do something open source or free as the entry point we do that. And so we, we use some Wikipedia, it's slash DBPedia is a source for us. And, as is some Google free offerings that help us sort of the provide the raw material for building our customer ontology. We've also take great advantage of some of the latest open source language modeling tools. One is when it goes by the name of the Google released one, I forget what the what the acronym stands for, but one that's called Bert, and then more recently, one that's called Muse. Yeah, we use muse. Okay, that, that allows us to represent anything, either a word or a sentence, or the whole page as a as a set as a vector of 500 numbers. And if two pages have the same values for those 500 vectors, then they are about the same thing. Yeah, you got you have some affinity there right now, even though in practice, they might be in different languages use totally different, you know, different sets of words, but they're still about the same thing. That's, that's, that's really, for us that technology has been a real breakthrough. Because it's we've been sometimes keywords and can be very, you know, they can be false positives or No, yeah, negative. Grant I mean, there, yeah, there's nothing that governs some, you know, webpage designer to, you know, say, hey, are they using the actual right keywords? Right? Michael Yes, or even? Or even? How do you a lot of words have multiple meanings? How do you disambiguate to get the right one? Yeah. So this this, embedding technology, this Muse model helps us do that. And then Facebook is given we use a tool, they think it's called Facebook. Ai similarity search. Yeah. And both of these are open source tools, y'all you have to put in the effort and have the knowledgeable people to master their use. And that allows us because great, it's great that you've now got all these numbers you can compare, but that's a lot of numbers. That's you half a billion a day, you know, and we have we see 600 million unique pages every month. So so how do I great, I want to rank the 600 million pages to see which ones are most about skiing in Utah. Yeah, that's, you know, how do I do that quickly, and then and affordably? So fate, the Facebook tool helps us a lot with that. Grant So let me ask you a question that So so far, you've been talking about leveraging AI technologies to help you get your arms around that sheer volume of data on a daily basis and to try to extract some meaning and semantics and understanding from it. That's a good point that's on the side of ShareThis and the benefits to ShareThis. What about it from pivoted to the other side? What does it mean to it is, you know, I talk a lot with small medium organizations, how does that benefit them? What takeaways or values come over to help them through something like that? Michael Well, what the I mean, the industry that we started with, is was is advertising and programmatic online advertising as a place where we make our solution available. And so we were at this point, probably the leading source of the ability to target ads based on interest. So if if A small business were doing online display advertising and they went to Google's, if they use Google's platform or trade desk, or any of the major platforms, and they searched on, I want to find people interested in skiing in Utah, our data would be one of their choices to find that. And so it's designed to provide a broad set of individuals who in the last 30 days have shown some interest in that topic. And it could be, you know, it might be at the level of skiing, and they might, then they might, but but the nice thing about it is that we we've, I mean, it's hard, this is harder for the stats, that's what's available for the smaller business. That's, it's, it's right off the shelf, you can, you can use $1 worth or $10 worth or $100 worth if it works for you. But then on the big company side, we use some of those tools I talked about for is, well, what if, what if we don't actually have ski in Utah, we just have skiing, right? Well, we well, for an advertiser can can say, well, I need to skiing in Utah. In fact, I need to, you know, skiing in snow. But what is the alter? You know, we can create a segment using keywords and, and topics that is just about that is exactly what they need. Grant So if I were to look at maybe an advertising opportunity, leveraging, you know, this great insight that you have, does it allow me to target specific demographics, specific locations or locales? So like, you know, you're able to? Michael Absolutely, it's pretty much anything you could, I mean, because every kind of website needs sharing, we have our, our customer base, our base of publishers use our tool is pretty representative of the internet as a whole. And so if your interest is travel, we've got sites that are about, you know, traveling Las Vegas, traveling to Europe traveling to do outdoor activities, if you're interested in financial products, we can we can find things, you know, content that relates to whatever be at a mortgage or or FinTech to know. And we we represent those in about 1500 standard audiences that we distribute every day. And every day, the nice thing about our data, compared to a lot of datasets is we refresh it every day. Yeah, Michael I mean, it's every second, right? I mean, yeah, it could be, you know, people talk about real time, and we were always looking for people who've got a real time use case. But yeah, at this point, the the most frequently we refresh for a client, the customer is up by a by his hourly. Grant Oh, it's hourly, okay, that's, that's still really up to date. Yeah. I mean, if you had hourly insights on what the what's in the mind of people are the consumers that's really fresh data? Michael Yeah, yes. Yeah. Yeah, one of the areas that we that we are moving towards is trying to go beyond advertising and inform other activities like demand forecasting, you know, how much should we order for a store in a given location? Well, our data about how much interest is being shown on the products of that store, and in that store, in that area, we can sort that way, and provide that as an input. Grant That makes that makes a lot of sense. You know, there's, there's some retail organizations I've worked with with AI. And obviously, it always comes back to or not always, but most of it comes back to the supply chain, right, getting further and further left in terms of their their demand forecasting. And if they were able to understand you know, where that interest lies, it does almost gets to, oh, I know, this is a stretch in terms of language, but it's kind of a sentiment analysis, a play on that. Right. It's the ability Yeah, the ability to say I understand what the sentiment is in terms of where their interests are. And if I understood what that was, in terms of particular set of products or other things I'm offering, and I could get that further into my, into my supply chain, that would be really valuable to Yeah, Michael I mean, it's nice that you mentioned that we do we do actually score the sentiment of the content on the page. So we're sentiment is useful, either to only talk to the people who are in favor or opposed or the middle, we can we can build an audience that or provide that as a data element as well. Grant Yes. See, that's that's powerful to understand the the sentiment of the page itself, even how people are talking about it, or what they're doing, have you ever ran into the ability to use it in terms of IP tracking, right. So in other words, if there is an organization that had a certain set of IP and, and and really, yeah, they felt like oh, my IP, I've lost control my intellectual property, it's showing up in other places. Michael Oh, that's interesting. You know, I was thinking of I was thinking of the I the the IP address the Internet Protocol address. Yeah. Should have been more clear. Yeah, I'd love to answer that question. But that wasn't what you were asking. Well, yeah, answer. Oh, we'll start with intellectual property. Yeah. One sec. Regarding intellectual property? You know, we have it. Let me think about that. Let me give you the scenario. I had, one of the things I've thought about that we haven't taken on it, you know, is that is, is using using intellectual property as a data set? Yeah. If if we were to, to read to do the same kind of analysis I talked about earlier on trademarks. Yeah, it could mean be the means for discovering which, what sites were about branded products by seeing the correspondence between the trademark and the, because that's always you run into difficult How do you tell something's a brand? When is Jaguar a brand? You know? Exactly. Grant Yeah. Yeah, it's a fascinating problem. I had a company reach out to me and say, Hey, can you develop something in this area, and we did some work on that. I called it smart catch, but they were looking to protect their IP, their intellectual property, which was, we've got this corpus of information. And, and we've got others that are, you know, getting access to it and are promoting it, you know, elsewhere out into the, you know, online universe there, or metaverse. And, and I want to be able to discover, you know, when it's opportunistic, and you can use, you know, SERP and other technologies to try to find some of that stuff and do lots of scraping. But that's got its own challenges in terms of a solution. And where you've got this opportunity to listen. Right, right, to observe what people are sharing and to the to compare that against a corpus of protected material, right? Michael Kind of an intro you're giving, you're giving me a product idea. Seriously, one of the things that we've done this year, is to create what we what we call, you know, similarity scoring. So similarity, and that's gonna cause Yeah, you can literally give someone who was curious about the dispersing dispersion of intellectual property, give us a domain. Yep. And, or a, you know, the piece of content that describe their, their stuff, and we would rank our sites for which ones had it most. Right. And, you know, whatever the top 100, you know, and you know. Grant What I found interesting on that, when I built the initial piece on that was that I found that, in some of the discovery, in some cases, what I found was a foe. And in other cases, it was a friend. Exactly right. That, you know, okay, just because I found it doesn't mean it's an enemy. But, but it might be, and so you want to then notify them? Is this? Is this someone that's an ally or not? Anyway, interesting thought? Michael Because I think I think that sometimes there is a, you know, I don't know, there's a presumption that fraud detection or a bad actor detection is, is, you know, worth more, etc. But I do find that in a lot of cases, the pro cases are actually, you know, sometimes you just by suppressing something, you do more yourself more harm than good. Yeah. Yeah. Right. Right. That's another I wanted to touch on the other meaning of it. Yeah. Yeah. Now IP address. Yeah, yeah. So So an IP address is one of the four or five things that we capture for each case. And there's a lot that you can tell from an IP address, like, it can be translated into a location of origin, we approximate we resolve that to within half a mile. So that it's still relatively privacy compliant, and you know, not too revealing, but it certainly helps understand, you organize the data by where it's coming from example. And so the one use that is, has been an important one for us is business to business. So we, we have a number of the major companies that are in the business to business world license our data as one source where they're able to see people from a from an intellect Internet Protocol address that is owned by or been associated with a particular company. Oh, and then see what sites that that IP address is showing interest in? Oh, it just can be. Yeah, so it can be a signal that oh, it seems like you know, Chevron is interested in a new CRM system because they're you know, there's there's a big spike in that kind of traffic Awesome. Yeah, that's awesome. Yeah. Talk about so almost like a lead management. Yeah, solution for sure. That's, that's powerful. Yeah, to do that. that. Oh, there. Yeah. And that's yeah. And IP in general, I think the location implications are a really well, it's how I can, how we can do that demand forecasting I mentioned earlier, it's about looking at the origin of the data. Grant So some of the AI solutions that I've built take into consider location. So So in other words, okay, but in what I've been doing is more around, oh, some transaction occurred? Where was that transaction initiated? From? Oh, this, you know, here's the IP address. Okay, I know that where they are on the planet. Now, tell me what the context of what's taking place in you know, at that location? What is what's the weather like, right, what are other events that are taking place in that location? And then then use an AI to help draw inferences on, you know, to what degree are those factors affecting it? It sounds like you might be doing some similar things with that Michael I well, I think we could be a great contributor to any solution that was along those lines. I was adding that dimension of what are people looking at? What are people interacting? What topics? Are people in this location more engaged by then people in general, fascinating those comparisons? Grant Yeah, it's fascinating is okay. Very good. All right. So let me ask you on. Okay, so we've gone from the the big corpus of what you're collecting on a daily basis, or hourly, actually, hour by hour. And then we talked about the impact to, you know, maybe businesses organizations, when when is there a particular case or outcome that you feel like you could talk about some specific example where some organization used the advertising from that? What you did, and it had this sort of impact or effect on them? Do you have any sort of case study like that? Well, it's, Michael I guess that some of the ones that are coming to mind, I think, I mean, there's some of it's very straightforward. Yeah. An advertiser, like Western Union, is looking for people who want to make payments, you know, at a distance, I mean, wire wire transfers and payments, and we offer people showing interest in wire transfer, so that the simple act of being able to get your message in front of people who have recently shown interest in it is the is the, you know, it just doesn't need no explanation. We've taken that though, one of the things we did this year that I'm proud of is we were inspired by some of the events of last summer, to get more try to take a more active role and figure out what our data was good for. Beyond commercially, and, and we ended up creating a data for good part new part of our taxonomy, we call data for good. And so people interested in social justice loving people entered interested in veterans issues people wanted in. And so and those those segments, you know, have gotten are getting a growing amount of usage by advertisers who either, you know, wanting to demonstrate their commitment to the court to a cause, like, or to find or teachers or to, you know, communicate, right people who have concerns of that kind. So that's been one. Yeah. Another kind of it's, it's not in the mainstream of what we do. But we've, I think this data could be really great as a as a resource for educational institutions. So we've actually a major business school has has is testing I've taken a take taken a subsidiary six months of our data, and they're looking at using it in a project that they have to investigate unemployment. So fascinating. How could you How could you see earlier unemployment trends in a in a location or region that could help the for the process of forecasting the unemployment rate, and it sort of feed into it? Because I've, what I've, I think that lots of people govern organizations included, are somewhat frustrated by the fact that, you know, traditional means of forecasting that were invented before there were personal computers or barely work computers. Take a long time, you get to find out that 40 days after the month, what happened in the month, I love both data can be used to generate that much more quickly. Grant Yeah, Michael, that's I love how you're bringing that up. It seems like it has both the opportunities for not only the capitalistic aspects, but the altruistic aspects of this, the values and benefits that can help society and be pulled out of that. I think that's awesome. So all right. I've thrown a lot of questions at you. So let me ask you this, if you will. To direct direct my listeners to where to go to learn more, where would you send them? Michael Well, I would, I would love them to visit our site, because and in particular to, you know, to ShareThis.com, look, look at our news and our, our blogs, we we basically we publish both as you know, as a demonstration of our the value of our data. And and it's just a general service, we publish a lot of educational and informative information about trends in the economy, and, and public interest generally about how to do marketing well about trends in data. So so we we, we try to be a resource for people and I love I'd love people to visit that content, sometimes. Some of the best stuff is is not on on the nightly news. It's like putting some of it out. I could also you know, I can give you some examples. It would be fun. I go right ahead. Knowing that knowing this audience I we are getting a sense of who maybe was listening is interested in the show, I asked our team to identify some current trends. Yeah, I guess as we come to the end of 2021. Yeah. And so so we put these together. So what one is that, that, that while the world isn't, we're seeing the trend of the gradual resumption of events in person events, even though COVID continues to cycle up and down against the backdrop of COVID. So as of August, for example, 77% of advertised events were in person events, there was a period where, you know, year and a half ago, there was there, they basically no almost having anything, it was just shut down. It was virtual or nothing. That's interesting. So as we adapt, we are adapting. And so as you as you think about should I make plans for a virtual vet, should I invest in advertise? Should I invest in participating in virtual event? Yep, don't count them out. Even if you're nervous, you know, they, they're coming back steadily. Another thing, pattern we observed in finance, that again, you know, COVID is inevitably one of the backdrops to what any of us are thinking about, but people are continuing to be engaged with saving money. So, it so as you think about what, oh, you know, what is what's going on in the in the economy? As the, as virus uptake increases, as one of the things to extract is, is increased saving? And so if that's a, again, depending on your business, how that factors in if savings is your business? Yeah. When your could be good, good to you. If if, and then let's see, what's another one? Let's see. You know, we've heard a lot about supply chain issues. And you know, what, but what, if your retailer what a consumers most worried about? When and so the top concern is shortages and out of stock, and 51% a second costs, inflation and rising prices at 28%. And then staffing issues like worker shortages and strikes, 14, and last last of all shipping delays. So it's thinking about communication strategies, what's on people's minds that might make them not come to the store? That sort of thing? So I'm not surprised. Yeah, yeah. So and we're, we're putting out new new stuff of this kind every, every month in the blog. And and I firstly, look, I think we did we have Superbowl trends out, as of yesterday, I think. Grant So it's already started to build right. That's right. That's, that's amazing. So So you gather it on an hourly basis, and then you do the AI on it Michael Truthfully, truthfully, Grant, it's being gathered continuously. Okay, that's, that's what I thought, yeah, I thought we built we build it as it happens, okay. We literally, you know, record a record for each thing. That's, that's, that's filled out all the way with all the data that will that will need eventually. And then once an hour, we some or as we frequently as our we'll sum it up into a distribution and push it to someone but the most people get their get their data delivered overnight. Amazing. It's picking it up on their AWS bucket. Like Well, this is Grant Fascinating. Any final comments as we wrap up here? Michael Well, you know, I guess that I hope I've given you a sense of the I mean, AI is critical to our business. We are you know, we When we started on this track, we were about a 50 person company, we're approaching 100 person company. And so you don't have to be, you know, IBM to use AI AI to build a great business. So it's a combination of finding the right tools and a core of of talent, the right kind of talented people, and you can and and then, frankly, sustained effort over a period of years and you can build a business that is really hard to replicate, without without it, so very hard. Right. That's, that's my thought. That's, that's Grant Wonderful. Well, Michael, thank you so much for taking your time today. I appreciate you sharing your insights and guidance with us today, everyone. Thanks for joining another episode of ClickAI Radio and until next time, go get some ShareThis.com. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.  

Welcome to ClickAI Radio. In this episode I have a conversation with some AI experts on how AI ethics affect your business. Grant Okay, welcome, everybody to another episode of clique AI radio. Well in the house today I have got a return visitor very excited to have him and a brand new person. I'm excited to introduce you to Carlos Anchia. I've been practicing that I get that right, Carlos. Carlos Sounds great. Good to see you again. Grant Even old dogs can learn new tricks. There we go. All right, and Elizabeth Spears. Now I got that one easily. Right, Elizabeth? Elizabeth You did it? Yeah. I'm really happy to be here. Grant This is exciting for me to have them here with me today. They are co founders of plain sight AI. And a few episodes ago, I had an opportunity to speak with Carlos and he laid the foundation around the origin story of AI for vision, some of the techniques and the problems they're solving. And I started to nerd out on all of the benefits. In fact, you know what, Carlos, I need to tell you, since our last conversation, I actually circled back to your team and had a demo of what you guys are doing. And yeah, I think it was very impressive, very impressive, you know, a guy like me, where I've coded this stuff. And I was like, Oh, wow, you just took a lot of a lot of pain out of the process. You know, one of the pains that I saw come out of the process was the reduction in time, right that how long it would take for me to cycle another model through it. That was incredible, right? I can't remember the actual quantification of time, but it was at least a 50% of not even 80% reduction of cycle time is what I saw come through, there's even model versioning sort of techniques, there's just, you know, there's another really cool technique in there that I saw. And it had to do with this ability to augment or, or approximate test data, right, this ability to say, but but without creating more test data, it could approximate and create that for you. So now, your whole testing side just got a lot easier without building up, you know, those massive test cases and test basis for for doing the stuff So, alright, very impressive product set. And let's see, Elizabeth can you explain that? Elizabeth That's right, Chief Product Officer. So basically, kind of the strategy around what we're building and how we build it. And the order in which we build it is is kind of under my purview. Grant Okay, very good. Awesome. Well, it's so great to have both of you here today. So after I spoke with Carlos, last time, after we finished the recording, I said, You know what, I want to talk to you about ethics about AI ethics. And so as you heard in my previous podcast, I sort of laid the foundation for this conversation. And it's not the only areas of ethics around AI, but it's a place to start. And so we want to build on this. And we're gonna talk about these sort of four or five different areas just to begin the conversation. And I think this could translate certainly into other conversations as well. But to do that, could could one or both of you spend a little time giving the foundation of what is AI ethics as a relates to computer vision itself? What are some of the challenges or problems or misunderstandings that you see in this specific area of AI? Carlos Sure, I can take that one. So I think really, when we're talking around ethics, we're bowling any sort of technology, we're talking around how that technology is implemented, and the use of that, right and what's acceptable. So in the case of this technology, we're talking around computer vision and artificial intelligence, and how those things go into society. And it's really through its intended use on how we evaluate the technology. And I think really, computer vision continues to provide value to allow us to get through this digital transformation piece. As a technology, right? And, you know, once we start with, yes, this is a valuable technology, the conversation really now shifts to how do we use that technology for good, some cases bad, right? Where this is where that conversation arises around, you know, having the space to share what we believe is good or bad, or the right uses or the wrong usage just right. And it's a very, very gray area, when we look to address technology and advancement in technology against a black and white good or bad kind of a situation, we get into a lot of issues where, you know, there's a lot of controversy around some of these things, right, which is really, you know, as we started discussing it after the last podcast, it was really, you know, man, I really need to have a good podcast around this, because there's a lot to it. And you clearly said there was a previous one. And now there's this one, you know, I hope that there's a series of these so we can continue to express value and just have a free conversation around ethics in artificial intelligence. But really, what I'm trying to do is set the context, right. So like technology works great from the just the science of the application of that technology. And if you think of something like super controversial facial recognition, now, absolutely. I don't want people to look at my face when I'm standing on a corner. But if there's, you know, child abduction cases, yes, please use all the facial recognition you can I want that to succeed really well. And we've learned that technology works. So it's not the solution itself. It's how we're applying that solution. Right. And there's there's a lot of new ones to that. And, you know, Elizabeth can help shed a little bit of light here, because this is something that we evaluate on a constant basis and have really free discussions around. Grant Yeah, I would imagine you have to even as you take your platform into your into your customer base, even understanding what their use cases are imagined, at times, you might have to give a little guidance on the best way to apply our uses. What have you seen with this, Elizabeth? Elizabeth Yeah, you know, there's, it's interesting what Carlos is saying there's a lot of the same themes for evaluating the ethics and technology in general are similar ones that come up with when AI is applied. So things like fraud or bias is actually more can be more uniquely AI. But that absolutely exists in other technologies. And then inaccuracy and how that how that comes up in AI, and then things like consent and privacy. So a lot of the themes and we can we can talk about how AI applies to these, but a lot of the themes that come up, are really similar. And so one of the things that we try to do for our customers is, especially kind of your listener base, that's that small, medium businesses is take a lot of that complexity out of the, like, Hey, I just want to apply, you know, I just want to solve this one problem with AI, what are all of these concerns that I, you know, I may or may not know about. So, we try to do things like build, build things into the platform that make it so something like bias. So, for example, is usually comes down to data balance. So if we provide tools that really clearly show your data balance, then it helps people make unbiased models, right, and be confident that they're going to be using AI ethically, Grant So that I'm sure you're aware of this Harrisburg University in Pennsylvania case where they ended up using AI to predict criminality using image processing, right. And, of course, that it failed, right? Because it you know, looking at a an image of someone and saying, Oh, that person is a criminal, or that person's not a criminal. That's using some powerful technology, but in ways that, of course, has some strong problems or challenges around that. How do you help prevent something like this? Or how do you guide people to use this, this kind of tool or technology and in ways that are beneficial? Elizabeth Yeah. What's interesting about this one is that the same technology that causes the problem can also help solve the problem. So so when you're looking at your corpus of data, you can use AI to find places where you have data in balance and and just to kind of re explain the what happened in that case, right. So they had a data imbalance where it was Miss identifying Different races that they had less data for. So, you know, a less controversial example is if we're talking about fruit, right, so if we have a dataset that has 20, oranges, two bananas and 20, apples, then it's just going to be worse than identifying bananas, right? So one of the things that that can be done is you apply AI to automatically look at your data balance and say, and surface those issues where it can say, hey, you have less of this thing, you probably want to label more of that thing. Grant So I'll try to manage the data set better in terms of proper representation. And try finding, finding bias is a real challenge for organizations. And I think one of the things that your platform would allow or unable to do is if you can take off the pain of all of the machinery of just getting through this and and free up organizations time to be more strict in terms of evaluating and finally, oh, taking the time to do those kinds of things, I think you might have an opportunity to improve in that area meeting customers might be able to improve in there, would that be a fair takeaway? Elizabeth Yeah, it's something that that we're really passionate about trying to provide tools around. And, and we're kind of prioritizing these these tools. The other one is, is that has to do with your data is as well is finding inaccuracies in, in your models. So the one example is X ray machines. So they did. They've basically they had an inaccuracy in a model that was finding a correlation, I think it was for it was for the disease detection. So it was finding a correlation just when the X ray was mobile, versus when they went into kind of a hospital to get the X ray. And so, you know, these models are in many cases, really just very strong pattern detectors, right. And so one of the things that can really help to, you know, to prevent something like that is to make it easy to slice and dice your data in as many ways as possible, and then run models that way. And make sure that you aren't finding the same correlation, or the same sort of accuracy with a different data set, or a different running of the model in a different data set. So said, in other words, you would be able to say, I'm going to run all of the portable X-ray machines versus all of the hospital ones, and see if I'm getting the same correlation as I am with, you know, cancer versus not cancer, or whatever they were looking for. Grant A quick question for you on this. So in my experience with AI, I have found sort of two things to consider. One is the questions that I'm trying to get answered guides me in terms of, you know, how I prepare the model, right? I'm gonna first lean towards certain things, obviously, if I want to know that this is a banana, right, or an apple or what have you. So the kind of question that when I answer leads me to how I prepare the model, which means it leads me to the data that I select. And the question is, is do I do I? Should I spend the time really putting together the strong set of questions? And rather, rather than do that, just gather my data? And then and then execute that data, the build a model? And then Ben, try to answer some questions out of that, you see what I'm saying that way, maybe I'm not going to introduce any bias into it. Elizabeth So we we encourage a very clear sort of understanding of the questions that you want to answer, right? Because that helps you do a few things, it helps you craft a model that's really going to answer that question, as opposed to accidentally answering some other questions, right. But it also helps you right size the technology so far, for example, if you're doing if you're trying to answer the question of how many people are entering this building, because you want to understand, you know, limits of how many people can be in the building or, you know, COVID restrictions or whatever it is, that that solution doesn't need to have facial recognition, right. So to answer that question, you don't need you know, lots of other technologies included in there. And so yeah, So, so defining those questions ahead of time can really help in sort of a more ethical use of the technology. Grant So one of the first jobs we would then have a small medium business do would be get clarity around those questions that actually can help us take some of the bias out. Is that a fair takeaway from what you share? Elizabeth Exactly like the questions you're trying to answer. And the questions you aren't not trying to answer can also be helpful. Grant Oh, very good. Okay. All right. So all right, the opposite of that as well. All right. So while we could keep talking about bias, let's switch to something that is that I think comes right out of the movie iRobot, right. It's robot rights. Is this, is this a fluke? Or what, you know, is this for real? I mean, what do you think? Is there really an ethical thing to worry about here? Or what? What are your thoughts? Elizabeth You know, in most of the cases that I've seen, it's really more like, it comes down to just property, like treating property correctly, you know, like don't kick the robots because it's private property. So not really around sort of the robot rights but you know, some already established rules be in for the most part, I see this as kind of a Hollywood problem, more than a practical problem. Grant Maybe it makes good Will Smith movies. But other than that, yeah, fighting for rights, right. Now that seems like it's way out there in terms of terms of connection reality. Okay, so we can tell our listeners, don't worry about that for right now. Did you add something back there? Carlos Just an interesting point on the robot rights, right. While while it's far in the future, I think for robot rights, we are seeing a little bit of that now today. Right? When like Tesla AI day, when they came out, they decided that the robot shouldn't run too fast that the robot shouldn't be too strong. I think it's a bit. It's a bit interesting that, you know, we're also protecting the human race from for us building, you know, AI for bad and robots for bad in this case. So I think it's, it's, it's on both sides of that coin. And those are, those are product decisions that were made around. Let's make sure we can run that thing later. So I think I think as we continue to explore robots AI, the the use of that together, this topic will be very important, but I think it's far far away. Grant I'm wondering is that also blends into the next sort of ethical subtopic we talked about, which is threat to human dignity. And it might even crossed into that a little bit, right, which is, are we developing AI in a way that's going to help? protect the dignity of human certainly in health care situations? That certainly becomes important, right? You probably heard on the previous podcasts that I did, I played a little snippet from Google's duplex technology that was three year old technology, and those people had no idea. They're talking and interacting with AI. And so there's that aspect of this. So where's the line on this? When? When is it that someone needs to know that what you're interacting with is actually not human? And then does this actually mean there's a deeper problem that we're trying to solve in the industry, which is one of identity, we've got to actually create a way to know what it is that we're interacting with. And we have strong identity? Can you speak to that? Yeah, Elizabeth I think I think the there's two things that kind of come into play here. And the first is transparency, and the second is consent. So in this case, it really comes down to transparency, like it would be very simple in that example, for that bot to say, Hey, I'm a bot on behalf of, you know, Grant Larsen, and I'm trying to schedule a hair appointment, right, and then going from there. And that makes it a much more transparent and easy interaction. So I think in a lot of cases, really paying attention to transparency and consent can go a long way. Grant Yeah, absolutely. All right, that that that makes a lot of sense. It seems like we can get around some of these pieces fairly, fairly simply. All right, Carlos, any other thoughts on that one? Carlos The only thing there and then touches on the stuff you guys were talking about on the bias piece, right? We're really talking about visibility and introspection into the process. Right. And with bias, you have that in place, right? We can detect when you know there's a misrepresentation of classes within the the model. In some cases, there's human bias that you can get that right but it's it's having that visibility in the same case with the threat to human data. With that visibility comes the introspection where you can make those decisions. You see more about the problem. Grant Mm hmm. Yeah, yeah. So if we were to to be able to determine we have a bad actor, if there's not transparency, that would be a way that we could help protect the dignity of humans through this. Alright. That's reasonable. All right. So let's move on to again, sounds Hollywood ish, but I'm not sure it is weaponization of AI. Right? What are the ethics around this? I'll just throw that one on the table. Where do you what do you want to take that? Carlos, you wanna start with that one? Carlos Sure. I mean, so weaponization and and I think when we talk about AI and, and the advancements of it, you quickly go to weaponization. But really, weaponization has two different pieces to it, right? It's obviously it depends on which side of that fence you're on, on whether you view that technology is beneficial or detrimental. But in some cases, that AI that same technology that is helping a pilot navigate, it also helps for a guided missile system or something like that. So we really have to balance and it goes back to use cases, and how we apply that technology as a people. But you know, weaponization, the rise against the machines, these kind of questions. While they're kind of out there. They're affecting society today. And we have to be able to have productive conversation around what we believe is good and bad around this while still allowing technology to succeed. So there's a lot of advancements in the weaponization and AI in that space, but it's really, I think we have to take it on a case by case basis, and not like a blanket statement, we can't use technology in these ways. Grant Interesting thoughts? What are your thoughts there? Elizabeth? Elizabeth Yeah, you know, I it makes me think of sort of turning it on its head is, is when is it? You know, when is it unethical not to use AI, right. And so, some of those questions come up when we are talking about weaponization, you can also be talking about saving human lives and making it safer for them to do some of these operations. And and that same question can come up in some of like, the medical use cases, right? So here in the US, we have a lot of challenges around being able to use AI in medical use cases, and there's, and there's some where you can have really good human oversight of the cases, you can have sort of reproducibility of those models, they can be as explainable as possible. But it's still really, really difficult to get FDA approval there. So I, again, I think there's two sides to that coin. Grant And, yeah, it's it's an interesting conversation have stuff wrong, because like, in that medical case, you talked about, you could see the value of using the same kind of technology that would be used to identify a human target, and then attack it, you could take that same capability, and instead use it in a search and rescue sort of scenario, right? Where you're flying something overhead, and you're trying to find, you know, pictures or images of people that might be lost out there. Same kind of thing, right, so, so where how, go ahead, you're gonna say something was, but I can see. Elizabeth And there's even simpler cases in medical, where it's like, you know, there's a shortage of radiologists right now in the US and, and you can use, you can use AI to be able to triage some of that imaging. So, because right now, people are having to, in some cases, wait a really long time to get their sort of imaging reviewed. And so can can, can, and should AI help there. There's also another one along those same lines, where, with things like CT scans, you can use what's called super resolution or de noising the image. And basically, you can use much less radiation in the first place to take the imaging and then use AI on top of it to be able to essentially enhance the image. So again, you know, ultimately exposing the patient to less less radiation. So yeah, there's it's pretty interesting when when we can and can't use it. Mm hmm. Carlos Yeah. And I think just to add a little bit to the one we can and can't right, so, advancements through drug discovery have largely been driven through AI in the same fashion weaponization of various all drugs or other types of drugs have also benefited from Ai. So, I mean, from a society's perspective, you know, you really have to Evaluate not only greater good, but that that ultimate use case like, where where do you want to make a stance around that technology piece. And understanding both sides really provides that discussion space that's needed, you have to be able to ask really honest questions to problems that are, you know, what you can see in the future. Grant So is the safeguard through all of this topic around ethics? Is the safeguard, basically, the moral compass that's found in the humans themselves? Or do we need to have less, you know, legislative or policy bodies? Right, that puts us together? Or is it a blending? What do you what's your take? Elizabeth Um, it's interesting, the UK just came out with a national AI strategy. And they are basically trying to build an entire AI assurance industry. And, and their approach is, so they want to make sure that they're make, they're keeping it so that you can be innovative in the space, right? They don't want to make it so regulatory, that you can't innovate. But they also want to make sure that there's consumer trust in in AI. So they're putting together from a, you know, a national perspective, a guidelines and tests and, and ways to give consumers confidence in whether a model is you know, reproducible, accurate, etc, etc, while at the same time not stifling innovation, because they know, you know, how important that AI is for a essentially a country's way to compete and and the opportunities for GDP that it provides as well. Grant Hmm, absolutely. Yeah, I can go ahead, Carlos. Carlos No, I think it's his it's your question. Left alone, should we got kind of govern ourselves? I think, I think we've proven that we can't do that as a people, right. So we need to have some sort of regulatory, and committee around the review of these things. But it has to be in the light of, you know, wanting to provide a better experience higher quality, deliver value, right. And I think I think when you start with, how do we get the technology adopted and in place and deployed in a fashion where society can benefit, you start making your decisions around, you know, what the good pieces are, and you'll start your start really starting to see the outliers around Hey, wait a second, that doesn't kind of conform to the guidelines that we wanted to get this implemented with? Elizabeth And I think also to your question, I think it's happening at all a lot of levels. Right. So there's, you know, state regulation around privacy and use of AI and facial recognition. And, and there's, you know, some the FDA is putting together some regulation, and then also individual companies, right, so people like Microsoft, etc, have have big groups around, you know, ethics and how AI should be used for, you know, them as a company. So I think it's happening at all levels. Grant Yeah, like we said, that is a people we need to have some level of governing bodies around this to, and of course, that's never the end all protection, for sure. But it is, it is a step in the right direction to to help monitoring and governance. Okay, so last question, right? This is gonna sound a little bit tangential, if I could use that word tangential. It's given the state of AI where it is today. Is it artificial intelligence? Or is it augmented intelligence? Carlos I can go with that. So I think it's a little bit of both. So I think the result is, is to augment our intelligence, right? We're really trying to make better decisions. Some of those are automated, some of those are not we're really trying to inform a higher quality decision. And yes, it's being applied in an artificial intelligence manner, because that's the technology that we're applying, but it's really to augment our lives. Right. And, and we're using it in a variety of use cases. We've talked about a lot of them here. But there's 1000s of use cases in AI that we don't even see today that are very easy. Something as simple as searching on the internet. That's helping a lot from you know, misspelling things and, you know, not not identifying exactly what you want and recommendation engines come and say, you know, I think I'm looking for this instead. It's like, Absolutely, thanks for saving me the frustration. We're really augmenting Life in that point. Grant The reason why I asked that as part of this ethics piece is one of the things I noticed. And as I work with the organizations, there's a misunderstanding of how far and what AI can do at times. And and there's this misunderstanding of therefore, what's my responsibility in this. And my argument is, it's augmented intelligence in terms of its outcome, and therefore, we can't absolve the outcomes and pass that off to AI and say, Oh, well, it told me to do this, just in the same breath, we can't absolve and say, We're not responsible for the use cases either. And the way in which we use it, so we own as a human human race, we own the responsibility to pick an apply the right use cases, to even be able to challenge the AI, insights and outcomes from that, and then to take the ownership of that in what the impacts are. Agree, disagree. Carlos Yeah, I would really agree with that. And if you if you think about how it's implemented, in many cases, right now, the best use of AI is with human oversight, right. So it's sort of, you know, AI is maybe making initial decision, and then the human is reviewing that, or, you know, making a judgment call based on that input. So it's, it's sort of helping human decisioning instead of replacing human decisioning. And I think that's a pretty important kind of guiding principle where, where, wherever that may be necessary, we should do it. There's one, you know, the Zillow case that that happened recently, where they were using machine learning to automatically buy houses, and it was not. There was not enough human oversight in that, and I think they ended up losing something like $500 million in the case, right. So it's not really an ethics thing, but but it's just an example where in a lot of these cases, the best scenario is to have aI paired with human oversight. Yeah, yeah. Great, I think. Grant Yeah, no, go right ahead. Yeah. Elizabeth You mentioned you mentioned being able to challenge the AI, right, and that that piece is really important, in most of the cases, especially in the one that he just mentioned, around that. That Zillow case, right, without the challenging piece, you don't have a path to improvement, you just kind of assume the role, and you get into deep trouble, like you saw there. But that challenging piece is really where innovation starts, you need to be able to get back and question kind of, you know, is this exactly what I want? And if it's not, how do I change it? Right. And that's how we drive kind of innovation in the space? Grant Well, and I would say that that comes full circle to the platform, I saw that your organization's developing, which is to reduce the time and effort it takes to be able to cycle on that right to build the model, get the outcome, evaluate, oh, challenge, make adjustments, but don't make the effort to recast and rebuild that model such that it becomes unaffordable or too much time, I need to be able to iterate on that quickly. And I think as a platform you developed and others that I've seen, you know, continue to reduce that I think it makes it easier for us to do that in it from a from a financially responsible and beneficial perspective. 100% Elizabeth Yeah, one of the one of the features that you mentioned was the versioning. And that really ties into a guiding principle of ethical use as well, which is reproducibility. So if you are, if you want to use a model, you need to be able to reduce, reproduce it reliably. And so you're getting the same kind of outputs. And so that's one of the features that we've put in there to help people that versioning feature to help people, you know, comply with that type of a regulation. Grant I've built enough AI models to know it's tough to go back to a particular version of an AI model and have reproducibility accountability. I mean, there's a whole bunch of LEDs on that. That's exceedingly valuable. That's right. Yeah. Okay, any any final comments for me there? Yeah. Carlos I think for my side, I'm really interested to see where we go as people with ethics in AI. I think we've touched on the transparency and visibility required to have these conversations around ethics and our ethical use of AI. But really, in this case, we're gonna start seeing more and more use cases and solutions in their lives or we're gonna butt up against these ethical questions, and being able to have an open forum where we can discuss this. That's really up to us. To provide we have to provide the space to have these conversations, and in some cases, arguments around the use of the technology. And I'm really looking forward to, you know, what comes out of that, you know, how long does it take for us to get to that space where, you know, we're advancing in technology and addressing issues while we advanced the technology. Grant Excellent. Thanks, Carlos. Elizabeth. Elizabeth Yeah, so for me as a as a product person in particular, I'm really interested in these the the societal conversation that we're having, and the regulations that are starting to be put together and kind of the guidelines from larger companies and companies like ours that are, you know, contributing to this thought leadership. And so what's really interesting for me is being able to take those, that larger conversation and that larger knowledge base and distill it down into simple tools for people like small and medium businesses that can then feel confident using AI and these things are just built in sort of protecting them from making some mistakes. So I'm really interested to see sort of how that evolves and how we can productize it to make it simple for people. Grant Yeah, yeah. Bingo. Exactly. Okay, everyone. I'd like to thank Carlos Elizabeth, for joining me here today. Wonderful conversation that I enjoyed that a lot. Thanks, everyone for listening. And until next time, get some AI with your ethics. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.

In this episode, we take a look at the question where is the line for artificial intelligence ethics? Alright, everybody, welcome to another episode of click AI radio. So ethics, what are they? Well, you know, if we look at Google definition here, right moral principles that govern a person's behavior, or conduct or con or the contacting of an activity. Alright, so I'll give you an example here, right? Without going into some again, some of my business law classes that I took in college. Here's, here's an example. I was in college and my brother came into town to visit and said, Hey, let's go skiing. Oh, sorry. I said, my brother, I've got to work. He says, Hey, no, come on. I'm in town. Let's go skiing. So what did I do? I lied, I said to my boss, I don't feel well. And I went skiing. Well, to make matters worse, I'm up skiing, and I get hurt. To make things even more embarrassing. As I come down off the hill being pulled by the ski patrol. They open up the sled there, the local TV news station was there interviewing and well, I showed up on the evening news. While my boss is watching the news. He sees me there. All right, what a way to learn a life lesson. So what I did was unethical. I don't know that I have to go deeper than that. That was unethical. That was 35 years ago. I sure hope I learned my lessons since then. That was a painful life lesson. He called me that night. And he said that grant, I hope you're feeling better now. I said, Oh, yeah, yeah, I was in pain. Alright, pain both physically as well as internally. Alright, so now let's talk about since apparently some of us carbon based beings have struggled or do struggle with ethics? What does it mean to do this for artificial intelligence? Well, to do that, I borrowed four categories from Wikipedia, talking about this area of artificial intelligence, ethics, number one, category number one, bias in AI, Category Two, robot rights, category three threat to human dignity, and category four weaponization of AI. Alright, so let's take a look at each of these briefly. So as it relates to bias in AI, right, this is where, as us humans, as we build these models, these AI models, then our own biases can either intentionally or unintentionally get incorporated. And what that does is that drives downstream decision making that's bias in AI, we'll look a little bit further that in a moment, robot rights so this is of course, the idea that humans have while we've got you know, moral obligations, we have them or we'll have them to our machines as well. Might be somewhat akin to animal rights. So alright, that's robot rights. Number three, again, was threat to human dignity. And of course, this is in the areas where respect and care and compassion and another human attributes are needed, right, AI should not be used, certainly to replace replace people. So how do we help protect that? And then the fourth area weaponization of AI, this is of course, using AI in military combat scenarios or, or other kinds of scenarios like that. Alright, so what I'm not going to talk about here, though, is and I think this is a separate episode, there's the concept of singularity, right? It's the notion that that we that some self improving AI becomes so powerful that humans can't stop it kind of like the movie eagle eye or iRobot with with Will Smith. Alright, so we're gonna park that one, put that over to the side. There is a nonprofit organization, however called Ready big breath, the partners the partnership of AI to benefit people and society is formed by Amazon, Google, Facebook, IBM, Microsoft, Apple, all the biggies. They're obviously looking at to develop the best practices in In AI ethics, all right, so you want, you might want to look them up, take, take a deeper view on them. Let's go a little bit deeper on each of these categories here for just a moment. So first of all bias in AI, as you've probably heard of this came out of toward data science.com. They, they pointed out an experience in 2019, where researchers found that an algorithm that had been used on over 200 million people in the US around hospitals, it was being used to predict the likelihood that someone needed extra medical care. And without going further into it, it ended up breaking it along the lines of race, black versus white, now comes to find out that what they discovered is that the data itself, the way in which was prepared, started to expose that sort of bias in the way it was being interpreted, and it translated into, you know, discriminate behavior. So that was quite quite a painful lesson. The good news is, is that they looked into it and caught that if they hadn't have looked into it or caught that then that would have translated into obviously, even worse, long term behavior. Here's another example. This comes from Compass see Oh, MPa, s, this was the correctional Offender Management profiling for alternative sanctions, long sentence, there are long title. There's an algorithm there that was being used to predict the likelihood that a defendant would become a repeat offender in the in the correctional systems there. And of course, coming out of there it translated incorrectly that a certain class or race of people was going to translate into twice as many false positives for repeat offenders as others that weren't of that race. So again, I yeah, not good, right. Ai bias is what that's about. So the questions are, what are the best practices to preserve the fair balanced use of AI? And how do you vet your original assumptions to avoid these kinds of mistakes? I find oftentimes, as organizations get focused on their question, or the problem they want to solve, get so so dedicated to solving that, that often don't stop to step back and look back at those questions, challenging the original assumptions on that. Alright, so bias in AI. Definitely a challenge and very relevant to small to medium business owners today. Alright, let's, let's take a look at robot rights. Alright, so this apparently came out around 2017 After an EU Parliament report, proposed reasonable approach to dealing with this right now. Here's the there's an article by an attorney that specialized in this from some of the research that I saw out of different reports some of it from did Jin No, comma. I said that wrong, as well as a CNBC report, both pointed out that there's this attorney that is specializing in business law and IP that argued for extending workplace protections to robots pointing out that people are kicking the robots, right that, you know, they're pushing them around, right? So for example, some are some of the stated look. And here's what's driving that argument by 2025. Some predict that robots and machines driven by AI are gonna are gonna perform half of all productive functions in the workplace. Holy smokes, I don't know, it's 2021, almost 2022? I don't know I've done enough AI right now to know, I don't know if that's really the case. That's half of all productive functions AI. Anyway, whether or not you believe that, when it's not clear, what is not clear is whether the robots will have any worker rights. And again, they pointed out that people feel hostility to him will kick over robot police resources or knock down you know, delivery bots, things like that. I've certainly seen some of that. So. So the question is, to what degree do you see this raised in the minds of people and where is the line for robot rights? And what does that look like? I'm pretty sure I Robot is not a documentary. Alright, here we go. Let's move on to number three, though. threat to human dignity now. Now, this one is really very sensitive, obviously. It's the whole notion of human dignity, right? It's the fact that humans possess some intrinsic value that caused them or says, hey, you know, they're worthy of respect, regardless of age, ability, status, gender, ethnicity. I subscribe to that. That makes total sense to me. A few years ago, Google announced a system this is I think back in 20. 18 An AI system with human sounding voice interactions. And in this particular case, I'm going to play an interaction for you. It's interesting, you can get this off off of Google site. So this is their, their material. I'm just going to take a clip of it here and share this. And tell me tell me what you think this is kind of interesting. I'm gonna pause right here. ... Okay, so I didn't play the whole thing, but pretty incredible how lifelike how human like that voice sound, and it was generated off of the Google duplex system. So the question is, did duplex reduce the human dignity? In this case of this? This assistant, right, who is trying to figure out scheduling for for the salon appointment? Right. So was the dignity reduced? And and of course, there are other variations of this, right? Where, where AI can then in behave, you know, acting behalf or as though it's a human? Where's the dignity for that for the people and let alone in, in a healthcare situation? Right? What would that look like? Those are difficult questions, certainly to answer as they go to this next category. weaponization of AI, right? Immediately in the minds of some there's this notion of, you know, the whole the whole singularity notion, right? That AI becomes in control, right? And people fantasize about that part, for sure. The US Department of Defense calls weaponized AI algorithmic warfare. That's, there's this interesting article on think ml.ai that discusses some of this. So there's a category of this warfare called lethal autonomous weapons systems, le Ws and the whole notion is that you know, autonomous weapons that can locate and identify and attack and, and kill human targets. And again, this sounds like the Tom Cruise movie Oblivion, right. In fact, I saw recently, session on 60 minutes, they aired something discussing the US and Australia's relationship with China, and pointed at a company that was producing weaponized drones as part of that puzzle. So the question is a where, where does weaponization of AI break ethical boundaries? Right? Or by definition, if you use AI for weaponization? Is it inherently unethical? Or you know, quite frankly, if AI is being used in a head up display of an Air Force pilot to improve decision making, saving the pilots life? Is that unethical? Boy more questions, and then there are answers. This area's fascinating to me, because the world's changing so quickly in this area. There are many, of course challenging questions to be addressed. Obviously, I've only scratched the surface here. But I wanted to let you know that in a future episode coming up soon, I've invited some guests from some AI companies to further discuss this topic and bring in various viewpoints. So in this particular episode here, I want to lay the groundwork for it. But to wrap up here, I wanted to bring it back to the premise of this podcast channel, which is, what does it mean to you as a as an owner as an SMB owner, right? Someone's trying to run your business and you're trying to apply AI in these four categories that I briefly touched? What does that mean to you today? Does it have immediate impact? My take on it is this. As it relates to bias in AI? I think that this one has the most immediate effect on you as a business owner today. And to help address it, it means we have to ensure that we're asking the right questions, not only the questions that we want to get answers to immediately for our business, but we need to be able to have the discipline to step back and evaluate the broader context in which we're pursuing it. It also means that we need to course prepare data, the data sets that are representative and are not skewed. So those are several things today that that can be done now, in the second category of robot rights. I don't think that that's a direct impact for right now. But you know, in the future perhaps the third category, threat to human dignity. I think this is a challenging one. Now, you know, when we've got things like deep fake, and tools like duplex, and that was a 2018. And here we are, you know, three and a half, four years since then right? You can imagine how far this is. So with deep fake and duplex and other such technologies where deceit or potential deceit can grow. I think ascertaining identity is crucial to your business. And this will grow over time. There's already been instances of AI driven technologies that have faked out business executives. So I do believe this is an immediate one. And then weaponization of AI. It's not a direct impact now, but you can see it's growing one. It's something that we need to watch, obviously, and address some more. All right, everybody. Thanks for joining and until next time, brush up on your AI ethics so you can put your business and your customers in the right lane. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com Now.  

In this episode we look at can AI help me see better in a cost effective way! Grant Everybody, welcome to another episode of click AI radio. Okay, I have in the house today with me, someone I've been very excited to talk with. He and his organization reached out to me and I was quite surprised when I saw the cool AI solution that they have been bringing to the market. And Carlos has been giving me a little background on this. And I think you'll be excited to hear what it is he's putting together. But first and foremost, welcome, Carlos Anchia. You got Yeah. All right. There you go. Carlos, please, welcome and introduce yourself. Carlos Hey, Grant. Thanks a lot for having us on. Like you said, my name is Carlos Anchia. I'm the CEO of Plainsight AI. And we're bringing to market an end to end computer vision AI platform. I'm really, really happy to be here love talking about AI, computer vision, and how we can get more people to use it. Grant So okay, so tell me a little bit about what got you going down here. As you and I were just chatting a moment ago, there's so many components to AI, or it's such a broad range of technologies there. What got you thinking about the CV or the computer vision space? What problem? What How did you get started there? Carlos Yeah, that's a really good question. So like you said, AI, the breadth of AI is huge, you have deep learning, you have machine learning, forecasting, prediction, computer vision. And these are just a few. There's a lot of different applications for AI and places you can go down and succeed in. From our respect, we really, we really focus in on computer vision, specifically how to apply that to imagery and video. Today, there's a huge amounts of data going throughout the internet and in enterprise storage classes, where you can't really extract the value of that data unless you actually perform some sort of computer vision machine learning on that type of data. So we're really extracting the value of the picture or the video. So it can be understood by machines. So think of a dog and a cat in a in a picture, right? Those cases, the machine doesn't know it's a dog and a cat, you have to train it. And that's where computer vision comes in. And really, we got into it because we were pulled in by customers, customers of ours wanted to start doing more computer vision and leveraging our platform that we had around high throughput, ingestion, and event driven pipelines. So these customers came to us and hey, you know, this is great, we'd love to really use this for computer vision. And the more and more that kept happening, we kept retooling around the platform. And finally, the platform from end to end is purpose built to do computer vision technology. And it really allows us to focus in on on what we're good at today. Right? And that's really delivering value within the computer vision space. Grant So I remember the first time I wrote some of the OpenCV framework code, right. And that was my first introduced introduction to it. This is a number of years ago. And I started thinking, Oh, this is so cool. So I'm writing all this Python code, right, building this stuff out. And then I'm thinking, how many people you know, are actually leveraging this platform and look at even though open CV is cool, and it's got a lot of capability, it still takes a lot, you know, to get everything out of there. So can you talk about how you relate to that open CV? And what is it that you're doing relative to that? And how much easier do you guys make this? Carlos Yeah, so I mean, you hit the nail on the head there, right? So from a developer perspective, it's really around, I need to learn open CV, I need to learn Python, I need to learn containerization I need to learn deployments. There's a variety of different companies that, you know, they're all great in their own right, right. Every one of those companies that we just talked about organizations are contributing tremendously to AI. But from a developer's perspective, you really have to learn a little bit of everything to be able to orchestrate a solution. And finally, when you get to, hey, I use AI. Let's pretend we're looking at strawberries. Hey, look, I built a model that the Texas strawberry that is your over the moon excited, but the very next thing is around, okay, how do I take that and deploy it 1000 times over in a field across the world and understand how to make that in an operational fashion where you know, it can be supported, maintained update, and that's really where we have this this crux of an organization where it's really different building something on a on a desk for a one time use. And and there's a lot of wins through that process. But then taking that and operationalizing for business driving revenues driving corporate goals around, why would that feature is being implemented, that's really where we come in, we want to be able to take off that that single single path of workflow where it's a little bit of everything to orchestrate a solution, and provide a centralized place where other people, including developers can go and help build that workflow in a meaningful way where it's complete. Grant So operationalizing, those models, I find, that's one of the biggest, or the most challenging aspects to this, it's one thing as you know, to, to build out enough to sort of prove something out and get some of the initial feedback, but to actually get it into production. I think I saw MIT not long ago, maybe this a year ago, now, they had come out with this report, it was through the Boston Consulting Group as well, they'd mentioned something about, hey, you know, 10% of organizations doing AI are getting return on their investment. And, and, of course, when you look at all of the investment of the takes for the business to really stand up all the data scientists and all the ML work. And you can see why the numbers translate that way. So to me, it feels like not only doing this in the area of CV, but the problem you're really trying to solve it feels like is you're attacking that ROI problem, which is you could take this kind of capability into business say you don't have to stand up all of these deep technical capabilities. Rather, you can achieve ROI sooner than rather than laters. Is that Is that accurate? Carlos That's correct. And I think it's really through the adoption of technology and you hit a you hit a really strong point for us there around the the difference between it works and it's operational. That's that's really the path of your your, you're less there in the CV world and more there with the DevOps ml ops portion of it, getting machines running consistently, with the right versions of deployment strategy, that latter half of it, just as important as the model building pieces of it. But even after you get to that piece, you need a way to improve, and improvement in the model is very costly if it's not automated. Because I mean, you can just look at the the loss for a simple detector, like a strawberry, where, you know, if the model starts to perform poorly, you're not pulling as many strawberries out of the field. So you need a way to be able to update that model, quickly, get training data into a platform and push a new model back out. And it's really around how fast you can go end to end with that workflow. Again, and again. And again. And again, this is that continuous improvement that we have born into us from previous software development life, but really in in machine learning and computer vision, your ability to train, retrain, and redeploy. That is where you really get the benefit out of your workflow. Grant Well, that really confirms my experience with AI will. Typically I'll refer to the term that we've been using, as we call it a SmartStep. It's that notion that I need to be able to refactor my models and take in consideration that changed context around me, whether it comes in from the world or from the customer, or whatever that means, some level of adjustments taken place that begins to invalidate my previous AI model. And I need to be able to quickly make those adjustments. That's fascinating. How long typically does it take for you to do that kind of refactoring of your models? Is that Is it a day? Is it a year, a month? Or the answer is? Well, it depends. Carlos So it's twofold, right? So it's, it's hours to do that. But it really depends on the complexity of the model, and how long you have to train. But in an automated workflow, you're you're continuously adding data to your training set, that are lower quality predictions, where you can retrain automatically when you hit a certain threshold, and then validate the model and push that back out into your production alized environment. So it's it when you go to develop these sort of workflows. You really have to start with whatever I build, I know I have to improve on later. So that improvement cycle ends up costing a lot if it's not part of the initial discussion around how do we count strawberries, right? So it's evident you and I can nerd out on this. Let me shift the focus a little bit and ask about it from say your your customer Write from their perspective, what is it that they need to be able to do to be successful with your solution? What skills or capabilities do they have to bring to the table? Yeah, and I think it's, I think I have this conversation a lot with our clients. And it's really less about them having technology around data science and building model, and more around a collaborative environment, where organizations, you know, they have a culture of success. But that culture of success is really borne by holding hands through the fire, it's, it's being able to commit and lean in when the organization sees something that's really important to them, either either from a technical perspective or revenue perspective. And it's these companies and these types of people that get to rally around a centralized platform where they can build and collaborate with machine learning computer vision applications. And, you know, it's it's a, it's really interesting to see the companies that succeed here, because it's really based on a culture of winning, right, where the wind doesn't have to be the hardest, most technical, logically difficult problem, because complexity really drives timelines. And if you're looking to change from an organization's perspective, start getting the little wins, get the little wins, start having some adoption within the company around, wow, computer vision is working. We've identified these problems in a few hours, we have a solution deployed, you start building this sense of confidence in the organization where you can take on those larger tasks. But you have to start with a build up, you can't just go right to the highest ROI problem. No one starts at human genome sequencing. Grant Have you, or do we got a problem? Yeah. Back up, back up. So So all right. So it means to me it sounds like as an organization to succeed with this getting my problem definition, understood or crisply put together first, what would be an obvious thing to do? But how long does it take for me to iterate? Before I know that I've got value, that I've pursued the right level of the problem? You You made an interesting comment a minute ago, you're like, oh, within a couple hours, I could potentially retrain the model and have that back operationally. That means if I can fail fast, right, if I can pick my problem space, get something out there operation, try it fail fast, and then continue to iterate with AI as my helper that that's really, really quite powerful is that the model that the your person? Carlos It is the model? That's exactly right. And it's not just hours to retrain, it takes hours to start, right. And just to highlight you kind of started with, we have to define that problem set first. So even after we define that problem set, a lot of times we have to go back and redefine that problem set, and really the piece around failing fast. It's it's experimentation. And do we have the right cameras? Do we have the right vantage point is the model correct, you want to be able to cycle as fast as you can through that experimentation phase. And sometimes you have to go back and redefine that problem set. Because you're learning more as you go, right? And you're evolving into okay, I now understand the corner cases a bit better. And with the platform, you really can cycle that quickly. I mean, machine learning at scale is really how fast you can iterate through improvement. Grant It's quite, I think it's quite a testament to how the AI just world in general is improving. I know that you and I were talking earlier, years ago, you know, when I first started writing some TensorFlow code and Keras code, you know, the, the time it took to fail was much longer, right. And then the cycles were huge and, and getting this down to a matter of hours or even a few days, you know, for an enterprise's is massive. What's the, from what you've seen terms of different industries? Are there certain industries that tend to be leaning into this and adopting it or the is there no pattern yet? Carlos No, there's a there's a definite pattern. And in 2022, we'll all see kind of what that what that's looking like. And it's really an industry that has traditionally not been able to go through that digital transformation. So think of think of think of a piece that's a very manual piece, right, like physical inspection, where humans would look at something, they'd write down their notes on a piece of paper, then that that item would go through either pass or fail or some criteria for rework. That's all possible. Now with computer vision years ago, that was impossible the accuracy wasn't high enough, plain and simple. It just took it wasn't, it wasn't better than a human. Now we have models that are better than a human for visual inspection. And these industries are digitizing their workflow. So it's not only the feature of computer vision, but it's also now I have a digital record of all the transactions, I have extracted video information, it makes auditing easy for those sectors that have a lot of regulatory compliance, those that require proactive compliance to audit requirements, as well as visibility. Visibility has always been an it's funny when we talk about visibility, but it's computer vision, but like a lot of human processes, that there's zero visibility in it there, it's really difficult to audit, you know, why is this working better or not? So having that, that digitization of the flow with the feature of computer vision allows us to extract the value. And industries like agriculture are going I mean, agriculture has been a leader in technology for a while, but now you're really seeing adoption at livestock and row crop with drone technology. It's a very rich image environment, medical space, medical space for computer vision in 2022 to 2028, is estimated to be billions of dollars just with medical imaging. And that's not that's not the the total addressable market for the hardware, it's just the imaging piece. So we see we see a lot of growth in sectors that are going through this digital transformation that are adopting technologies that are now getting to the point where they can get pushed down into the masses instead of just the top five companies in the world. Grant Excellent, it seems to you part of your comment earlier made me think about process optimization for organizations, and the ability to extract processes, you're familiar with process mining, right? The ability to extract, you know, out of logs of these organizations and doing something like that where you can produce this visual representation of that, and then building models against that, to optimize your process might be an interesting use case. Yeah, that's fascinating. Carlos That's a really good point, right? Because that's, that's a different portion of AI that can be applied to like just log analysis, that then would allow you to go back and Okay, now that we have the process mind, where can we improve along the process? Grant Yeah, yeah. Amazing. So many uses and use cases around around this CV area, for sure. So let's say that someone listening to this wanted to learn more about it, where would they go? How would they? How would they find more about your organization? Carlos You can find us everywhere, right? We have a website, plainsight.ai. We're all over LinkedIn, we have Twitter, we're on Reddit, we have a Medium blog, there's a Slack channel where we geek out around computer vision use cases and how we can improve the world through computer vision. We're really we're really out there and feel free if you have questions come reach out to us. We have amazing staff that are looking to empower people in AI. So if it's through just just a question around how does this thing work, we'd love to talk to you if it's Hey, we're kind of stuck in our journey. We need some help reach out to us we can help you. Grant That's awesome. Carlos I can't thank you enough for reaching out to me and for a listening to click AI radio, but also for reaching out and sharing what it is you are you and your organization are bringing to the market think you're solving some awesome problems. Carlos Thanks a lot, Grant. Appreciate it. always appreciated talking about computer vision and AI and thank you to you and your listeners and really appreciate what you're doing to the AI space. Grant Alright, thanks again, Carlos. And again, everybody. Thanks for joining and until next time, go get some computer vision from Plainsight.

What are some of the ways AI can benefit your business? So with some of the current talent shortage is going on currently in the US the challenges for businesses to find employees or employees are going to be best for your organization. He got me thinking about what are ways in which I can ask AI Please make my job easier, right? What are some ways that I could make better decisions, whether it's finding the right employees, or whether it's building my business with the right business partners? I stepped back and said, Wait a minute, what are the benefits of AI for business? There's many hype out there for sure. Let's look at a few of these. Alright, so one of these is around efficiency and productivity gains, for sure. Right. That's certainly where we're looking for the benefits of improved operational efficiencies for the organization. There's a case study that I saw, this was from press@taste.com, they were actually talking about, about an AI restaurant, excuse me about an AI driven restaurant. And in this restaurant, they had experienced some slow service times in their drive thru. Also, when people came into the lobby service was slow, there's a high rate of employee turnover. There's all the human aspects of that, certainly in terms of being a great leader. But that aside, they looked at AI to see if they could identify some inefficient operational areas, what came out of it was, the AI helped them improve the speed of their service by 21%, during rush hours, right, that's, that's a really decent improvement. The other was, they improve the efficiency of their kitchen by 53% 53%. So the amount of time it took to actually get the order in, get get food, turn around, get things cleaned up, get food prep, that that sort of stuff. And then of course, their sales, they said increased exponentially as well. Alright, so looking for opportunities to minimize your costs as well as to perform improvements in your operational efficiencies. That's certainly one of the areas for productivity gains. So that's benefit number one or a benefit, another benefit to consider, which is fairly similar to this, but it's all around improving the rate at which you do business, right, the speed of your business, perhaps one of the things that we found in this area is that it's key to focus your AI questioning on specific scenarios of your business. So the idea is rather than trying to, you know, eat the entire elephant, so to speak, metamorph metamorphic Lee is I don't, that's not even a word. Anyway, rather than trying to eat the entire darn elephant, adjusting the AI models based on where you're focusing is critical. So create smaller AI models focused on a certain business scenario. Alright, number three, new capabilities and model expansion. And of course, you know, when you think about AI, sometimes, right? That's when we tend to think of other things like, oh, Elon Musk, or, you know, what they're doing with Tesla, or you know, the autonomous cars, all that sort of stuff. Certainly, there's those models out there, for sure, where new business models being created because of AI. But for for most of us, as we're getting started with AI, that's, that's kind of a step beyond where we typically see see organizations go for typically, you'll tend to start with an efficiency gain, you know, sales, improvement, gain some some things like that, then as you grow, then new opportunities for capabilities start to emerge. Alright, here's another one. That's kind of interesting. It has to do with better customer service so AI can benefit in those areas as well. There's an example that I love on this one is we've all seen you've probably seen nor Domino's Pizza, they are monitoring so when you call and say, hey, I want a pizza. They've got some AI listening and they're trying to figure out hey, is that granted Oh, that's grant on the phone at that Larson home, well then order that guy some some sinister pics right or, or upscale, you know upsell him on that right? So there they eyes listening, it's determining who's on the phone, and then prompting their order takers to customize coupons based on who it is they're talking to. So, quote unquote, better customer service that might be one example. All right, here's another the another benefit is around monitoring of your business and how to improve that. Lots of times what we found in this area is, this requires the AI model to be running somewhere, there's kind of two approaches to think about AI models, there's, there's the approach that says, hey, just give me some of the insights of what you see looking backwards. And then there's also the aspect of using AI in the course of doing your work. And this permits AI monitoring, right? So here's the idea, right? It's as perhaps, as you're going through a particular sale, while you're in the act of doing the sale, the AI model is running somewhere. And it's queried, right? It might be out on Amazon, or Google Cloud or Azure. In any event, it's running out there. And it's watching those business activities. And it's giving you some quote unquote, real time insights or feedback like, hey, the probabilities of this being a good client for you, has dropped dramatically, right? Or perhaps this is, probabilities are high for good one. Alright, so improved monitoring certainly is a key a key area, and it takes a little growth to get to that point, when you've actually got the AI models out there running, you don't have to wait for that to start getting the benefits. If you back up, you can start getting the analytics much sooner to put yourself in a position so you can turn on the monitoring. There's another one here another benefit tends to be around reducing errors that people make that you know, I guess what I'd say here is, this is a key reminder, I think that one of the benefits of AI really is I think we should view it as augmented intelligence, it's, it needs to be looked at as, as something that causes you to think or rethink what you might currently be doing. And sometimes the reduction in human error comes because the AI saw something that causes you to rethink your processes, and then you make changes. And sometimes many times those changes are beyond what the AI saw. But the AI SIRs or served in that scenario is a trigger or an instigator of the change. So in that sense, it can help catch human error, but should always challenge it, right. And so we want the AI to inform us and say, Oh, here's things that our brain couldn't see. But sometimes, you know, doesn't mean the AI is always right. So we wanted to augment your intelligence for sure. Alright, here's the, the one that I sort of started with was around talent and Talent Management and, and, you know, using AI to gauge things like employee sentiment, although I worry about the whole big brother thing, right? Don't really want that. But to monitor the sentiment for sure. I'm not necessarily talking about employee productivity or things like that. Did they log on to their computer the right time? I'm not looking at that, but the way in which they interact or talk or think about things right, or, or to monitor evaluate the the nature of the behavior of your business partners, right? Do I have the right business ecosystem in place? And are these in fact going to be the right business partners for me using AI to augment my talent management, both internal as well as to those external organizations as well? So AI can be used to leverage those kinds of things as well. I've been interested to know what are the highest value areas for you, you can check out click at click AI or radio.com and subscribe and let me know what you think, everybody thanks again for joining and until next time, plan out your business AI benefits. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com Now.  

In this episode, we take a look at curating those leads which come into your business using AI. Is it possible to underestimate the value of a quality customer in your pipeline, we all have experienced what it means to bring a customer into your business, which later we are kicking ourselves saying, oh, man, I wish you didn't have that customer. Well, can AI do anything for us? Right to lead or not to lead? Right? That's the question to ask AI. Can you help us with this AI? Well, as you know, today, there are multiple AI tools out there which provide insights on qualified leads. Lots of times are looking at things like recency Right? Like how many leads, you know, came in today or yesterday? Or they're looking at frequency? How often did the leads actually touch our organization? Right? Was it through some phone interaction? Or did they click on our sights? Or what did that look like? There's easy to action scores, that of course, you want to try to get in front of your sales teams. And of course, the goal is to leverage that info so that the outcome then becomes realized, right, and it turns into a valuable kind of customer. Now, one of the things that we want to do is we want to connect to a variety of systems downstream. So one of the challenges around lead management and lead AI is you need to be able to organize the information from the earliest touchpoints and identification of who the person is, and follow their lifecycle all the way through to the realization of the income, or the revenue, I should say, coming into the business, or the rejection of it, or the later reclamation of that in the form of a of a refund. So number one, we want to think about how do we connect all of that data together and tell that story, really looking for a series of breadcrumbs, if you will, that ultimately can knit and tie that together. And for a lot of businesses today is you know that this is found in a lot of different systems, right from things like HubSpot, or you know, Pendo, or what have you, right that the data is all over the place. But number one, you want to think about fit, right? You want to think about who are your leads, right? And how do they match what your ideal customer profile looks like? Do you even know what that is? Have you described that perhaps some refer to that as I see p. The world needs another acronym, right ICP. But nevertheless, this is a critical one, what's your ideal customer profile? What do they fit? What does it look like? Can I describe that, and in order to apply AI to leads, it helps for me to see that now, quite frankly, using AI to help describe that is actually very common practice. So if you think well, I don't really have my ICP defined. That's all right, run a series of AI analyses on the full lifecycle of your leads. And in time, the AI will begin to discover what that looks like for you. So you don't have to have that first. In fact, the act of preparing AI for leads can help you to develop that. Certainly you want to understand, though, what's the leads activity? What are they doing? What's their ultimate intent? And of course, how often do they actually touch and interact with it with the organization? It turns out that in some cases, it's actually more valuable to reduce the number of leads in terms of, you know, all those we're working with in the organization and actually increasing our sales. So if I could reduce my leads, let's say in half, but of that remaining half, I have very high close rate, then my cost of goods sold starts to drop dramatically, and so becomes really valuable to the organization. So it's all about finding revenue, obviously, revenue specifically that's untapped in the organization. This is one of those efficiencies. Laser optimization plays that we do on a business, right? It's not necessarily saying, Oh, we're going to go after a new business model or changing this or that this is looking within your current pipeline of the kinds of leads that come through, how can we optimize our behavior so that we return the best possible dollar to the organization. Alright, so, in general, that's the model. But rather than a generic model, I wanted to introduce five or six different scenarios around leads to consider. Alright, so one of these is around product, profitability impact. And I'll come back to these here in a minute, there's lead or customer segmentation. Another or third scenario is around date or time of sale. There's refund impacts around leads, there's demographics, obviously, for leads, and then there's what we like to call destructive behavior. All right, so let me just to maybe peel the onion back, go down one more layer on each of these. So on this first one on product profitability impact, the real question is, what are the conditions that surround a lead that produces the higher profitability? And of course, as you know, a lot of groups are focusing on the sales itself, which is obviously good. But quite frankly, having your AI pivot on your profitability is a key question to ask your AI solution. So when you're working on leads, build that connective tissue to the ultimate products. And then not only that, but the profitability of those that starts to pay you back very quickly, in terms of any investments that you do with AI actually very strong impact. Here's another one that pays back your AI efforts. It's around lead lead segmentation, right. And of course, it's finding those segments that drive towards higher sales growth, and of course, higher profitable growth and the ability to segment that, obviously, your benefits certainly marketing activities, pricing opportunities, as well as value delivered to that lead, who becomes a customer. Alright, so having the AI work on that highly valuable. third scenario for AI with leads, of course, you know, when the lead progresses to the point of sale, or for every touch point leading up to that sale, understanding the insights of those touch points, the timing, the areas of interest, etc, that leads some some very insightful AI analysis as well as predictive behavior. That certainly tipped you off as a business owner of when to proceed or when to stop pursuit, the sooner we can stop the pursuit of something that by prediction says, this will waste your time, then certainly the better the better than it is. I had an experience one time with an organization that was trying to sell something. And at one point, I was not getting the cycles. And the salesperson looked at me and said, I'm going to start to I'm going to stop talking to you. I said, Yeah, it's probably a good idea. So he figured out, yeah, I wasn't going to be the right one. So the better you know, the sooner Of course, we can find these, the better. So date, time of sale, meaning all the touch points throughout the lifecycle of the lead. Those scenarios, obviously, very critical. Ask your AI to look into that. Number three places to put the AI for leads is around, you know, refund impact, of course, is when your your lead turns into a sale, of course, that ultimately then turns into a refund. And it's certainly critical to understand what those look like and how we can get away from and move away from those sorts of situations. The fourth, or excuse me, the fifth scenarios around demographics. So lead demographics. Now, sometimes this can also be aligned with lead segmentation, but not all segmentations are demographic based. And so that's why we break it out, because we see situations where organizations actually treat that as a separate segmentation. So within some segmentation that they'll have to then do further demographics work. Of course, it permits you to focus your messaging and your marketing into sub markets, where things have historically been lucrative or for which you have a high predictive probability. So applying leads to this sub area around demographics for your leads, very, very powerful. Alright, and then the last one I mentioned here, is around what we call destructive behavior. And I recall early on when we first started applying AI for companies and we got to a point where we said to one of our clients, oh, and by the way, look, we've been able to prove out here with high probability you need to stop having salesperson x with with product why in this particular scenario, the client went home Wow Okay, hadn't even seen that and understanding those kinds of destructive behaviors things to stop. So, in the same vein doing that with leads is also critical which is stop going after certain kinds of leads as early as possible and how asking the AI tell me what are those conditions or situations in which the lead produces destructive or bad behavior and let the AI find those bad situations so that actually becomes a critical thing to understand obviously, alright, so as you know, there's a range of AI solutions out there today certainly to help you with leads now whether use that we know one of them or you use click AI the real trick is to get control of the front end of your business and and certainly qualify with AI your intake process, obviously reducing you know, the less qualified leads the earlier than certainly it's better for the business. Okay, everybody. Thanks for joining. Until next time, get some AI for your leads. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com Now.  

So how does Elon Musk manage his time? Is it AI? Come join us and find out. Hi everybody, welcome to another episode of ClickAI Radio. So Mr. Musk, how do you do it? Calling out Mr. Musk. All right. So of course we've I'm sure all seen the video where it talks about how he manages his time, he manages his time, apparently using timeboxing. And on some of these reports that I've seen, he says he spends 80% of his time doing engineering design work. That's pretty amazing. And that, some have said that he manages time down to five minutes slots, holy chicken soup, that's pretty tight. So what it means is that he plans out his whole day on five minute increments, that's quite the level of planning. I don't know for sure if that's what he does, but time boxing. So apparently what you do with time boxing is, as you plan it out, you put it on a whiteboard or a note paper, whatever. And then of course, you fill out the time that you think it's gonna take to get that done. All right. Now, of course, there's some fallacies and problems with this right? One of those is, as humans, we seem to overestimate. We're overly optimistic, I should say in terms of how long things will take, we think it might take 10 minutes to do something. In reality, maybe it's 20 minutes, right? And of course, there have been studies that talk about people tend to choose the best case scenarios, right? When we're, when we're allocating our estimates for time. This, of course, makes timeboxing really difficult, which would look like if you had a piece of paper, a series of time slots at some increment. Now, apparently, five minutes for Mr. Musk, I would do more like 15 minutes. But hey, who knows? In any event, so when you come up with those, one of the challenges is, how can I improve my accuracy for that? Turns out, there's some tools out there today to sort of help you do this toggl, t-o-g-g-l is one of those tools to help you track your time and see how things are gone. This can of course be painful to do. I listened to one guy, he has a technique where he asks you to go through and describe everything you did for two for two weeks. So every day you write down on a piece of paper for two weeks what you did. And then at the end, he calls it a time study at the end of the two weeks, and you really kind of have a sense of where you're spending your time. And I've talked to multiple people that have done this. And they all complain about it saying what a total pain. And yet how revelatory and insightful it is when you start to see some of the areas that they're spending their time that actually was, was lost to them, right? lost productivity and things that are meaningful and useful. Of course, one of the big objections to time boxing, which says, I'm going to plan out my day on some sort of increment and for each increment, here's here's a box of what I'm doing. One of the objections is around interruptions, right? So let's say you had planned out your entire day for that. Now, how do you deal with the interruptions? I know some people to deal with that. They'll create two columns. They'll have one column that says, here's the my original plan. And then and then they'll have another column, which is the revised plan, right, which Well, here's really what happened, right? thought I was going to be doing this activity of this task. Turns out, I did something else because I was interrupted. I think it was Dwight Eisenhower. That said, planning is everything but plans are nothing. Well, so of course the bottom line is we need to be able to pivot in order to adapt and adjust certainly. And of course, we need to avoid over scheduling too because some also have said that the more you try to do, the less you will accomplish. I think there's something to that. If I context switch too much, man on a five minute increment. Wow, Mr. Musk, if that's true, hats off to you. Five minutes in That's that's that's pretty tough to context switch constantly like that. I think that might be some overscheduling. Who knows? Well, that's why he's where he is, I suppose. Others have said that the way in which they plan, they'll organize it based on the intent for their business. So they'll plan tasks and activities in the morning, that have to do with money generation and revenue generation activities, when they're the most fresh. Those are the areas they want to put most of their intellectual capacity to. And so they'll do those types of time boxed activities in the morning. And in the afternoon, it's more cost management expenses, things on the cost side of the balance sheet, that's interesting technique, then I started to look into AI for time management, you've probably seen there's an app out there called timely think it's from a company called memory AI Anyway, you can download, I think the way it works is you download it onto your Mac or your Windows computer, you start connecting it to your Google calendar or other calendars. And over time, you start training the AI to start recognizing how you're spending your time, and you ultimately are teaching the AI model, something about your work schedule and the way your time is being spent. Now, why would I even talk about time management? What does that have to do? Well, one of the things I've noticed is that, given the increased demand, of course, on SMB owners, finding ways, obviously, to optimize our schedules is certainly important. But the real reason is, many view activities to transform their business as too much of a stretch, right? Because we're so busy running around just keeping the lights on as a small to medium business owner, who can think about adding more activities to our days. So you know, things like, hey, maybe I can improve my decision making by looking at more analytics, right? Or, hey, I could spend some extra time making changes to my business processes through AI insights. Yeah, sounds interesting, but also sounds like, hey, you're being told to take your vitamins. And who wants to do that, right? That's not too exciting. So what you really need to do my experience with his bins is we need to find what's the real business cause, or the motivation to do something like that, right? So sometimes it might be competitive pressure, or it might be a business model threat, right? Or might be growth goals or, or even cost pressures. And when something like one of those is understood first, then finding the time by doing some kind of time analysis helps us to free up the cycles to address this. So I kind of have to find the why right? Why is it that I would do this? What is it that's pushing me, right? And lots of times as humans will move away from pain faster than will move towards a goal. So if you can find, where's my pain point, my business? What is it that would drive me and give me the motivation to make this kind of adjustment, then I can start finding the business context, where I'll start making the changes then to leverage new techniques such as putting AI into your business. I was talking with a large retail organization not too long ago, they said that, before the covid 19 pandemic, they had discussed providing curbside service, right. But at the time, the estimates in the organization, they said it would take about two years to roll that out. And then when the pandemic hit, they were able to roll it out in four weeks, right? So suddenly, obviously, there's this, there's this pain, right? There's this pressure, we have to do business differently, the context has shifted. So finding the motivation, then allowed them to find the time and then suddenly, a lot of the red tape and other things move out of the way. So find your motivation for change as a business owner, right? Then find the time to pursue the change. Those are the precursors for most organizations to pursue an AI strategy. Now many times of course, like I said, we move away from pain more quickly. So where does your business hurt? And then let's get AI on the job. All right, everybody. Thanks for joining and until next time, find your motivating factors to set aside the time to apply AI in your business. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

In this episode, we take a look at the skills gap that's really affecting your business. Welcome everybody to another episode of ClickAI Radio. All right. So in overcoming the lack of skills in your business, now I'm going to start with, with the focus on AI itself, right, and what that looks like. And I'm going to pull here from certainly my own experiences, but also this is a report I saw from Deloitte, they had they'd said that, hey, they found that those companies that are seasoned with AI skills, they didn't feel they had a big skills gap. Oh, that makes sense. Right. The other thing they pointed out, though, is that the companies that are doing a lot of AI right now are really focusing more on transformational projects than on cost reduction. So from their perspective, hey, when you're getting started, you're often looking at cost reduction as a way to leverage AI. Hmm, I don't know my experience, I've found that it's been more around sales and say, of course, in the supply chain area, there certainly cost reduction there. So all right, that makes some sense. It also said that they broke it into three categories. There were those companies that were seasoned with AI, then the other categories are skilled with AI, and then the other word, the starters, and said that the seasoned ones are of course relying on their own internal skilled resources, had stair season, I guess, by definition, whereas the starters, were leaning more on other companies, right, they're outsourcing some of the AI itself. A couple other points here from this report. So they indicated that two thirds of the seasoned adopters are currently training their developers to create new AI solutions. They're providing more training for more of their employees to do AI work. So rather than leaning away from it, they're actually leaning more into it, because of some of the benefits that they're pulling from it. In another report, this comes from the hill, they said, like there's concerned about AI, an AI workforce, specifically shortage, an AI workforce shortage in the United States becoming a top national security priority. And there are some calls for some legislative action. They're trying to compete with other countries like China and others. But you know, some see little data on the actual AI labor market dynamics, is it really, is there really a workforce shortage? And if so, what does that look like? Now? How do you even quantify that, so you have to back up and think about the skills of AI itself. Now, given that my intent here is true to target and speak with the small to medium business owner, for the most parts, I'm never gonna say go build your own data science team, it's too expensive, as well as you're not going to get the ROI on it. As we all know, we saw that report from MIT last year. That said, Only 10% of companies that are building out their own teams like this are actually achieving achieving the financial results. So we're not we're not going to focus on that, rather, I'm going to target let's go after another approach. There's certainly a shortage of AI skills in the US, right? But hiring your own team to do that isn't going to be the best way. So if you use an AI service and rent the capabilities to get the insights at a fraction of the cost, then if there's not an AI skills gap from your, from your organization, excuse me, then what really is the gap that you're facing? what's what's missing, in terms of you achieving the effects. So the benefits of AI? Well, there is still a skills gap. And the skills gap, though, comes in areas that are not too strange sounding to us. In other words, it's not going to be hey, can your can your particular business analyst deal with, you know, logistic regression algorithms? No, no, no, that that's for the AI people to figure out but as a small to medium business owner, you don't worry about that part. But there are some skill sets that you do need to have or strengthen in your team in order To be effective with leveraging the effects of AI, so I'm going to pull from this report this comes from, from go scale, excuse me, yeah, go go skills excuse me, they they reported that there were three hard skills lacking in recent graduates. This is interesting, right? And it's stuff you already know, right? It's writing proficiency, public speaking, and data analysis, let's apply this to AI, right. So as a small to medium business owner, you want to play AI, you don't need to go deep on all the AI skills. But what you do need to go deep on is your ability to leverage those insights into your business. And that's where you need to spend the time building up your team. Let's look at writing proficiency, Well, certainly AI does a great job at identifying those things that our our brains can't see. So it's also critical that we describe meaning we can write down what the business problems are, and what the challenges are. So writing proficiency does affect the ability to properly point to and use and therefore adopt AI in your small to medium business, public speaking Well, this, of course, dramatically affects your ability to communicate. But specifically, the ability to communicate the transformation, the iterative transformation that your business goes through, when you apply AI, you know, you never apply AI, once you apply it, make a couple small steps, and then apply it again, you do it incrementally, but you also need to be able to communicate, what's the journey that we're on? Alright, so public speaking, whether that's external to your customers, where they're seeing the impacts of this, or internally, the ability to speak is critical. And then third, here was data analysis. And of course, you know, the ability to create insights on your data, use an Excel or whatever other tools, tableau, etc. Those are certainly critical. But in this case, they're critical to help point out that there's a case for AI. Let me let me illustrate that. I was talking with a business executive actually, just last week, who made a statement that although his engineers could do data analysis, they had that skills to use the you know, the various tools to do that. They could not estimate their supplier forecasting, because the data was spread out across so many systems and was so many levels deep, that just the the, you know, analytics tools couldn't actually give them the insights they needed. This is a great place for AI to step in, right. And they were wise to recognize the limits of the data analysis and said, we're not solving the problem, right, the data spread out too much. And so they saw the opportunity for AI to help out. So yes, be skilled the data analysis, but also be skilled at knowing what its limits are. Right. So those are three hard skills that your teams today already have, but course need to strengthen and bone up on. Let's look at some soft skills that was reported out here by this ghost skills report. The five most lacking soft skills in employees they pointed out was number one, critical thinking and problem solving. Number two, attention to detail three communication for leadership and five teamwork. All right, so let's let's just pull these apart here real quick. So critical thinking and problem solving, obviously selecting the right use case to apply AI to solve business problems. That is critical fact that thinking is so critical that we've seen multiple failed AI use cases in the market, right? And of course, it gives a black eye on AI, right? It's like Oh, you're applying AI how you're doing what with it? No. So stay is a small to medium business person stay in the guardrails of sales, use cases and supply chain use cases, right? Look in those areas. And don't go outside of that elite, at least for now. Alright, as AI matures and grows. And as you feel like hey, you've got a handle on this, then certainly go wider. But for now just focus on in those two areas. Number two, the attention to detail. Well, I'm an admitted nerd, and it's so it's easy for some of us to pay attention to detail. But this in fact, becomes our weakness at times, right? We, we can't always see the business problem. So it's critical to help your people certainly have attention to detail, but also be able to step back and look at the context of that. And then this third bucket here communication, leadership, teamwork, you'll need to apply AI transformation as a team, alright, it's because it will affect business processes as you transform your business getting the insights from AI. So it turns out that your organization already has the fundamental skills for AI, not to build your own data science team, but rather to apply the resources. But what it means though, is that you need to strengthen those current skills so that you can be more successful at applying. All right everybody. Thanks for joining and until next time upskill your team to leverage AI services to transform your business. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at the AI hammer that's looking for a nail. Hey, welcome everybody to another episode of ClickAI Radio. So I was talking with a group of technical people recently. And they talked about a very cool solution that they prepared. So I asked this question I said, so which part of your business will this benefit? And they said, well, we're still trying to figure that out. I said, Oh, you've got a solution looking for a problem. Is that exactly. I said, Well, you know, if you're not careful, AI can turn out to be the same thing, right? One of the biggest mistakes you can do with AI, in fact, this happens quite often is it's the tendency to create AI solutions. without sufficient understanding of the business problem. Everyone gets excited and enamored about the technology, well, maybe not everyone, but some do. And so it turns out that a crisp definition or description of your business problem is of course critical. However, as you know, with AI, you can, you can do a bit of both, in fact, that encouraged both, it kind of looks like this. So start by building your AI solution. And what you do, the reason you do that is you run it against some of your data. And it gives you a sense, without any sort of tutoring or guidance on your part, it gives us a sense of identifying what it can see, right, and what it does is, it lets you understand the potential areas or problems that the AI can start providing answers to. So with those kind of insights, initial insights, you can go back to the drawing board and rethink the kinds of problems which may be addressable with the kind of data that you have, that's actually really critical to do early on. Some groups will spend a lot of time doing lots of data curation, cleaning stuff up over long periods of time. And the whole time, the other hand is saying, Oh, these are the problems, we want to go solve with it. And then the two don't meet, right, you don't have the kind of information that's even needed, that AI would need to dress it. So what you're really trying to do is an interactive iterative, give and take process, as a way to think about it. Now, I've never seen a situation where the first time the AI solution was created, that it was ready to be used by the business. So you should get that out of your mind. Rather adjust your thinking, to consider discovering what the possible problems are, that can be solved with AI by looking at your data, then go back, evaluate the business problems, and continue to iterate and fine tune that right, you can certainly grow and build on your data set after you do that, but save a lot of time by doing that. All right, well, let's talk about what actually is a more significant and critical way to look at this. So in reality, as you know, most of our businesses have many existing systems, right? And our business information, of course, is spread across the systems with lots of redundant data. Well, we of course, rely on our teams and our brains, obviously, to logically connect the information together on these systems, so that we can have the kind of information that we as humans need in order to make decisions. Now, when we use AI, we want to think about recreating that kind of experience. And you can go through the process, certainly of saying let's integrate all of our systems together. But that can be a real challenge. So for example, let's say that you have three systems. This will be a little bit oversimplified, but let's say you have one system for leads and lead management. Then you have another system for ads and ad campaigns to those leads. And maybe you have a third system that's used for closing sales and servicing. All right, a bit oversimplified, but it's sufficient for the example. So it turns out as you know that your customer Numbers journey unknown to them touches each of those systems. And of course, in many cases, those individual systems provide analytics and insights in their respective rights, which is cool and helpful. But your brain ties that all together into an overarching customer journey or customer lifecycle. So this is where AI can work wonders. So even though these systems may not be connected, or even integrated, using AI, along with some data management, the AI can look across that entire data set from those three systems as one data set, right and view the whole customer lifecycle. So then with that the the AI can identify what are the probabilities that a certain lead will translate into a closed sale, even though your own systems aren't integrated to sport that AI can actually do that work and give you those insights. So you don't need to do that integration in order to make that happen. So I encourage you to focus less on getting all those systems connected, or even transfer, you know, transforming or moving your data to one system that does it all. Certainly, that's good. And there's some benefits of doing that. But the key is, don't focus on getting all those connected, but rather focus more on understanding what the predictive golden nuggets are that are already hiding in plain sight across your integrated data, not necessarily integrated systems, but you integrated data and let the I see that. It turns out that this is a nail that the AI hammer can use. Alright, thanks for joining until next time, everybody. Get a holistic view of your disconnected systems with AI. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

We take a look at the three things to overcome the FOMO that occurs when you see the one with the most information wins. Hi Everybody, welcome, my name is Grant. So you're growing your business and you're trying to scale. In fact, you even have the data, but you and your executive team might be missing out on profitable insights. Welcome to insight FOMO right. So hey, there was a book written back in 1997 by Stacy M. Clements. I love the title. It was the one with the most information wins. Interesting, right? And in 2012 Tim O'Reilly was an interview think he was talking about his were conference back then. Anyway, he made this comment. The comment was the guy with the most data wins in at the time I think he had referred to a you referred to back then the NBA, you know, the one with the most superstars wins, right? Well, today, the you know, the one with the most data wins in or is it the one with the most insights when right? You might have a lot of data, but you're not able to harvest the power of information out of it. Tim said something interesting, though, back in that interview, what is that? Almost, almost 10 years ago, he said, data is the source of monopoly power. That's interesting, you know, that sort of viewpoint course it means that it leans into the benefits of large companies, right? those organizations that have been developing massive databases over the years. So what does that mean to an SMB, right to a small to medium business that's trying to compete? Well, certainly others think about this, and there's a movement out there called Open Data movement. In fact, if you check out data.gov, you'll see a whole set of free data sets. I don't remember the latest number, but it's 1000s of free data sets that various groups produce and certainly, you know, you know, give away Well, and while some of them have value, I find lots of times it's difficult to contextualize it. For your specific organization. I think the aspirations are great, though. For for people, if you're able to leverage it, then then more parity, of course, I have found that there are three steps to help turn you around or tour turn your company around, as it comes to making data one of your greatest assets, right, let's say you've got insight FOMO, the FOMO is or the fear of missing out is that others are already leveraging and are competing, and maybe even be winning or beating you at this. So what are three things you can do to get in that game? Number one, it's start organizing your information today, right? Your data today sounds simple. But in a previous episode, I talked about some of the things that go into that, take a look at the episode called control your data, or it will control you and that talks about the things to do for this first step number one, number two, well leverage an analytics and AI platform that and the goal here is it's to use a platform that provides I'm going to get nerdy here, the summation of many AI models, because one of the problems when you don't have such a significant set of data, as you might have, let's say, of course, if you're a Google right or a Facebook or who who have you, then it means that you need to do a lot more analysis on this to find those good predictive behaviors on it. Alright, so it's critical that that you don't get caught up in the world of amusing the most accurate data model, you'll actually you lose money in your analytics if you take that approach. Alright, so number two, again, just reiterate, you got to use an analytics and AI platform that leverages and summarizes multiple AI models that helps your predictive behavior even on smaller data sets. And Firstly, As the SMB business owner, you're gonna want to do that. Alright, number three, so you're gonna want to leverage the experiences of analytics and AI teams that are already down the road. So AI is producing the foundation to leapfrog your competition. And in other episodes, I've talked about why what the level of effort is to take on the skills to become a data scientist, right? Or machine learning expert, right? 30, different skills and so forth. It's a lot of work. So don't do that, right. Okay, as the small to medium business owner, leverage the skill sets of others. So let's talk about what's at stake here. If you don't do those three things, and go after that, here's the FOMO part, Okay, I'm gonna lean into some FOMO. What's at stake here is we're trading off these benefits, we're trading off the ability to reduce operational costs. If you do this analytics and AI stuff, right? You actually will reduce operating costs, you're also going to trade off deeper customer engagement. So the ability to get better, customer segmentation is a critical part of this, you're also going to trade off innovation doesn't mean you can't innovate without analytics, and AI, people been doing it for years. But the rate or the pace of it, and the insightfulness of that innovation in terms of being a better predictability in terms of where you should innovate. That's where you're going to trade off. You'll also trade off some top line growth. McKinsey reported, I think this last year, they reported that 20% of the large organizations are increasing their sales Now, using AI. So top line growth is up for grabs. All right. And then last but not least, certainly least in my list here for right now. Competitive posturing, right. This is a leapfrog approach in technology today. And so yes, you will trade off that meaning you will not get as much competitive posturing, if you In fact, don't do the three things that I'd mentioned a moment ago, all right. But to reiterate, some of the tools and techniques right to do this are very expensive. And as a result, it makes it difficult to get the ROI that you need. So this is not about being customer centric. This is about being customer conscious. And that's a critical part of this. So if you're going to do this, make sure that you go after the three techniques that I've mentioned, otherwise, the costs will become prohibitive, and it'll be hard to get the ROI. And again, this is not, hey, we're going to be more customer centric, you can do that without applying these analytics and AI. But if you're going to be more customer centric, or excuse me more customer conscious, that means you're going to iterate on a more frequent basis, you'll be able to look at ways to pivot more as your customers are pivoting, right? You're the ability to anticipate emerging patterns in customer behavior. And a pivot while they're pivoting is key to this, right. So part of this is mindset got to be thinking that way, if that's an important part of your business. Now, this means that you're not only going to be applying analytics to your business, it means AI is informing those analytics. Now, many analytics, as you know, can be backward looking, right? It's a view of what got you to where you are. And that is a helpful view. But when you apply AI to those data analytics with the right platform, again, you're not only going to get the predictive insights, but you're also going to get the insights that are best for your business operations. It doesn't help as a small business owner, if you get some insights that cost so much or require so much change, that your business operations can't even afford to do that. So it's not about just having the most accurate model. It's about having the right model with the right level of predictive behavior that allows you to make incremental steps to move forward. So FOMO grows, of course, as we discover that the businesses around us are preparing the organizations to leverage these capabilities. And let the FOMO begin because that is happening. Now. At the end of the day, you know, the one with the most insights wins, as long as they act on them and stay continuously curious, that is the critical piece. So he can do insights, even if you do all the things I talked about here, but you don't actually act on it and put it into operations, then it becomes a moot issue. All right, everybody. Thanks for joining in for next time, fix your FOMO and start preparing for predictive insights to grow your business. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at controlling your data. And what happens if you don't! Welcome everybody to another episode of ClickAI Radio. So just about everywhere you go, there is some sort of data being collected on you or about you, right? Whether you're online, walking down the street, wherever it is now, or even driving your car, right? Even, you know, your your mobile device, obviously tracking where you are. And that does sound a bit creepy, right? It brings with it a whole new set of issues. But let's talk about this principle in your business. What kind of information are you gathering in your business? So, you know, for example, do you know what information you are gathering? And do you know where that information is in your business? And do you have duplicated information in your business? And if so, which copy of that data is the correct one? Well, there are some benefits, of course, to organizing your business information. One of the reasons that we do this is to help us understand predictive activities of your customers of your costs, and even of your operational efficiencies. Now, some companies capture a lot of data about you certainly we all know things like Google or Facebook or Apple, how they use the data is really the critical topic. So you'll recall a few years ago, the Attorney General of Massachusetts announced an investigation into Facebook and the data firm, Cambridge Analytica, this was I think, back in 2018. Now, in this case, the allegation was the Facebook data was misused in a political campaign. Now, I'm not here to dispute that. But what I am here to say is that getting control of your data, hence managing it, and using it in ethical ways, to course better serve your customers and improve your business, this is the right thing to do. And that's the area that I want to focus on here with you. So what are some of the activities that you should consider to get control of your data? Well, here's a list of seven items. This comes from lead space, which I mostly agree with. So let's take a look at these seven items. One of those is very first, outline, number one, outline your business goals. All right, so you might choose to increase your sales or, or you might, you know, hey, I want to improve my decision making process in my company. Or you might say, you know, you know, what is the common theme that's taking place across across all of my customers, right, in other words, you might want to improve your customer segmentation or try to find their buying habits and patterns, or perhaps your business goal is, you know, you might want to improve your lead management or, or maybe improve the efficiency of your internal operations. I work with a lot of companies. And one of the common themes that I see across these companies is poor decision making. Now what I mean by that is, it's not that they themselves are making bad decisions, it's that there's not consistent decision making processes and therefore sometimes the decisions are great and other times, maybe not as good. So having consistent insights to guide and influence your decision making process, that's certainly a worthy goal. Alright, so that's number one. So if to get control of the data, my business, think about outlining your business goals, choosing that first number to prioritize data protection and security. Man, we all know about the breaches that we hear and see where hackers get in and take data from organizations. You know, when you talk about data protection and security, no one really wants it or we know wants to talk about it, and until it happens to you, right? So it's not typically that exciting for most but of course, if you don't do it, it's challenging to recover from these data and security breaches. So when you're applying things like AI, it's critical to protect data, as well as information, privacy information about the people themselves. And so this is a really critical area around digital ethics also. Alright, number three, focus on data quality. Now this is a constant challenge, right? controlling the ingestion or the input of your data and its quality, all the way through to consumption is a challenge. I was working with one group and the clerks at the retail stores, they were putting pricing information in a wide range of locations, and it made it very difficult for the business people to run their business. Now today, there are some awesome tools that help to clean and scrub your data. But nevertheless, data quality absolutely critical. So that's number three. Number four, reduce duplicate data. Now, as you know, lots of times we'll have multiple systems in our business, and they overlap in terms of their capabilities and what it is they do and that produces multiple copies of similar data might be customer information or address or what have you. And of course, you know, when you think about that you're struggling with Well, which one of these is the source of truth, right? Which one do I want to turn to and say, This is the master, if you will. So while you might have multiple systems from multiple vendors for your business, it helps to think about your data as a flow through the business like a river, right? So it comes in from data ingestion, to Hey, I'm going to curate it and clean it to him, I'm going to reduce, I'm going to actually get rid of this duplication. And finally, get it to a point where you can do analytics and AI and things like that, to help improve your business decision making now, if you have the ability to reduce the actual number of duplicate or similar systems, that helps tremendously with this as well, certainly can take a lot of money off the table for the business also in terms of expenses. All right. Number five, ensure your data is readily accessible to your team. Well, yeah, what can you say, you know, having the right access controls to the data preventing others that shouldn't get into the data is just common sense. Number six, create a data recovery strategy. It reminds me earlier in my career, I've worked with one client who had a significant system crash. And so they went to their backups. And well, yesterday's backup actually had failed, they didn't realize it. So they went back to the day before and the neighbor and then they went a week back and then two weeks back, and then a month back, they went two months back and finally found a backup that was valid that they could use. And so they lost two months of data had to do a lot of obviously re re entry manually back in those days, right? So of course, it's not just about backing up your data. It's about the recovery. So this is called a data recovery strategy, not just data backup, but data recovery, right? You want to constantly go back and re verify, can I get back to this information? Is it in a valid state so that I can continue to serve my customers? All right. And then last one, last but not least, number seven, use a quality analytics and AI platform for insights. And of course, you know, I got to toss that one in there, right? I'm a fan of good analytics and AI to guide decision making. It's augmented intelligence is the way that we look at it the way I look at it. But at the end of the day, you want your data to unlock the insights and the guidance to propel your business. Alright, so let's take a step back. So these seven steps identify certainly some of the things to do and, and it can feel like a lot of work. And while it can be but think about the other side, if you don't do this, what's the impact, right? While it can impact your business growth, it can impact the quality of the decisions that you're making, it might even impact the security of your business and your data can be compromised or stolen or take your business all the way down. So recently, I was talking with an executive at a major bank, and she made an interesting statement. She said, data is our most important asset. And then she stopped and paused and corrected herself. And she said, No, it's our weapon. All right, that's the power of data. So when you're controlling your data, you're really taking control of your business, which is really one of the greatest assets or might I use the term weapons for your business to help it compete and grow. Alright everybody, thanks for joining. And until next time, get control your data and turn it into your best business asset. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

In this episode, we take a look at the anatomy of growing your business one step at a time with AI. Hi Everybody this is Grant, thank you for joining another episode of ClickAI Radio. So when we think about transforming our business, right, any sort of transformation of your business, obviously requires a vision of where you want to go. And certainly a commitment to make that change, of course requires more than that. But those are two fundamental pieces, obviously. Now managing the change, of course, is one of the more difficult tasks for a business owner. Now of all the things that I could change in my business, which one should I pursue? There's always tons to do in our business. And the other question is, how do I even prepare my team to handle the change? And then how do I make the chain stick so that the organization continues with it over some time? Oh, and then one other question, now that we've made the change, what should be our next change? Right? What are we going to change next? If you've ever watched the Star Trek next generation series, I'm clearly dating myself here. If you ever watched that, there was a character on there called data. And data was brilliant. data was an Android. and data knew a lot of facts. He was fully functional in a lot of ways. However, one of data's downfalls was his awareness of how much information to give a person. There'd be times when the captain Captain Picard would ask a question, and data would ramble on with long answers. data would go on and on and on, obviously, being unaware of the human's ability to either remember the information, let alone to be able to assimilate it, and then to act on it. But AI is a bit like data in Star Trek, right? It can, and often does give a lot of information, meaning many, many insights, it can go on and on and on. And not all of the insights are of the same value or quality. Now, while this might seem useful, of course, it's obviously overwhelming. Not long ago, I was applying some AI for a beauty product supplier, and the amount of insights which came from their data was enormous. And the question was, where to start, right? That's a lot. It's too much. any effort, as you know, to improve your business using AI? should start with your business questions first. So it's one thing to just start looking at your business information. But really, you got to start with the question, Where do we hurt? Where are the pain points? What's the vision where we trying to grow? And of course, we still want to leverage AI to discover those things, that it's hard for our brains to see. But this is a critical step, it's a necessary step where we guide the AI in terms of the business questions that we have first, and it should serve as input to where the AI should focus. Now, even when you do that, there are generally many insights which are discovered. So the real question is, therefore what? Let's say that you've done that work, you've got your business questions, you got some AI insights. Now what what are you going to do with all those insights? So it was on this journey, we discovered what we call a smart step. So when you're making an adjustment to your business, right to apply the AI insights, you're looking for the smart step. And a smart step has these characteristics to it, right? It's anatomy includes these things. The impact, the insight needs to have high impact, of course to your business, it's got to have high relevance to your business problem, as well as high probability, right? Not all AI insights are the same level of probability. You want at least those three but in reality, there's one other key point that you want, you need to select a change that your business In this and your team is willing to make, I've seen multiple organizations who go through this process, they get the insights, it's relevant to the business. But you know, this is the way we've always done things, people start thinking, right? Why would we make the change, right? And so this becomes a big hurdle for a lot of organizations. So you apply AI insights using smart steps. And one of the reasons why is not only to deal with all of the mass of information so that we, you know, we don't go through this Star Trek data scenario, right. But it's also to avoid change fatigue, organization can get overwhelmed with too many changes to make. And as a result, it's hard to measure the effectiveness of a smart step if you do too many of them. So in reality, over time, you mold and groom your business in a series of successive smart steps. So the flow looks something like this, you're on your business, which of course, produces business information. And then you analyze your business information, leveraging AI, then you pick one or two smart steps from the analysis, make the changes to your business, that's your sounds easy, doesn't it. And then, of course, you run your business with those changes in you give them time to stick, and then you repeat that process. Now, we have found that this style of iterative and incremental changes to your business, where you leverage the analysis from AI like this, that is sustainable for a business, otherwise, it's too much now, maybe a way to think about it is as a set of stairs that are ahead of you. But in this case, you decide as the business owner how wide each step is, and therefore, by making that decision, you're also deciding the angle of the staircase, by literally changing the course just the width of the step. So for example, you might apply some AI insights as a smart step in your business and execute for six months, that would be the width of the step, right, you're gonna take six months to see the results of this. And then while you're doing that, of course, you are capturing more business information. And now it's time to at the end of that six months, say, we're gonna let the analysis from the AI give us some additional insights. And then we'll pick one or two smart steps, and we'll repeat and apply those, of course, you might decide to make the adjustments on a quarterly basis, it's really up to you. It's your business's ability to handle the change, as well as your ability to respond to disruptive behavior. And so sometimes the change is intentional, right? It's part of our vision where we're going. In other cases, the width of the step, right meaning how long we wait, before we make the next change is placed upon us right there. It is put upon us by other factors, of course, that are outside of our control, might be the economy might be competitors, might be a pandemic, could be legislation, new taxes, whatever it is, so on and so forth. The key is, is to take those inputs that come upon you, and then change the width of your step, right? Don't wait as long and then make some more incremental steps. Do them a little more quickly, and then you might spread it out over time. So I'd encourage you to consider using smart steps to grow your business. It's where you take AI or the insights from AI in bite sized chunks and you decide the size of those chunks. All right, everybody. Thanks for joining and until next time, apply your AI and smart steps. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In This Episode we look at the question: When do I start trusting AI? Hi Everybody, welcome to another episode of click AI radio. My name is Grant. Alright, so been looking into this problem around trust. And what is it that gets in the way of us leveraging AI? Well, part of the problem comes from some reports. Right. So we'll look at a couple here. One of those was a report from Pegasus and the other was from pega systems, I should say, Now there was a Harvard Business Review report. And of course they they capture what people what we think about in terms of challenges around AI plenty of horror stories out there for sure. Can we actually trust this stuff? We all know about, you know, the Chinese board game go and how AI beat beat the Chinese board game player. kg. I hope I'm saying his name right? Back in 2017. ertainly machines can do a lot. And that's, of course, what we expect what we need them to do when we look at the Harvard Business Review. And what it is that they pointed out some of the challenges around this in in the Journal of marketing, they said based on data from over 3000 people who took 10 experiments, they provide some evidence, all right. And so here's here's the evidence, right, so there's the word of machine effect. And that stems from a widespread belief that AI systems are more competent than humans in dispensing advice. That's kind of an interesting viewpoint, especially when you really think about what it takes to build an AI model. It requires multiple layers of a network, typically, to help create decisions. Now, that's typically too much for us to think about as business owners. And generally, you don't want to have to get to that level of detail. One of the problems around AI, those are expectations of it, given all the hype that goes on out there, one of the best things I found that helps us to work through this is the mental model of viewing it as augmentation intelligence, rather than as artificial intelligence. I think sometimes that misnomer gets us into problem. And we expect it to do more than it than it really should. In fact, one of the best practices around this that I found is, as you're working through making decisions, and you're getting insights from an AI model, you need to compare it against reality. Certainly there are contextual things that the model designer may not have known. And of course, the more that you can get context into the brain of the model designer, the better it is for your business. But that doesn't always happen. So you need to apply the human cognitive sense and the sense of reality that the humans can pick up that the AI is not able to. So what what's critical to this as it becomes more of that collaboration, if we view AI as I'm going to spend time giving feedback into the model to inform it of ill advised insights, or things that just didn't work out. We need the models to be able to learn and grow and make adjustments over time. So coming back to this, to this HBr review, Harvard Business Review, one of the things that they pointed out was convincing a customer to give AI the benefit of the doubt. All right, so Okay, I think that might get a little bit too Ryan's talking about a moment ago, which is we need to look at AI as augmentation to our insights. And one of the key areas, of course, to think about is those ways in which AI can see things that's hard for us to see. That's really where we should should leverage the capabilities of AI itself. So I was working with a business owner recently who made the comment. Well, I'm not sure that my people will actually trust The insights from this, which I find interesting, and then there's this other group that I interact with and work with, they provide an AI platform that I strongly recommend. It's one that we've built and based our stuff on. And, and they made this interesting case study. Recently, they said that, that one of their clients had taken an AI model, and had deployed it and put it into production and hadn't actually told anybody. And what happened is, is over time, people started to notice this score, and the score was integrated into their system. So they were making these these selling decisions. And they started to draw a correlation between when a score was higher, the sales opportunities, they found out, were turning out to be better. Whereas when the score was lower the sales opportunities, were not as good. Rather than tell their employees, you must use this, they just put that into the system and let people start to observe it. And then they started to derive their own conclusions from it. And so rather than this top down approach of you must use this, they took this organic approach that said, let me let me put this in front of them, and then let them experience the results of it. and discover if if what the insights are giving reflect the reality and in fact, augment your reality in a way that it builds your confidence in that system. And so it's a critical way to approach AI. It's not to look at it in a dogmatic fashion, but rather look at and I love that case, study, look at it in a way that says, Let's, let's leverage and build on what we're already doing. And then let's verify is this augmented intelligence actually benefiting and restoring and building our growth and trust in AI itself? Simple example. But I think it's a very telling example, for sure. All right, trust is built over time. We know that certainly with humans, and I believe with AI as well. So though the approach here, get some AI going in your business, do it low key low cost, don't push or force it, and let the people in your team start to experience the benefits as well as the downsides of it. Learn together, refine it, over time, that trust gets built, and then he starts to see the results that will actually increase your business outcomes. All right, everybody. Thanks for joining and until next time, build your trust in AI. Thank you for joining Grant on click AI radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at why AI is ruining your business. All right. Thanks for joining. So how is AI ruining your business? Well, certainly over the years, I've seen several ways the AI ruins your business today, I want to focus on one of them. So first, let's take a look at a report. It came from MIT Sloan Management Review. This was last year in 2020. And they were quoting a study by BCG. That's Boston Consulting Group. And here's what the result was the result of BCG study was that a mere 10% of organizations achieve financial benefits with AI. Holy smokes. That's that's quite the conclusion. So why do only 10% of businesses achieve financial benefits with AI? Well, the ROI to AI gets wrapped up in at least two key areas. So the first is the technical foundation that's necessary to conduct AI. And second, the business problems on which AI is applied. So let's let's take a look at the first one. So what does it take to create a technical foundation? And how do many companies do this today? Well, first of all, one of the things that happens is they start hiring data scientists. And of course, you need data scientists in order to do that kind of work. So that's the first thing. The second is they begin doing AI model creation. Now, this is typically a multi month effort for these organizations. All right, moving on to number three, well, while they're building out their technical Foundation, they start deploying the AI model to something like Amazon or Azure, or Google or something like that, right. And it turns out, that at this point, about 50% of the models actually don't even get deployed. All right, because so far, they haven't been able to find a good, a good application for that. So at that point, you're down to 50%. And then number four, now you start on working for user adoption, hey, let's get the people to actually start using it. And by this time, your model may be out of date, right? So needless to say, by the time you do all of this, right, you've invested hundreds of 1000s of dollars, if not actually millions of dollars. So again, where's the ROI? Well, that's one of the reasons why only 10% achieved some financial results, because you're putting all of that investment upfront on your business, right? That's very expensive way to do that. That's crazy. Don't do that, there's got to be a better way. And there is, and I'll come back to that here in just a moment. But before we do that, let's look at the other reason why AI has ROI problems. So it has to do with the business problems on which AI is being applied. Now, as you know, as it turns out, the majority of organizations start running all of their machine learning algorithms, without clarity on the business problem on which they're focused. So what that means is, they're looking for highly accurate AI models that in fact, aren't actually going to work well for your business anyway. And that is, well, that's bad business. So the net result is, ai gets a bad rap, because sometimes as business people, we get too excited about eye candy, right? And so as you know, there's got to be a better way and of course there is so here's a couple things to think about. Right? Instead of making those mistakes, and ruining your business with AI to address the first problem, right? So the first problem was, gosh, darn, this is expensive and holy smokes. You know, I'm buying expensive AI resources. As a small to medium business owner, you're not going to do that. So don't get caught up in the world of expensive resources or building out your AI team, or expensive AI tools. So if you do go that route, then getting the ROI from AI is quite difficult. As you know, one of the best business rules is to use leverage. So leverage the skills of people have already done it with the platforms that have been tried and true. Alright, so that's overcoming the first problem to address the second problem, which was, hey, there's no focus on the business problem, right? It turns out that if you do this first, right, if you focus on the business problem first, it's actually the best thing to do. sounds cliche, sound sounds trite. Always start with the business problem. This actually influences the AI model development. So as you identify not only what's the business problem, but you also need to identify what parts of my business Am I willing to change? Because it turns out the cultural piece to this is just as important. If not, if not more important, you might have a business problem. But if you can't figure out how you're going to get the people to make the adjustments to take the insights and make the changes, might as well not do it, right. So Furthermore, identify that your business is willing to make the change a Have I got the right business problem, be are willing to make the change. All right now go apply the AI resources, but don't go build out that dog on team, right. So if AI gave you some insights, and you were willing to make the business adjustments needed, now you're in a good spot to actually get some ROI from your business. So it's critical to identify the business problem first, and to do it in an area that you're willing to make a change. All right, then let that serve as input to your AI model development. Now, I promise you, if you apply these two rules, then you'll be in the 10% of businesses that are getting ROI from AI. Now it turns out that, in another recent study, this one came out of McKinsey, that 20% of the large companies are now getting financial results from their sales activities with AI, so the number actually goes up. So think about that. So you'll actually go from the 10 to the 20%, right? So if you not only just applied in general across anything, but apply it to your sales related scenarios, focus on the business problem. First, focus in areas where you're willing to change, don't go build all the resources out and the skills don't build out the team leverage teams that have already built this capability. If you do that. You will not only get ROI, but you'll actually leapfrog your competition. All right, and who doesn't want to be a frog right? All right, everybody. Thanks for joining and until next time, apply the two ROI rules for AI in your business. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

In this episode, we take a look at the question. Is there a way to use AI to find my online content? This question was recently posed to me by business owner. Hey, everybody, welcome to another episode of ClickAI Radio. It is good to be talking with you again. All right. So as I mentioned in the intro, recently, a business owner approached me and asked this question, he said, Is there a way to use AI to find my online content? Because I think it's been taken, I think people are taking my material. And is there a way to discover if they're a poacher or not? So what if that happened to you? Right? What kind of content do you have? That is online? And that potentially people could take in fact, how much of online content is plagiarized? Well, we know that there's quite a bit out there, but we know it empirically. But what what does it actually look like? I was looking at, at one site, they were talking about some some metrics, this was back in 2015. So this is a little old. It said researchers found that roughly the same percentage of papers included plagiarized text, were about half of the pre internet papers. So from their research, they came back and they said, You know what, there's actually more plagiarism in papers before the internet versus after that, I don't know that. I agree with that. That's not what I'm seeing. Right. So I kept looking around, I found another group for this was, of course, in the schooling, and education sector, came back and said most college presidents about 55% say that plagiarism in students papers has increased over the past 10 years. That was just a few years ago that that particular research base, they also went on to say that among those who have seen an increase in plagiarism, 89% say computers and the internet have played a major role that came from a Pew Research poll. All right. So that that line a little bit more with what I think I'm saying, I searched out another one, too, that also confirmed some similar things. All right. So when you talk to some people most sort of nod their head, right when you say, you Oh, yeah, there's plagiarism that takes off. But the the, you know, that takes place, but is there anything that could be done to protect yourself? So there are a number of solutions, of course out there today. And then the question is, well, what's the medium of the content? For instance, Grammarly, most of us know that site, that that solves the problem of, Oh, I'm developing a paper or a document or an article, and it will do the work to help you understand potential plays, or plagiarism in there. And there's a whole host of other sites out there for sure. The question I have for you, though, is, what if you want to find your content out there on Google or on YouTube? And let's say that your content was in the format. Not necessarily just the document, but it was perhaps something on your website, write some some particular findings that you had created or posted? In your content, even some of your course material, perhaps he created or even some digital material, such as, you know, on YouTube, things like that. How do you even scale to find something like that? There are many Google domains, not just google.com, but Google dot , right? There's tons of Google domains around the planet. And so your content has this ability, digital content to grow legs, metaphorically. So how do you even scale to go find something like that? Certainly, you can do it once or twice, but then you run into pretty quickly that it's obviously very labor intensive. So as I mentioned, recently, this business owner had asked this question, and so what we did was we ran some AI to identify plagiarism candidates. And the result for this particular business owner was that there were over 150 sites which has some level of reference either to him or to his material. Now, the next step, typically that we see in these scenarios is to identify if the reference is a friend or a foe. Now and the AI indicates right, it says while it thinks it's plagiarism or Well, not sure if it's plagiarism, but definitely, definitely referring to and and some of your content. The key is, is that most AI and the way we should treat AI, at least my opinion is more like augmented intelligence, right? And so you still need to be involved and say, Well, okay, the AI estimated that right? Or no, you know, this actually looks like it's not a problem or situation. Now, in this particular case, about 40% of the sites turned out to be suspicious and required some level of pursuit to say, Hey, you know, you've taken my stuff, right. And, and, of course, the thing about this is this, this takes place on an ongoing basis. It's not like you run this once, and then you're done. It's that you run it, and then you pursue, and then oh, gosh, it grows more legs. And so you know, you're in it again, you continually have to chase these sorts of situations. So it's it's an ongoing sort of capability now, leveraging AI automation to monitor your content usage, especially this kind of scenario, right? work continues to, to expand and grow. And to find potential poachers, it's a great way to protect your business, right? It's kind of like a business content insurance is maybe a way to think about that. Now, interestingly, we found in some cases, the sites that are discovered, turned out to be friendly, and are actually referencing you. And this has been an interesting outcome, it translates into an unexpected good outcome in that scenario to where it actually allows you to build your network and discover new allies and partners that you may not have known about. So applying AI in this scenario is interesting, finds potential bad guys and also finds potential good guys, so it sort of draws a line in the sand. Now, while plagiarism, of course, may continue to grow in the online world, right, there are definitely some cool tools out there today to help you combat that. All right, everybody. Thanks for joining and until next time, get some AI protection for your content. Thank you for joining grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

In this episode, I have a guest who discusses nutritional AI that you can eat. Grant Everybody, this is Grant Larsen. Welcome to another episode of ClickAI radio. Wow. Today I have in the house with me. Mr. Daniel DeMillard, he is the CTO of FoodSpace, and I am just honored for the opportunity to meet him and to get to hear his story and what he and his organization has been doing with AI. Alright, let's jump into this. Okay, so, Daniel, first of all, welcome. Daniel Thanks for having me, Grant. Great to be here. Grant It is, it is really great to have you here and to have another another patriots in AI, right things that you guys are doing, as I got to know you. And before we started this recording, I was fascinated with the kind of work that you guys are doing and where you focus your use and application of AI. But before we get too deep into that, would you step back in time and tell us? Who's Daniel, where do you come from? And what is it that's brought you to this point to be CTO applying AI in such a cool way? Daniel Yep, so I actually studied economics and finance in school and came across an online class by Andrew Yang on Coursera. And at that point, I just absolutely fell in love with machine learning and artificial intelligence. And I was like, wow, this is absolutely what I'm wanting. Yeah, with my life. So, you know, started studying a lot. Got a job eventually at IBM Watson, and worked at a small company doing what type classification, then I was doing some consulting on the side, where I actually got connected with iosshare, Nike, the CEO of foods, but at the time, it was a lunchbox. And they were developing a consumer facing app that, you know, they were trying to pair people with recipes. And you could set up a diet profile for yourself, and instantly order things online through instacart, based on recipes that you find. And I initially got engaged with them, building a wine pairing recommendation application, where, given a certain recipe will automatically recommend a wine pairing that would go well, really well I Grant ...need that certain kind of food, you're like, Hey, this is the right, right, Daniel ...we're gonna wanna medium red wine with that right or a very sweet dessert wine. Grant Until it this where lunchbox started, they were focusing on solving that problem. Daniel They were focused on solving the one stop shop for keeping all of your recipes together, ordering food very easily. And then also being able to manage your diets, and allergens, and just make making sure all of that was really seamless. And they also had a great mission of trying to mitigate food waste, so that they could recommend given all of the stuff that you have in your fridge that would normally sit there and forget about they could recommend a recipe and for you to make of the things that you've already selected. And so Grant all right, very good. All right. So so then you bumped into them, and you started to work with them. Tell us a little bit about the transition over to food space. How did the vision change? Daniel Yeah, it was actually pretty serendipitous, and rather abrupt. So back in 2019, io was a part of grocery shop, which is a big conference in the CPG space and food industry. And he's trying to pitch this idea of lunchbox to brands and retailers to get them to sign up for it. And they all have basically kept telling them the same thing of like, hey, it's a great idea. It's super interesting. I would love to but what you're asking for with all of these like dietary profiles, and The information necessary to build these types of recommendation algorithms. It's we just don't have it, we don't have ingredients and digitized nutrition labels. And, you know, we have the very minimum. So given that that information is like, Okay, well, we've got to take a step back. So he calls me up. And he says, you know, hey, Daniel, would it be possible given a bunch of computer or a bunch of e commerce images, to extract the product information from there and actually read it from those images. And I had done a little bit of stuff with computer vision by that point, using, like pre trained, convolutional neural net models on image net, and then using transfer learning to identify key regions. And so I stopped and thought about it. And I was like, yeah, you know, I think the problem is tractable enough. And the technology's in such place, now that we can absolutely solve this problem. And so yeah, two years later, we now have a model that can classify things that matter of seconds. And we're gonna extract that product information and seconds with 99.7% accuracy 99.7. So if I bring I can assume to you, and you can see the label on it, you can figure out what this is, you can identify it. Exactly. So we'll extract things like brand name, product, name, net weight, the ingredients, the nutrition label, any certifications, such as whether or not it's recycled, kosher, non GMO, vegan, all of that awesome stuff, including marketing claims, like low sodium or, you know, contains less sugar, those those types of things, well, pull out all of the relevant information from the product label. And we'll read it in the same way that a person would read it. A lot of other products that do something similar or are entirely based on the universal Product Code code, the UPC barcode on the back, where they're basically just looking things up from a database, a database might have inaccurate information, it might be out of date, so might have been accurate at one point, it might have been transmis, transcribe with whoever transcribed it in the first place, we're gonna read that label and not image, the same way that a human would label it so that it's, we're going to the ground truth. Grant So PC based, you're actually extracting the actual text, you're figuring out what this is, and then the semantics of it. What does that mean? Oh, it means this ingredient, this so much in the, you know, in terms of the amount of the ingredient, and so forth. Daniel Exactly. And then we will also derive new information from that information that we've extracted, such as whether or not it's going to be had certain allergens. If it contains peanuts, we're going to let you know if it has a peanut allergy. We're also going to determine whether or not it conforms to different diets. So I'm a vegetarian. And I'm constantly reading labels for obscure things like whether or not my cheese contains rennet in it, which is a animal derived enzyme. So we will read all of that for you, and then derive whether or not it's going to correspond with with your diet. Grant So can you talk about some of the use cases around this? So are you targeting b2b scenarios? Are you doing b2c? Is that something that I as an end consumer comes in interacts with it, let's say through through my cell phone? or How are you? How are people going to consume this this cool platform? Daniel Yeah, absolutely. So right now, we are primarily focused on optimizing the e commerce experience. So if you're on Walmart, or Albertsons or target, and you are using your favorite grocery delivery app, or you're going in to do pickup, all of that purchasing decisions are happening upfront on the e commerce website. And at the very least, we want to make sure that that information is present and accurate so that you can make the decisions yourself whether or not at the very least, you can see that ingredients label and search to see if that rennet in gradient is there, or if you're trying to, you know bulk up make sure that it has enough protein or has low sugar, low fat, whatever your dietary needs are. We want to make sure that information is there. But we really want to enable a more optimized ecommerce experiences where you know in your little left side of the toolbar, you can select vegetarian or pescatarian or low sodium diet or a South Beach diet, or I'm allergic to shellfish, and automatically only be shown products that correspond to your dietary needs. So we really think that optimizing that e commerce experience and the search is where we can have the largest impact right away. Grant So So some of the health profile of the person intersects with this, is it coming off? Like, I don't know, like, like the fitness app? Or is it coming off of other sort of apps? And then are capturing that health information? How do you integrate with that? Daniel Yeah, absolutely. So right now, we are basically providing the data to the retailer so that they can make those optimizations. But certainly being able to integrate with, you know, My Fitness Pal, or Weight Watchers would help optimize these experiences. And we are in discussions with those types of companies as well to improve their databases. So that you aren't, you don't have to manually type in all of that information on your fitness app, you can basically just look it up in the database, and it's accurate and recent. One problem that we've seen is that 30% or so of data is of grocery products are updated every year. So anyone I think use one of these apps has the experience of typing in their information, finding out finding the correct product, but it's a little bit outdated, the calories are a little off the nutrient profiles a little bit off, we're gonna make sure that it's updated. And in the right place. Grant That's interesting. So you talked about accuracy, the model accuracy for AI? And I think you should say 97%. Right. 99.7. Daniel Yeah, we are absolutely religious about that is, wow, you know, that is the problem that we're trying to solve. Right now, if you look up any product, on, say, a large, very large e commerce website, like walmart.com, there is a somewhere between 40 and 70% chance that there is at least one mistake on that website, regarding just the ingredients and nutrition information. So if you're trying to base you know, your health profile on that, it's it's an inaccurate, so we are just absolutely religious about getting every single piece of information. Correct, at least as so far as it corresponds to the product images. Grant So is this is this just for humans? Or is this also food for animals and pets? And how does this work? Daniel Yep, so we've definitely just, we started with humans, we are expanding to pet food and being able to build attributes around that two things like wet versus dry pet food, whether it's for a large size dog, or a small size dog. And all of those attributes we're hoping will also assist in that product search and discoverability so that you're not being shown a dog food, that's, you know, too too big for your small small dogs. Right. Grant Right. Right. Okay. All right, that makes sense. And then in terms of what we're talking about, who it's relevant to terms, your current market, so it's for humans, obviously, animals in the future. But as we think about the humans, this English base, is it other languages, Spanish or Mandarin or others? Where are you in terms of multilingual? Daniel Yep. So, you know, food is, I think, sacred to everyone everywhere. And as we move from this, in store grocery experience, where you're, you have the product in front of you, you can pick it up, and you can read the label to an e commerce experience, where somebody might just be dropping that off to your doorstep, and you don't see the product until it's there. We really think it's important that we have as larger reach as possible. So we definitely are working on expanding our algorithms to apply to different regulatory regulatory environments. You know, Europe has, I think, 12 allergens, whereas the united states currently has nine, and they just added sesame, to their allergens. They also have different nutrition labels and different information that they require to be on those. And then in addition to that, the different languages that are actually present there, and all of that obviously presents different technological issues, custom models for each of those markets, but really what we've spent a lot of time Building and working on is creating models that can quickly adapt to these new domains and building a really robust training pipeline. So that basically all we have to do is collect more data, instill a little bit of domain expertise, where we have to learn a little bit about that new market or that language. But after that, we can adapt our models very quickly to that new. Grant You know, I just have to ask, given that I love the AI piece of this, as well as just the benefit that you're bringing to human family. I mean, that's, that's huge. When I think about the AI portion of this, I think, how, how was building that model? I mean, how you have a lot of cans in your food storage now. I mean, how much? How many boxes of Cheerios did you buy? I mean, that's amazing. How did you get through all that? That's just that, right? There is a big challenge, right? To get through enough instances? Daniel Yeah, um, I Oh, and Dan, my business partners, they spent a lot of time getting kicked out of grocery stores, because they kept picking out prod products and taking pictures with their phones. And so they were kicked out of a few grocery stores, I think they learned to, you know, explain what, what they were doing their first after a little bit, but certainly a lot of time, taking pictures of your entire pantry. Going around the grocery store, just pick it up as many random things as possible. That's creative. Grant Yeah, that's, that's really great. If you have any particular challenges in terms of the kinds of food and other words, some things don't have labels, right. So certainly asparagus typically right or decent, things like that. So how do you deal with that? Daniel Yeah, absolutely. So currently, we only support branded foods. So it does need to have that product label. But it's interesting that you should mention certain types of foods, we were doing a analysis an audit of our accuracy. And we were noticing that a certain product category, yogurt, in particular, was creating a lot of issues for us and was very low accuracy. And it turned out that the curvature of the yogurt container, and then the fact that it kind of tapers down, creating a lot of issues for OCR model, where the text is kind of getting bunched up at the edges of that, you know, yogurt container. So we actually had to like build a specific model just to handle those types of containers. So certainly, you know, a lot of our time and effort has been focused on the corner cases in those weird scenarios where that are particularly difficult. The like, very simple run of the mill cereal box, where it's a nice rectangular box and the nutrition labels very prominent. And it's a very usual format that's easier to solve that most of our time has been focused on these weird one offs, like these tobert, tapered yogurt containers. Grant So so let me think about because I love the, again, this problem that you're solving and how it benefits people and their dietary needs. When I think about how people can consume this, what's the way that they will be able to interact with this standard? And what's the state of what space is doing today? Is it? Is it out there ready to be used? Or Where are you guys? Daniel Yeah, we're currently working with brands to get their data to the retailers and some retailers are a little bit further along than others and optimizing the, you know, experience for you where you can set up those dietary profiles for yourself and only be shown the products that correspond to your values, or do you only want organic food or you have a gluten intolerance, only being charged on those foods that correspond with those values or dietary needs to just getting the product information out there to the retailers in the first place. We're also working with some initial engagements with smart appliance manufacturers, things like smart fridges and smart micro microwaves, where you can simply scan the product, either using the barcode or just the front of the product and instantly have your oven or your microwave set the time timer or the temperature for you to cook that product for you. Additionally, being able to do things like recipe planning based on the products that you have in your fridge, being able to order products from I'm a retailer directly using the feature on your fridge that is based on your dietary profiles and just you never needed to get on your computer. And you could just order, you know, your gluten free pizza directly from your smart fridge that is linked to a product database with information that we're providing, we really think that more and more people are going more and more of our purchasing Our food is going to happen in this virtual digitized space, whether that's through your computer, your smart fridge, and the more that information is available, the more that we can build a more customized experience, and really make shopping easier as well, so that you aren't ever being shown products that don't correspond to your dietary dietary needs or your values. You know, even being able to set timers and things for microwave, it might sound trivial, but it really should make the entire cooking, cooking experience that easier for you. Grant You know, I certainly could benefit from walking up to the fridge and say, what are the possibilities of what I can create from what's in there, my wife will do that she's got that AI model already in her head, but I don't have that model, same set of food and go, there's nothing in there. And then she can craft you know, miracles out of it. Daniel So yeah, I'm the exact same way. And, you know, you could you can set user profiles for everyone in your family and say, Hey, you know, I'm a pescetarian. And my daughter's gluten intolerant and my son really only it's organic food, and being able to mix and match all of those constraints, we can figure out what recipe and you know, what to eat for dinner, right? Grant And so it sounds like, like, like, we've done that, that South Beach diet multiple times. Sounds like you know, you can literally walk up to your, to your fridge at some point and say, Hey, what is it that I can make that is in compliance with the South Beach diet? Daniel Exactly. And then things like, you know, macros counting, like calorie counting, and counting how much protein that you're consuming, would be a lot easier using if all of this information is digitized, and you're interacting with it in a smart fridge type environment where it can track what you're picking up and making. So I think entering information into one of those calorie counting apps is often a pain and I think, a limitation for a lot of people. So anything that can mitigate some of that barrier to getting healthier and keeping track of what we're putting in our bodies, to me is very much welcome. Grant So we've talked about the art of the possibilities around this right? What is it that this can bring the people that dramatically influences and impacts their health? What do you see in terms of the downsides? What hurdles or challenges? What could get in the way of either people adopting this or getting value from it? What what concerns or challenges do you see there? Daniel Yeah, so some of the things that we've seen in the industry about the difficulty to use this type of data is, every retailer kind of has a different format for how they ask for data. Some retailers want the units and the nutrition and the value to be separate. So if you have seven grams for protein, sometimes they want us separate key for seven and a separate one for brands. They might call things different. Some people might think call things, UPC, other ones call it barcode. Other ones call it product ID. So that's some of the work in transit translating the data mapping or the data model to each of those retailers can be a major bottleneck for a brand say wants to get their data to Walmart to Albertsons to target. And they basically had to look at these like massive Excel spreadsheets, but like 70 columns or 150 columns, and manually copy that data over and it's a huge pain. And that that is one of the major reasons why only the largest of brands have the resources to get their data digitized in the first place. So what we do is, you know, we're going to first extract that information for you automatically from your images. You don't have to hire a team of people to do that extraction in the first place, where we've also built these mappings for the top 10 retailers where we can automatically syndicate and get the data in the format that they want to see. Whether that's directly through an API, and just automatically updating your information through an API, fortunately, some of the grocery industry isn't quite as forward thinking. So a lot of updates are just made through Excel spreadsheets. But we'll create that Excel spreadsheet for you. So that it's basically just a matter of sending that over an email. And I think that should mitigate a vast majority of the bottlenecks currently faced in the industry. Because some of the, I could just imagine being a brand manager and be like, Alright, well, here's my data mapping. But then there's these close lists for Walmart, where, you know, I'm supposed to put in a certain beef cut type for this product. And doing that, for every single one of my 150 500 products, that is going to be a huge ass. Grant Yeah, it has said that. It's one of the things that drew me to this. And when you and I were first talking about this recently, which was, I feel like the work that you're doing is not only scales to the larger brands, but also it's pulling out all this information that makes it available, even the small to medium business space as well. And so feels very scalable, therefore approachable to benefit a lot of people, lots of different scenarios. Daniel Yeah, absolutely. And we try to make things as easy as possible to get integrated with our system. So, you know, our simplest use case, if you already have data and a list of URLs for your product, you're going to send us over a CSV with your URL links and the product IDs associated with those. And we'll download those images for you and process them through the system. And now you can download it and whatever data format you want, you know, CSV or JSON, or an Excel or in target specific taxonomy format, or Walmart's or Albertsons. Or you can upload it through a, you know, drag and drop upload portal where you can just drop, drag a folder of your product images into that upload portal, interact with an API, or even give us access to your put them up on an FTP server and point us to it and we will download the images there. So it's really trying to make things as simple as possible. So that whatever your tech stack is, and whatever the size of your organization is, we can help you get up and running as quickly as possible. Grant Lots of integration strategies for if that's powerful. That's awesome. Alright, so let me ask you, if the for the people that are listening to this, where are you going to direct them to what's what's, where are you going to invite and where do they go find out more about this? Daniel Yep, so a FoodSpaceTech.com is the place to find all of the information. Grant Okay, FoodSpaceTech.com; Awesome. That's great. Daniel Okay, we actually just released a brand new website. So it looks great. And you can look at it now. Grant It looks awesome. I've asked you a ton of questions. What questions Haven't I asked you? What would you like to share that I haven't prompted you? Daniel Yeah. So I think that can be skepticism and the world of AI. And, you know, whether or not we can do what we say that we can do. And we are, again, just absolutely religious about product accuracy. And I think it's good for anyone who knows a lot about AI to know that AI can only take you so far and the machine learning is only going to get you so far. So we've spent a very large amount of our time building a very sophisticated human in the loop process, were really trying to figure out where the ML system is doing well and can be trusted, versus when a human needs to come in and take the reins and make a more educated more critical thinking decision about things with things like building known rules between the nutrition label. So calories is a very direct calculation from total carbohydrates and protein and total fat. So we can basically just cut check to see if that calculations done well. We can cross check our nutrition information against our ingredients where we've actually built models where we can predict certain nutrition elements based on the ingredients. You know, we know that a cookie were the first ingredient might be butter or sugar is going to have more fat content than something where the first ingredient is carrots. So if anything falls outside of those ranges, we can alert it and say, Hey, something's gone off the rails here, we should make sure if human takes a look at it. For certain container types, we know we are struggle a little bit more things like that yogurt container. So instead of relying on the ML models that work most of the time, but not all the time, we can just flag that certain product type for review by a human just to get another check on it. But we really think that the just to solve a problem, at least in the near term, using AI involves humans in that in the loop and being able to really distinguish that the easy cases, the happy path that I like to call from, hey, we've seen a new domain, you know, maybe it's a it, both English and Spanish is written on the back. So our models are getting a little bit confused. Let's flag that for review. Grant Yep. Yeah, I really appreciate the qualification around AI. I tend to prefer to think of it as augmented intelligence than artificial intelligence, I feel Yeah, I feel like that's the state of where it really is. There's so many things out there, like, Oh, you know, ai robots and Terminator that give a real misperception. But, but today, this stuff around deep fake, right, is really starting to become, you know, a bit of a challenge, right, in terms of creating even less trust around this. So it's a real misuse, if you will, of that. So in this particular case, this is obviously real, honorable use of AI itself. But the whole if we can keep people's perceptions to this is to augment your thinking process, right? Your cognitive behavior. So even though it's coming to you and saying, This is what you could or should eat, or this is what makes sense, you know, from a nutritional value, you still own the responsibility yourself, right to end up saying, Yeah, this is something I'm gonna do, I'm not passing that off, you know, to the AI model and say, do all the thinking for me, right? Daniel Oh, absolutely. And I could not agree more, you know, we are just providing the information to you. But it still requires that critical thinking and decision about your own values and your own goals to make the final decision about what you're going to put into your body. Right. We're just trying to make that easier. Make that whole decision process simpler. Yeah. Powerful. Grant They're very cool. Okay. All right. Any last comments, before we wrap up here, Daniel? No, it was a great to be here at Brampton. Thank you very much for having me. Yeah. Thanks for taking the time and for sharing this cool platform that you've put together everyone, go take a look at food space tech.com. Thank you for joining and until next time, go get some nutrition. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

What I didn't know that I didn't know cause my very first startup company to fail. Thanks for joining, this is Grant. So we started this company, I was right out of college. And as you probably recall, I turn to my wife and said, Hey, I'm not going to take the secure job, I'm going to go do this, this startup company. So we headed to Chicago, and we started working on this company, the focus of the company was to deliver some technology solutions to the healthcare world. That's that's honorable, right? Well, as we all know, in the world of all possible knowledge, there's the sliver of things that you know, and then of course, there's there's the section of, you know, the things that you know, that you don't know, and then there's this big wedge of the things that you don't even know that you don't know. Well, what we did know was that my main partner, he had years of healthcare experience and tons of connections. So we knew that. All right. And then what we knew that we didn't know, was we weren't familiar with the healthcare payer details, right? We were focused on health care providers, right and the solutions around there. But what we didn't know that we didn't know, that was the level of investment that these health care providers were putting into technical solutions. It far surpassed our ability to support, right, so just a couple of us guys coming in saying, Hey, we're gonna go solve this serious healthcare problem for you. Yeah. Now, the other thing that I didn't know, that I didn't know, was that my partner was unwilling to accept input, he was unwilling to change course, and align with the market better, right. And so that unwillingness to change, that's a common challenge for many SMBs. Hackett's common challenge in big companies to, but the ones that pivot both the large as well as the small companies, that pivot, right, that shows some willingness to adjust course, with some market input. That's a critical skill set for business survival and growth. So of course, the key is to be open to change. And there's a few things to consider there. Number one, commitment, right, make a conscious commitment to be open number two, trust, trust that you're heading in the direction. Not that you want to say is right, but you're always heading in a direction of new knowledge, right? second, or third curiosity. So allow yourself course to experience the curiosity of not knowing, right, some that sometimes we get uncomfortable with that we want to stay safely in the things that we know. But curiosity is actually critical to the business pivoting and growing. And then fourth, is around freedom. And that's basically the freedom of experiencing something new and unexpected. So getting open to change is a critical part of any SMB. Now, you know, dive I have plenty stories of things that I didn't know that I didn't know, one of those was even years before the startup that I did. This was well before college. But before I went into college, I had an opportunity to run a business for a friend of mine, and he was going to be away from his business for a year so and so on paper, this looked great with like a great deal. He had set up about $100,000 in business contracts, all I needed to do was to fill on those and you know, keep the profit. Now, the team was small, and this was back in the 80s. So everyone had big hair Anyway, you know, to a young pre college age kid this this looks like a lot of money. So I'm thinking hey, how could I pass up such great opportunity? Oh, and by the way, I could use that money later for paying for college. So in my mind, it was a win win win. So I told him, yes. Well, I got a few weeks into this and he hadn't left yet. And one day I got this pit in my stomach, right? Something just did not feel right. And this not at me for several days. So I eventually got some alone time, right? Just ponder and think about this right? What is going on. And during that time, I got some clarity. And I went back to my friend and I turned down the opportunity. Now I thought the story would end there. A few months later, he reached out to me, and he informed me that, that the other business partners that were involved had embezzled a bunch of money, and the company went down. And he was tied up in in legal matters. Now, certainly, I didn't know what I didn't know, right? I didn't know that these potential partners were planning to do something like this, right? We're right in the middle of when I was trying to make make this decision. Now, in this case, gut instinct, some inner voice right gave me some reason to say, to pause and reconsider, right, which I did, of course, and, and I'm grateful for that. Right. It didn't tell me of the impending scheme that was about to unfold. But it's so how we learn about things that we don't know that we don't know, that tends to vary, right? Sometimes, it's got instincts, right? Other times, it's some AI insight, we need all of that when we're running our SMBs. Right, we need to understand and be open to the change of constantly learning and, and constantly not knowing and have that curiosity for that. That's a critical aspect of running our businesses, of course, well, there's some techniques around this right. One of those is if you want to practice with your brain, allocate some time on a regular basis to learn something new, right. And so that's literally it could be set aside 10 minutes a day, or whatever it might be. Maybe it's while you're driving in your car, you're listening to a podcast, or whatever it is put in some time to learn and to expand what you're what you know, and what you don't even know that you don't know. Because there's incredible cross pollination that takes place from other industries into yours. So recently, we were working with a group who had been in business for quite some time. And they made the statement, they made the following statement. I'm paraphrasing, but they made the statement about receiving some insights from AI. They said, Look, we know enough to keep the business going. And if we don't know what we don't know, how's that gonna hurt us? By the way, they paraphrase, I'm paraphrasing them. We've been in business 10 plus years. And so you know, we actually don't need this, we already know the business. Alright. So in contrast, we were working with another group. their mindset was different from the beginning, it was more like, hey, what is our business information mean? And what is it that we don't know? And what changes should we make? They were, of course, informed of many things in their sales that were both effective and ineffective. And many of the insights were things that they didn't know. And they didn't even know that they didn't know. All right, well, they approach the insights, the AI insights much different than the other group. And as a result, they actually experienced a different outcome, a great outcome. So here's something to think about. One is find new ways to solve some of your problems by looking at other areas, right, by encouraging or bringing in additional ideas or insights. It turns out back in the feudal days of Japan, the samurai, they were not only warriors, of course, and I'm no expert on this, but I discovered that their mindset, and their approach was that death was not to be feared, right. They wanted to die honorably. Well, that's interesting, right? That's amazing. But one of the cool things about them is that they were cultivated in many areas, like the arts, so they took classes and calligraphy and bonsai cultivation and tea ceremonies and origami and flowers and painting and so forth. And they, these, each of these areas influence their fighting skills, right to ensure that they, they could be this Ultimate Fighter, right that ultimately develop them and their abilities as as a samurai. That's amazing as they tried to live the Bushido code. I'm amazed with those kinds of stories, right, where they're willing to be open and look outside of areas. So mindset is a key, certainly not only with AI, but with most things. And so in our SMB businesses adopting a mindset, like the samurai, right to learn from other sources, that's real similar to the AI insights and applying it in your business fight. Hey, everybody, join me on Thursday for a web class to discover what you don't know that you don't know about your SMB. Go to ClickAI.com/BreakTheTrap Looking forward to seeing you there. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback and Remember to download your FREE eBook visit ClickAIRadio.com now.

Hey, we tried it once it didn't work, so why would I even try AI? Hey, this is Grant, thank you for joining in this episode. So okay, we tried it once it didn't work. Why would I even try AI? Well, it turns out the global artificial intelligence market, I think Market Insight report estimated it to be I think it top $40 billion in 2020. That's a cumulative or compound annual growth rate at 43%. That's pretty amazing. So there are some amazing achievements around AI. However, you know, there's still a lot of bugs to work out with this stuff. Let's take a look at some. Okay, so let's take a look at one of these failures. That was gender phi, right? 98.4% probability that the word male match to the word professor, and if you put in the word stupid and come back and say 61.7% was female prediction, oops, what the hack right? what it what a mistake. Here's another failure, right, this facial recognition, I'm sure you've heard of this one. You know, you've got some 94 year old granny who has to be held up, you know, in front of the picture, you know, in her Chinese bank, to just to get her face, right, just that there's some usability challenges there, right? Uber course walks away from driverless citing adolescent AI, obviously, Amazon, they axed AI for their recruitment, because they found out that we've had bias at all had a lot of bias in it. And that's, you know, eventually damage Amazon engineers realize that, that the AI was indicating that the male candidates were automatically better outs. Not good. Here's another Amazon failure. Right? There facial recognition software match 28 US Congress people with criminal mug shots. Yep. Yep, that's facial recognition thing is, is tough. All right. So in short, what does AI actually work for? Right? Where does it actually succeeding? Where's it doing? Well, well, McKinsey points out that AI is shining in the area of big companies that are now reporting 20 plus percent of their revenue coming from AI insights, that's pretty big. Two thirds of those big companies are using it to increase sales. And these companies plan to invest more into AI in response to COVID. So that's a success story seems like a lot of these facial recognition things. Heck, I think even IBM was applying it, you know, in some of the cancer, cancer use cases right, with with their Watson technology, and the doctors rejected it, claiming it was giving some some really bad advice. So the areas where he tends to do better is when it comes to the area of sales and sales planning. Now, the question is, is how do you identify the best AI project? What does that look like? Well, in typical, you know, typically, what you're looking for is something that's going to give some level of conversion, or attrition impact. So if you can increase your sales, conversion, or reduce the attrition of your people, that's typically where it tends to do well. So your sales and account management teams, typically are benefited from this. So you got to build a business case around it. In fact, there's an article by a guy by the name of Jeff Caitlyn. This is on Forbes, he pointed out a couple things here. He said, Look, typically you're looking to predict customer churn or you're looking to handle and review or handle online reviews better or improve your messaging. But the bottom line is, you're really going to build out a business case for AI much like you would other business cases. What does it take for me to put into this, what's the effort and then can I get something out of it and the place that the large companies tend to be getting some traction with it is in this sales improvement area. So three things here. Number one, AI has been bringing positive impact to sales and Supply Chain use cases number two, identify which sales or supply print you know, supply chain problem areas that you're going to focus on. And then number three, of course, as an SMB don't go build this out yourself. It's too costly, it takes too long to turn it around. So hey, I invite you to join me Thursday in a web class. To bring some positive impact to your sales and supply chain scenarios. Go to ClickAI.com/BreakTheTrap Look forward to seeing you there. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we'll take a look at how to run from your SMB pain. Okay, welcome, everybody. So AI requires months and years of training before results are seen. Where do we heard this before? Right? Well, we saw some material not long ago, of course, and we've all experienced this on Amazon, right? Where they're spending well over $20 billion annually on you know, research and development, things like that. What they do is they collect data to give product recommendations for AI, right, and we've been on Amazon, we see a product. And of course, out of that then comes this other set of related products that might be interesting, right or might be related to what we bought. That takes a long time for them to gather that. And as an SMB, of course, we certainly can't take that long, or certainly even spend at that level to get those kinds of AI results. In fact, when you look at it, some people feel that it takes six months just to learn how to do data science. Now that's just to learn it, that's not to actually become an expert, and be proficient at it. That doesn't even include building an AI model. And then finally, eventually getting some business results. Another group, they say that it takes their teams 30 days just to get their AI models deployed and functional. As an SMB owner, you don't have time for that. And another set of group said, it takes six months just to build their AI models and deploy them six months, can you imagine as an SMB owner taking six months to do that? Now, I found out looking at some other stats in over 60% of the RL artificial intelligence projects. They don't even get past the initial proof of concept and data preparation stages. So you know, pay, you're just trying to figure out is this even feasible? over 60% doting get through those phases, that's, that's huge. So look, take, take the pain out of your SMB and don't even do those steps, right? Put that into the hands as someone else Unless, of course, you got you got money to burn, right and you got time to waste. I guess if you do, that's the way to go. But as an SMB owner, it makes, you know, doesn't really make sense to build out all of those teams and to take months, eventually, to get something to help your business grow. I was looking at a case study recently, where a group came back and they had used some AI. And they said, Look, the time and the effort that it took for them to find $3 million in additional sales leads, it was only a matter of a few days. That's huge. So by the time they got their data, a little bit of cleansing, and then ran it through this advanced AI platform, hey, came down to just just in a few days, they found $3 million in additional sales leads. Now the point is, that obviously is a very short time with a huge turn right a huge, huge result for this medium sized business. Nevertheless, it is indicative of the other side of the spectrum, which is you're not spending the months, lots of months, your eye to get these teams set up to go try to find some sort of business value. So to reduce the time that it takes to get value from AI for your SMB. There's three steps that I'm going to go through here writing three steps to reduce the time that it takes. Step number one, list the areas of your business that you're trying to move away from pain, it might be in the marketing part of your business might be around sales, or the supply chain. But the key here is you got to get specific with this because as we know as a human race, we tend to move away from pain faster than we move towards other emotions. Alright, so that's step one. list out the areas of your business that you're feeling the pain. Step two for each of those areas might be in the sales area. Identify with Current processes are, are the activities that you do around that right where the pain is actually occurring. All right, so that's, that's step two, step three, for each of those processes, identify which ones you're willing to make a change on. Alright? That actually becomes incredibly, incredibly important that third step. And the reason why is, I've seen time and time again, where some AI insight comes into an organization, it sits on a shelf, right, there's an unwillingness to actually make the change or to adopt. Right now, I want to walk through an example of these three steps. So let's go back to step number one, list the areas of your business where you're trying to move away from pain. So let's say that the pain in this example, let's say that the pain was, I don't know what changes to make in my business, because I don't know what my business numbers mean. Alright, so let's say that the business numbers in this case are some sales numbers. Right? That's step one. Alright. So we've got we got some pain identified step two, for each of those now. So let's let's identify what the current business processes are, that we do. So let's say we have two processes. Process number one is with our sales team, we do a monthly sales planning and forecasting process. All right. So that would be one area around the sales that I need to know what those business numbers mean, that would affect that. And let's say process number two is with the business team. So that might be the CEO, the President, etc. That's a quarterly budget review, let's say the of those two processes. Alright, let's now go to Step three. And step three is critical, because this is where you now identify, which ones of those processes are you willing to change, when you might have some pressure, right, there might be someone in the organization that says, I'm not willing to change our quarterly budget review process, I'm not going to take some, some insights from that. So let's scratch out that one, let's just say not changeable, the CEO, the President, whoever, just saying not willing to do it. But let's say in that first process and sales team, they're interested in, hey, if you had some additional insights, we might make some changes then, in our monthly sales planning and forecasting process. Alright, so now we've got this setup. It's first we identify the pain, the pain is, we don't really know what our business numbers mean. And then the second step, hey, we not we know what some processes are, that relate to this pain once in the sales area. And then third, we picked the sales team, monthly planning forecast process, right, you got to have those three. Notice we haven't talked about data we haven't talked about, go get your data, go get it cleaned up, ready to go? No, as an SMB, you focus on the business part. And on where are we willing to make changes? Given that we get some insights? All right, that that's the critical piece. Now, let's say then that some AI gets applied, you don't go apply it, you have someone go apply the AI. And what comes back is some insights about sales effectiveness. Okay. And I'm going to give you a couple in this example, insight. Number one, the AI comes back and says, you know, sales on Mondays and Tuesdays are less effective in the southwest of the United States for two products. Okay, so the AI looked at the numbers and figure that out. It's like, okay, that's an interesting insight. Insight number two, sales staff member a will call the staff member, a sales staff member a has a higher closing rate for product x in the central us for a specific demographic, right. So it might be for people of a certain age range or whatever. But let's say it's for a specific demographic. Okay, so the AI has found those two insights. Now, this is where the AI gets interesting. The AI then produces something called a smart step. Smart step is forward looking, right? It's predictive, it's okay. Now, therefore, what should we do moving forward, and the AI comes back and says, you know, to increase sales, there's an 80% plus probability to have staff member a sell product x to a target demographic, in the southwest us on Thursdays, Fridays, and Saturdays. Right? So the question is, let's say that we have that prediction coming back from AI to grow sales, do these things highly profitable, or, you know, high probability to go do that? But question, the real question is, what changes will the business be willing to make to adopt this smart step? Again, we said hey, we got to process our monthly planning forecast. Would we be willing to make some adjustments to apply that so Given that the sales team was willing to make those process adjustments and the, you know, the quarterly planning and budgeting process team wasn't. So given the sales teams willing to do that, then what you do is you put in place the strategy to apply that AI smart step, right? In other words, we will make the adjustments, we're going to get staff member a to actually go off and do that work and focus on that particular area. It's critical to do that, because really to be successful with AI, yeah, you need the data. But as an SMB, don't, don't go focus on the data piece focused on what problem? Where's the pain? What am I willing to change, and then let me go put that change in place. So to sort of be successful with AI, three things. One, the AI insight, you need to have that obviously, otherwise, this is all moot. So you need the AI insight that augments your intelligence, right? This isn't about saying, the AI is taking over your brain, it's more it's bringing insights and uncovering things that we don't often know, we don't know. So, a we need that. So that's number one. And then two, we got to have the willingness and the ability to make adjustments to your business, right to apply the insight to some either existing process, or maybe it's creating a new one, but two, you got to have that. And then three, this is critical, you gotta track the impact of the changes. So how did things? You know, how did things come out? At the end? What was the results of that? So the scenario I just walked through, actually comes from some customer examples. You know, I liked some of the big company timelines, where it's multi month, you know, six months or whatever, right? This can actually be done a lot more quickly for an SMB, and one of the advantages of an SMB have been a small to medium business owner. It's It's the ability to pivot on the insights more quickly than the large companies can do this. Now, if you think about it, can you imagine the rate and the pace of growth that can happen with an SMB that applies AI smart steps, again, and again, and again, again, you We can't go do all of it in one step as a small company, but you pick one or two of those smart steps, per each time you apply the AI, as the organization has the mindset to make those adjustments to track it and move on, you intentionally have the potential of putting a dent in the rate and the pace of change that big companies are doing, in fact, going even faster, quite frankly. So the takeaway is like, hey, as an SMB owner, I can access AI insights quickly without major time and cost event investments right and feel and experience the results of that. So come join me on Thursday for a web class on how to take advantages of AI as an SMB, go to https://ClickAI.com/BreakTheTrap Looking forward to seeing you there. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at how to get more time for your money. So time, what a crazy concept right? Have you guys ever listened to Myron golden? If not, you should look him up. He's got a great, great segment on this right, which is, what's the relationship of time and money? Right? We've all heard the statement. Time is money, but my run really throw some rocks at that right? And then he convinces Yeah, you know what? It's actually time is more valuable than money. And that's how I feel about it as well, it is more valuable than money. So what is the value of it? What's the value of your time? If you looked at 2500 hours working hours per year, and let's say you made $12,316 per year, then your time is worth $4.93 an hour? like wow, how'd you come up with that? At one point that was the poverty line in the United States? So $4.93 an hour is what your time is worth. So that today that's actually worth or excuse me, that's actually below the poverty line. So the question is, what is your time worth? I have a brother speaking very personally, here I have a brother who has ALS. And it's been disabling him over the last few years for him. And for me, time is the most precious commodity that we have. And it's even become more more more pronounced since this has come about. So when life hands you a situation like that it innately changes the value of time as it has for us, for him especially. But for all of us involved. Now, you know, a second is still a second, right and a minute, still a minute. From that perspective, nothing's changed, right? Time still clicks on from that perspective continuously. But what has changed is the value the intrinsic value that that we associate with that second, or with that minute. Now associated with this is the perception of how fast time goes by all right. And we've all heard of studies like this, there was there was a study of the fluidity of time. And scientists were looking at how to figure out our emotions, affecting our perception of time value, and how fast time goes. And, and, you know, we've all experienced this, right? When we're doing something that we think is fun, or we think it's a high value activity to us. Time tends to go more quickly. That's our perception anyway, right? And then when we're bored, then the you know, the perception is that time seems to drag. And we've all experienced that phenomenon. But the scientists were looking to sort of prove that. So they did some experiments. And in one experiment, the researchers took some train participants, and they took him to tell the difference between pictures and pictures that were shown for a short period of time or a long period of time. And the participants then viewed the pictures, some that were neutral, right? Or some that were positive, or some that were maybe not as exciting, right, so they had these different ranges like somewhere flowers, somewhere delicious desserts, right somewhere, geometric shapes, I guess if you're a mathematician, though, that might be pretty exciting. So for each picture, though, they had to indicate whether the picture had been displayed for short or a long period of time. And just as the researchers had thought that participants perceive the enticing pictures of the desserts, as having been displayed for a shorter amount of time, regardless of the actual duration. Then compared to other geometric shapes that were shown or even some pleasing pictures of flowers. So in general, the peoples that ate the dessert pictures weren't shown As long as some of the others fascinating, right, they also found that that the perceived amount of time for the enticing pictures was also related to when the participants had eaten that day. So they found out that someone had eaten recently, then then those people actually felt like these desert pictures were displayed for a longer period of time. So some of it gets quite situational, right? In terms of what what have we already been satisfied, right. So if I'm being given some information in a business, and if I've not had some either validation or other positive things taking place, then sometimes I respond to that situation differently than if I have had those other sort of mental those, you know, mental validations. Right. And we do that as humans with food and food pictures, right? As well as with other things. I also want to switch and, and say one more thing about this. Turns out that the scientists looked at fear, right. And of course, we've all experienced fear to one degree or another, and of all the human emotions, they felt like that was the most intensely examined one in the study that they should go after. And so what they found was this relationship between fear and and emotions, right, the emotion of fear and time perception. So what they did was they took a bunch of people and think Disney World, right? They took a bunch of people. And what they did was they, they strapped these devices on them, and then they sent them on to a 15 storey drop on an amusing amusement park. Right. I think one's called tower terror. I think that's it. I don't think that's 15 stars, though. But in any event, what they did was they asked the people, okay, now that you've gone through that, how long What was your time perception of going through that fearful event? And when they asked, almost all the individuals over estimated the duration of the fall, right, in other words, oh, we must have fallen for whatever was five seconds, when, in fact, maybe it was two seconds, whatever the difference was just about everybody overestimated that, and from that, they started to drive that, that when we have some threatening stimuli, that it causes some intense, you know, physiological reactions that that distort our internal sense of the passage of time. And this happens also in business as well, I, I found that these emotions applied in one case recently, where we had found some insights, right, that were, you know, we gathered, we shared with a professional team, it turns out that the insights did not cast a positive light on some of the previous business behaviors, right? mentally, I suppose it felt like they were dropping, you know, 15 floors down. And therefore they were struck with some fear elements, right. And in this case, they came back and they said, Well, you know, felt like too much time had been spent on these kinds of insights. And that that was not as productive. Interesting, right. And the team was sent back to get other insights. So one of the things about AI is that it will identify things that may not appear as positive, right? It might say stuff like, hey, if you continue to sell this particular product on a Tuesday, it's actually not as helpful for you, right, you're actually going to get, you know, less revenue, if you continue to do that. Right. So could quit wasting time, right on those kinds of things. Rather, if you do it on the second Friday of each month, etc, etc, then this is going to be a higher probability. And in fact, that's, that's what it's been in the past. So in other cases, I found that when business insights from AI are shared, that focused on building on the good things, then the team typically seemed to be more favorably acceptable of that, and they felt like then it was worth their time, right, fascinating. And so there's a certain amount of vulnerability that comes with this AI stuff, right? That if you're tend to be more vulnerable as a person, meaning you're willing to accept both sides of the story, then you tend to react to a better kind of like, hey, if you ate well, you know, before you did, that, that experiment that those scientists did, then maybe felt like the you know, the the actual dessert pictures weren't as enticing. And similar in a business sense. If you come into, you know, an AI analysis report right on the business, you kind of have to eat well mentally before you show up. Because once you do, then when you come in, you got to be Well, and to say, I can hear some things that maybe we should stop doing or maybe not do as much. And definitely, let's keep leaning into the good things for sure. Interesting correlation, right? In the end, though, we all know time is more valuable than money. In fact, Myron golden I love the way how he points it out. He said, Hey, if I wrote a check for $100,000, who would like that? And then of course, everyone's raising their hands. Yeah, sure, I'd love that. They're like, okay, but what if what if I told you, you know, you're gonna die today, right? So Well, okay, that's not as valuable to you? Well, what if that $100,000 though, allowed you to get the one, the one qR that would take care of you, and it would heal you 100% of the time, suddenly, everyone would take it, and they would use it, you know, the certainly, you know, extend their life, our perception of time and its value seems to have a connection with emotions that are based on our immediate interests and and also in the avoidance of fear. And I think what I found over the years is applying AI in our business requires some vulnerability to receive the insights, which can build on the good things we're doing, but as well as to give us some insights on the negative things that we should stop. So to learn more about how to grow your business with AI, join me on a webinar on Thursday. It's 1:00pm ET, and 10:00am PT. Go to ClickAI.com/BreakTheTrap Thanks for joining. And until next time, give more time for your money using AI. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we look at why I don't have the time and you don't have the time to do one more thing. Hey, everybody, welcome to another episode of ClickAI Radio. All right, who's got the time? You know, as I work across a lot of different companies, that is one of the common themes right from company to company. It's everyone's slammed everyone's busy, who has got the time to do one more thing, you constantly are struggling, each of us are with a I don't have the time or the energy or the resources to get things done. And the stuff that really matters. I would even add that that applies to this whole AI business. Right? Which is okay, fine. I got some AI insights, Holy smokes, don't have the time to actually do this. You know, think back to the last time you implemented something in your business, and it took longer than what you expected. That is a recurring theme across tons of projects, tons of companies. So how on earth? Could it be any different with AI? You know, I've, I've always liked Ted, you know, Ted.com, I just love the format of they figured out sort of this formula of Hey, if you, if you do it in less than a 20 minute conversation, we seem to have time to watch one of those right or to listen to something that someone's prepared. That actually takes more work, doesn't it to actually get it small enough, in fact, wasn't a Mark Twain that said, Hey, I would have written a shorter letter, but that would have taken more time. So it actually takes more time to produce things in a more succinct way. So the question is, what are the things that we do take time for? I've come up with a moniker that I use to help me describe that I call it click time, a click time is seven minutes, right? So I look at things in terms of click time units, am I willing to invest seven minutes of my time into something? And so as we've seen and applied AI to multiple organizations, we'll take a look at how many click times did it require of their time? And how many click times did it require for them to be able to then make a decision? You know, each of us wants to know what needs to be done, but in simple steps, and then you want to be able to get the guidance in a short time, and then move on with it. I was fascinated with this when I started to look at how much time did it take for us as humans to get through a drive through a drive thru restaurant right for fast food. So I started looking for some stats on that. And I found something interesting. This is from 2018 research. There's some a little bit newer, but for whatever reason, I grabbed the 2018. One, our Here we go. So this is the amount of time that we're willing to invest. So right we make the argument, I don't have time, and yet we still spend our time on things. In what chunks do we do that? Well, this is in this is in descending order. In other words, the first one that I read off to you is the one that takes the longest. All right. Now what this means though, is that just because it takes the longest, right? It doesn't really mean that it's the worst drive thru, or could it also be a function of, hey, there's a lot of people that want to go there. It's probably something between the two of those, right? So for example, the one with the longest wait time in 2018. Now for the drive thru was McDonald's. It came in at four minutes and 33 seconds. That was the average. All right, so if you want those chicken nuggets or for me, it's the Egg McMuffin. All right, you're gonna wait four minutes and 33 seconds. That's less than a click time. All right, the next one is chick fil a four minutes and 21 seconds. And then we've got Carl's Jr. Four minutes and 13 seconds. And again, these are average wait times. party's four minutes and 16 seconds. And then we drop we break the four minute barrier just like you know we did that with running the mile, right? We Break the four minute barrier, we've got Arby's, and Taco Bell come in about the same three minutes and 58 seconds. Wendy's comes in at three minutes and 46 seconds, which I have to say the frosty is, I'm really, I'm really, I really liked the frosty, especially when you dip the french fries in it, alright, anyway. And then we got KFC, three minutes and 39 minutes, Dunkin Donuts, three minutes and 21 minutes. And finally, Burger King coming in three minutes and 13 seconds. All right. So the average turns out to be three minutes and 56 seconds in order for us to get whatever that is that we're targeting going through the fast food line. All right, that's less than a click time. So I'm willing to invest time as a human least here in North America, to get through the line, so that I can get one of those items. So maybe I do have time, maybe maybe it's where I'm going to allocate my time. Now, one of the problems of some of the AI providers, as you probably know is that it takes a lot of time. In fact, in my previous podcast, I mentioned the effort, right, we looked at some some research and stats of the effort and the cost that it takes just to stand up the data science team to do this, right. It's massive, right? In fact, there's an SMB owner, it's too long of a time and effort before he can actually get any sort of results out of it. So I was reviewing a recent case study where a small to medium business had used an AI platform. And this is a different platform than then what most of these are, in fact, this platform recently got some accolades. Anyway. So within 30 minutes of loading their data, and seeing the AI analysis, right, so after the models were built, okay, they identified $3 million in additional sales opportunities. Hey, that's like four click times right? Now, I have to admit, that's not a very common amount of time, right in order to get those kind of results so quickly, right. But it's also pretty amazing to see that kind of result as well. I'd say it another way, that's about seven times through the McDonald's drive thru. So that company, they went through for their chicken nuggets seven times. And in that period of time, they're saying, they got some AI insights that led them into $3 million of additional sales opportunities. Wow, that's pretty good return on your chicken nugget. All right. Now, if you were diligent, right, which you are as an SMB owner, and you do the work to run your business. In the course of doing that, let's say that your critical business information was being safely and securely monitored by AI. And that you didn't really have to actually set up all that AI, let's say that the AI was working on your behalf. And then when the AI found something, then you'd be notified. And now you can use it as an augmented intelligence tool, which is really where AI fits in today's world, right? It's take all the good things you already know. And then you're going to take a few click times, right? A few times through that McDonald's drive thru, metaphorically, right? To look at that insight and see, is it relevant? Can I apply it? It may take you, let's say 10 times sir McDonald's drive thru, to be able to put the decision together to make a change in business direction. All right. That's might be not the norm. All right. But in any event, when you look at what the big companies are dealing with, and there's a ton of time that they put into and massive teams, in order just to start getting those insights, you start realizing that as a small to medium business owner, you actually can compete against the large organizations by applying AI in a manner that allows you to allocate small time slices to get the AI guidance to increase your sales growth. Now, to learn more about this, I'm inviting you to join me on a webinar on this Thursday at 1pm. Eastern 10am Pacific, and I'll invite you to come register at ClickAI.com/BreakTheTrap. ClickAI.com/BreakTheTrap I'd love to tell you more about how it is that as a small to medium business owner, you can get the results in a quick time. Thanks for joining and until next time, find some time Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we're gonna take a look at how many data scientists does it take to predict your SMB success. Hey, welcome, everybody to another episode. All right. So we've been looking at this problem around how do I help my own SMB to grow? Right? What does it take for me to have all of the sufficient technology that I need to compete today? We've heard the adage, of course, Hey, you know what, if you're not participating in AI, you're going to get left behind, right that there's a lot of large organizations that, of course, pursuing this, and we'll talk about that here for a moment. What I found is that most AI platforms today, they really require a deep technical bench, right? And a lot of that gets outside of the reach of the small to medium business, right. So one of the things that led me into this world was to say, How can I help and find a way to close that gap for the small to medium business? What does that look like? The drive for Business Insights, leveraging AI, through simple platforms is growing at a cumulative annual growth rate CAGR of 47%. From now up through 2030, the safe some of the researchers out there. Alright, so the need for this and the desire and the appetite for it is clearly growing. But how can you afford it? What does that look like? So I was looking recently, at this came out in February of 2021. There's this report that was looking at some of the top 10 companies, right, that are hiring data scientists. These are large companies, right? And what were they paying for them? Right? Let's say I had the checkbook out. And I want a data science team. What does that look like? Pinterest? At the time anyway, they were paying the most 212k on average annual salary for one, one data scientists. Lift was 154k. Snap was 152k. Man take a breath, right? slack 148k, Uber, 139k, Microsoft 136k. Right. These are average salaries for data scientists, Oracle 132k, Intel 120 3k and Accenture 107k. So the average of that using fancy math 144k. All right, how many data scientists does it take? I don't know, how many does it take to, to, you know, screw in a light bulb. But let's take a look at this. If you're gonna build a data science team, there's 12345678, there's about eight different key roles that are typically done on the stains. Now, a role doesn't always translate into one person. Sometimes it's multiple people. Sometimes one role can go across, you know, a person. Now let me run this by here, there's at least four, four critical needs, right? Let's say as a small to medium business owner, you're going to go build your team, you're going to need someone that's going to take care of analytics, right. And so that's, that's a critical role. So that's a necessary one that's required. Someone's got to know some coding work. So in languages like R and Python, stay with me. A third area is of course, all of the data and database work. So you know, working with SQL and no SQL, those sorts of things. And then and then there's all the algorithms and the models, right. This is regression models, right? And dimensionality and yeah, the list goes on. All right, that's four roles right there. So it let's just take that, let's say that, and that those are unique enough that I'm going to argue that you will need one person for each of those roles, and using the average of 144k. We're up to 576 $576,000 a year. That's it. Even fully loaded, you know, that's, that's, that that starts to become a big number instantly it goes outside of the reach of the SMBs. If you really had and I was looking across some of the large companies doing this, they've typically got around 10. Right? And so you're in the 1.5 to 2.5 million range. And that's still not fully loaded. Nor does it include all of the technology costs, and you know, et cetera, right, all of the hardware and everything you need for that as well. Is it a surprise? No, it's not a surprise, when you say, Hey, you know, the big companies, they are really getting an advantage here by using the resources to bring in this talent, and then to actually widen the gap between what they can do, and what's available to the SMB world. And that's really started just sink deepest in my soul, right? I was like, wait a minute, wait, hey, it's the small companies that have brought some of the coolest innovations, and, and great opportunities to the planet. Oh, and I didn't even mention, there's another role. It's called the chief analytics officer. Not every company has those. But that's yet one more. And of course, I'm sure you could tell that roll is going to be above the 212 k range. I didn't even include that in there. Right. So let's say that, at the very least, as an SMB, if you're really going to do this, if you're going to put together your own data science team, you're at least in the 500k range somewhere right in there. And that's actually on the low end. So like, Alright, so how do you let's, let's just assume that we did that. All right, for whatever reason, I've got the money in the bank, let's say we're just pretending let's say I'm going to go do that. How do you integrate this data science team in your company, there are several operating models to do this. There's a decentralized model, that's where you take an analytics group, right, your data science group, and they're focused on a particular function or business unit, and each business unit, or each function in the company, they have their own data science teams, some large companies have that. Here's another operating model, it's more of the functional model, it's where you have sort of this key function, there's one analytics group, but they will reach out and provide occasional support to other teams. Then there's the centralized model. And this model is where you have one analytics data science team that spans across the entire Corporation. And they of course, then go and reach into the different analytics projects, which are owned by the different business units, and, and different business functions. It turns out, that that model, that centralized one tends to be the one that works best for SMBs, right, where, in reality, it doesn't make sense financially, that, of course, you're going to have multiple data science analytics teams, you'll have one for the entire organization. But you know, like I said, in the larger, larger companies, they'll certainly fun multiples of these. So for our purposes, here, we're going to talk about the centralized model. And there's a flavor of that called the consulting model, which means sometimes that analytics group, or that data science team is actually not in the company. It's external, in its reaching in and providing insight, and, and, and guidance and predictive work into different parts of the organization. So it's critical to know that when you're doing this kind of approach with the consulting model, that that the understanding of the business and the key problems you're trying to solve, that goes back and forth, right between the analytics group and of course, then the the SMB itself. But what does this really mean for an SMB? One of the most important things that it really means is that in order for you to do AI, it means that you're going to have to have one of these big teams. And I did not like that conclusion, I was like, this is this is actually really hurting the SMB teams. So I went hunting for a solution, right? And the solution was, how can we get data science and AI and predictive capabilities into the hands of an SMB? Right? How can we do that in a way that it does not require a data science team within your organization? So there's, there's not only this monetary challenge, or hurdle to it, which is okay, at the very least, let's say it's 500k, for argument's sake. There's another problem though. And that problem is, how do you declare return on investment? Right? Have that all you know of all of that machine learning and data science team investment, and that's of course, where a lot of business executives still need to be convinced. Then you know, you can't can't complain them or Can't can't blame them, right? I mean, you're looking at, you know, at the very low end 500k definitely up into the millions of dollars, what is it that's going to turn around a return on the investment that will make that justifiable. And that right there makes it even more challenging for an SMB to say, I'm gonna gonna jump into this. So, as an SMB, one of the things that I found is that you can skip hiring that team. You do not need the team that the platform's AI platforms have matured, to enable SNB teams to get the benefits of AI without actually requiring bringing in all of that sort of expertise into your own organization. Now, that's that's a huge promise, right? That's a huge change. That capability wasn't really even there a few years ago, right now today, what we found what we've developed, what we've provided is a way to do that. That is literally pennies on the dollar. So what I'm offering up to you is to join me Thursday for a web class on how to take advantage of this 1pm Eastern 10am. Pacific, this coming Thursday. I'd like to share that with you. I will be talking with you soon. Listen to me on my next episode, and I will start letting you know how to participate in that. Thanks for joining and until next time, don't go buy a data science team. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

In this episode, we take a look at what it takes to win the SMB war. Who has a reason to reduce YOUR competitive edge? What tools are THEY using? Okay, everybody, thank you for joining. So a couple episodes ago, I talked about this Harvard Business Review, where they talked about the gap between the large and small companies, how that's growing and why. And if you if you think back to that a lot of it had to do with big companies innovating more and spending more on r&d, as well as having more data. And they were investing in these intangibles. Right, and r&d and technology and artificial intelligence. Well, they also found that you know, the small companies that invested in these intangibles meaning technology, ai data, that they were found to be more resilient, during COVID, as well. So started to think about how can an SMB win, right? What does it mean for an SMB to win in this war, where even the large companies are going after the footprint? that historically has been the small to medium business area meeting, where we expected more innovation and growth and new things to come out? So how can how can an SMB when I started looking at several sources, and one of those is a group called tech aisle, they had done this 2021 SMB challenges, right? What were the top 2021 SMB challenges, thing that was kind of unique about this is they broke it into three areas, one, the top 10, SMB issues. And then the next was to the top 10, mid market issues, and then three, the top 10, upper mid market, that's, you know, 1000 or more employees, okay, so they were doing that based on the size of the company, and the common thread across each of them. That were the issues, right, that the small to medium business owners facing in the areas of business issues, it priorities, and so forth. The common thread across them, was increasing business growth and increasing profitability. So companies from the size of one employee up to 5000 employees, least according to tech dial, they said, Hey, number one issues is business growth and increasing profitability. Now those shifts are the little as you went is the size of the company grew. But nevertheless, they were on the top two across cross those three sizes. All right, so how am I going to win as an SMB? First of all, I got to take a look at increasing business growth, and increasing profitability. All right now, that's according to tech guy, let's look at another one. So Business News daily, they came up with 12, small business trends for 2021. And the net of it was, I think it was number two or three on their list of the 12 was AI and big data are driving personalization. And they go in to talk about, hey, how the large organizations are leveraging these technologies to create a more personalized experience. Alright, so that's the second second one that's confirming and hey, gotta grow and got to leverage the resources that you have that you know that that you have, let's look at the next one. This is from someone called SMB group. And they said, Hey, here's our top 10 2021 tech trends. The first two was, number one SMBs reinvent their businesses for a virtual world. And number two, the business performance gap between the SMB digital accelerators and laggards grow, what they're really saying is, hey, for those SMBs, that are taking advantage of technology and using it as a differentiator, are actually leapfrogging and getting ahead, and the AI and data usage fits squarely within that. So now we've got three different groups that are saying, hey, in terms of either challenges or trends or opportunities for the SMB space, we've got this situation where AI and data play a critical role in building a key differentiator for the business. Let's look at another one. So I want I took a look at the guardian. And they had developed this piece on the state of small business and 2021. Now, the course for them, they talked about the uncertainty that's been created by COVID, that a lot of SMBs. As you know, we all face that. There's also an increase awareness of racial inequalities. And, in fact, in one of their examples, one business committed billions to help advance racial equality. With one of the programs adding I think they said 15,000 loans to small businesses that are in the black and Latin communities. How cool is that? I've also seen AI applied to demographics, to also help serve all communities even better. And I think that's a great way to look at using AI to help address this issue. Now, you know, the movement to cloud is certainly lowered the cost of entry for SMBs, to take advantage of these advanced technologies. But I thought, hey, let me go see if I can find some other evidence, right, if I wanted to take an SMB, and win the war, of all of the big organizations that are now leveraging some of the best techniques to actually keep their footprint and grow and make it more difficult for the SMB space, what else others have to say? So here's another one. I tracked down guidance, financial guidance, financials an interesting one, they had this really cool pictorial where they focused on the small business trends in 2021. They I think it was like 2400 organizations, SMB companies that they surveyed, they came back and said, 78%, expect to survive, and 19 were unsure. So those are interesting, interesting stats. But when it came down to Hey, what were the things that cause a lot of problems? It was loss of revenue, or reduced budget or temporary closure? Those are the most five, you know, most common COVID impacts. But at the end of the day, it came back to, are we taking our resources and using them wisely? To get the insights to now compete more effectively? That was their takeaway? All right. Can you handle one more? Alright, let's let's just talk about one more, a group called Constant Contact, they looked at small business lessons for 2021. And just to summarize, in the top two, they summarize that the leverage the small business has to use its online marketing and tools was number one, and number two, collect and take action on your customer data. Of course, that comes right back to using AI to segment your mark your market and then go after those vital areas to create success. So that was what that five maybe that I looked across. I thought, Okay, that's enough to look at. And then I bumped into this one from Ernst and Young. This one's interesting, because they were talking about, hey, how do we bridge the gap between big and small businesses? Now they wrote this article from coming from the other direction, right? So the first five that I mentioned, were coming from the direction of Hey, what what what are the challenges I you know, that I face as an as an SMB owner, right? They came from that perspective, the E y one, Ernst and Young one came from the other direction, which is, hey, we're in working with the big companies, how can we be more like the small businesses, right? So they're actually peering the other direction? And what's interesting with this viewpoint, is of course, the ask the question, how do we help the companies, you know, that are big? How do we get them to start acting small? Now, what is it about a small company that a big company would like, and of course, one of the things that you'll you'll notice right away is agility, right, they aspire to have the agility of an SMB. And in fact, according to a recent Innosight survey, they found that 50% of today's s&p 500 companies will no longer exist in 10 years. Holy smokes, that's amazing. So as a result, the larger businesses, they're creating their own internal innovation programs. And what they're doing is they've got this nice blend, this blend of large companies scale, mixed with a small company agility. And what that does is that actually makes it a little more challenging as an SMB owner to compete with that because well, you don't have the skill part, right, we don't have the skill part. So they're trying to be more like the small companies. They're trying to think like that. But you know, sometimes they get up, they get caught up, and you know, all of the minutia that takes for them to turn those big organizations around. So here's what I suggest. It now's the time to think about the key problems, then issues face your SMB and apply AI to efficiently innovate. So they're mimicking your best practices, and they're using AI to propel them. So the question is, then why wouldn't you continue your best practices? Stop the bad ones, and then use AI to propel yourself. So the second thing in this Ernst and Young articles interesting is that they have developed a practice where they blend large corporate teams with small entrepreneurial teams. Again, they're looking to blend and bring together the big, you know, the small company agility with the big company scale. So when I started this particular podcast episode, I started by asking, Hey, way, how can SMBs win the war? And of course, you might ask yourself, what war? Well, the war is this, the big companies are leveraging the best practices of the small companies with the intent to maintain and grow their footprint, not as they're borrowing the best from the things that you've been doing? Well, so what do you need to do as an SMB owner? Well, I suggest apply some of the big company best practices, which includes leveraging your resources that you have by applying AI. And then here's the here's the trick. Make 10 Smart steps to improve your business before they can make one smart step. And by applying AI over and over again, actually builds your ability to move much more quickly, and get ahead of now, even the big guys are in the competition. Hi, everybody. Thanks for joining until next time, as was said in a Batman movie, I think, be mindful of your surroundings. In other words, get moving on your AI for your business so that today you can win the SMB war. Thanks for joining. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

In this episode, we take a look at the secrets of the growth mindset. If YOU could adopt the techniques for changing your growth culture, would you do that?   Thank you for joining me here today. So recently, I was taking a look at a site that was talking about some of the pain points around SMBs. Right? What are some of the things that make it difficult for small to medium businesses? I've been so busy doing some AI work, and projects that I've not had a chance to podcast recently. So I've missed doing this. Alright, so let's let's take a look at this. I was interested in focusing on what are the things that hold back a small to medium business. And I look through what lots of organizations are saying certainly looked at what my own experience has been as well. I found one in particular, that was interesting. It's called WalkerSands.com interesting site, they focus on accelerating the growth of b2b brands. Anyway, in their on their blog, there was a gentleman who had written down a list of 100 pain points, and it caught my eye and I, I started to look through those and then I copied them down. And then I started to categorize them. And I noticed that well, there were some that focused on business process pains. There was cash flow pains and competitive pressure pains, eco system paints, which are a How do I deal with, you know, those that I'm partnering with? Of course, there are pains around expenses, of course, financial pains, government pains, legal pains, marketing pains, and then there are some personal pains, right things that happens in our lives, then there are product pains, and finally, workplace pains. So I went through on each of the 100. And I started to categorize them as well, right, this one's business process, this one's workplace and culture, this one's product and marketing and so forth. And then I sat back and I counted them up. And what I came back with, first surprise me, and then I realized it was confirming because it reflected my experience with working with lots of companies. So here's here's the results. Now this is an order of repetition. In other words, this is the ones that had the most number of pain points. Alright, so this is out of 100. So the top one was 30% of the pain points on his list, dealt with workplace slash culture slash mindset kind of issues. Right? All right, that was the one that was the most repetitive. Now I need to say that the weighting of all of these that I'm going to go through certainly not the same, right. So at any given point, you know, one pain point could become more detrimental than 10 others combined. So I didn't look at it from that perspective. In this particular grouping, I just said, hey, how many of these in this category are there? Alright, so here's the list. It's 30%. We're workplace culture mindset. And then the next most repetitive was marketing. So 15% were marketing, and then 12% were financial. And then it drops down into single digits area. So 5% for competitive pressure, another 5% were personal, another 5% of them, were business processes, and then down to ecosystem and product, both of those are 4%. And then going down a cash flow is 3%. So there's only three out of the 100 that talked about cash flow. I thought that was a little small, you know, my experience with small, you know, doing small business stuff is cash flow is obviously extremely critical, and an important one. So that's why I pointed out a you know, in terms of the numbers of these, it's not that, you know, they're all weighted the same. All right. So it's an interesting look, though, as I've looked around lots of companies and worked with tons of companies over the last 35 years, I've noticed two common themes regardless of industry. The one is culture. workplace mindset challenges. You could be in the medical industry, healthcare, you could be in financial services or banking, you could be an aerospace or defense, wherever it's, this is a common thing, culture or workplace or mindset challenges, just our are always the common thread. And then number two, which is real closely related, is communication challenges, right? That's always a big a big issue. And sometimes that feeds into culture itself. But if I, you know, those are the common things that I see. So when I, when I looked at this guy's list, and I see that 30 of his 100 dealt with workplace culture mindset, I wasn't surprised that, you know, he, at least from his perspective, that that was a common set of problems. Last month, or maybe it's six weeks ago, when I did my previous podcast, I shared some findings from a Harvard Business Review report, which talked about the small company trap, and how to break that. Now, if you recall, there are two and I invite you to go back and listen to it. If you haven't heard that one. There are two key areas in investing. Well, let me back up, there are two key areas to break that trap. The first one is investing and and adopting technology. So in that bucket, I put things like you got to have control your data, you have to have, you know, good systems to help you. Obviously, I think AI is a key part of that. And of course, I make a pitch and appoint a case for that. Right. So the Harvard business folks said, that was one of the common themes that they saw of small companies, and big companies that both survived and flourished through through COVID, as well as in other times as well. Okay, that was the first one. second one was marketing slash brand equity. All right. So when you look at this other guy's list that I was talking about earlier, on pain points, marketing was high on the list. Interestingly, there's a little bit on on technology that was in there in terms of pain points. Here's the thing I wanted to get to those. When you look at the list above, you know, marketing had the highest repetitive points, other than the culture piece, right. And it was central to Harvard's finding, my experience has been that when AI is properly applied, it helps close the gaps between the marketing needs and the pain points we have there. And where we need to go to right in order to accomplish the kinds of outcomes, the Harvard that Harvard was talking about in that report. Now, what I did was I pulled together a list here of some of the marketing pain points that this particular gentleman included. Right. And and I'll just, I'll just mention some of them. Number Number one here, that he'd mentioned was, we are not able to get new customers. All right. The other is nobody knows who we are. Or here's another pain point. I'm not getting any foot traffic, or or online traffic. The other is, let's see. This is a commodity business. How do we differentiate ourselves right? on something other than price, right? Hey, Mike, my take on that is you need a good offer. I know an offer guy. Here's another one. I have no clue why we just lost that big sale. All right. Here's another sales are down 30% from last year, why? and so forth, right? Now, those are all interesting marketing problems. And I've had an opportunity recently to be doing some things with an organization where we've been applying AI, looking at these kinds of questions and discovering, hey, what was the behavior that drove growth or that caused, you know, customers not to repeat their business right over the last several years, an AI brings out some really interesting insights for these kinds of organizations. So I'd say hey, you know what, the Harvard folks are, right? You got to invest in these kinds of technologies so that you can compete, because the table stakes have changed in terms of what it means to compete effectively with our marketing and AI is a piece of that. So I reiterate AI properly applied, does help us close the gaps between where we are today and where we need to go. But I want to come back to the previous point about workplace culture and mindset, right, which is by far the most common recurring pain point across organizations. You can even have the best marketing the best technology but as Peter Drucker used to say, culture eats strategy for breakfast. Remember that culture eats strategy for breakfast. So you might have a strategy of a I'm gonna do AI, and I'm gonna apply it into my marketing. But if I don't have a culture and a mindset that will adopt and accept and put into place those particular changes that are coming out of that, that's a waste of time, right? So how do you achieve a growth mindset in your business, it's not enough to do the marketing, that's obviously critical. It's not enough to do the AI. Although I would say that it's critical as well. It requires these two things. Number one, you do need to apply the marketing best practices and use an AI to help you do that is certainly key. But number two, most important, you got to have a growth mindset, it's a change in thought, on adopting and adapting the way we work, right, we have to address the culture part. So growing our businesses by just technological insights alone, in my experience, that's naive to consider. Now while we may have short term gains, doing that, it doesn't possess the power to sustain at least my experience. So what means sustainable growth? While it typically requires this mindset, that's continuously evolving it continuously vulnerable, right, continually speed being asking for what do I need to do or improve next, it's where I want me to take on new insights, and then to accept the need to change and grow. But you got to do it in steps, right? That's, that's the key. If I get too much insight, or too big or things to change, then it's overwhelming. And I might as well just stop because the human brain sort of shuts down, right, and the culture won't accept it. So it needs to be given in doses into a series of steps become smart steps. Now, I've seen large companies apply culture change techniques. But if they're done in mass, like I said, it becomes overwhelming and soon against syndicates drop. So from my experience, the growth mindset secret is these two things, number one, got to apply AI insights to your marketing, your sells your internal operations, take advantage of those platforms that are available today. And I can tell you about the platform that we provide, we build on the big guys to do the stuff. Alright, that's the first one. Number two, bundle those insights that you get with a culture hack to make minor, you know, but yet still measurable adjustments to the way you work in order to adopt those AI insights. If you do number one, without doing number two, my experience has been the organization doesn't sustain. If you don't have the mindset for growth, than this stuff really isn't for you. Now, you might go one or two times through it. Now we like I said, we call this a smart step. Smart step has those two components in it? So the growth mindset, what that really is, is it's a succession of multiple smart steps. When you're doing smart steps, think of those two components: 1) applying AI insights to your marketing, your sales, your internal operations, and, 2) then to applying a hacker culture hack to your organization that allows you then to change the way you work to adopt those changes that will come to you from the A AI insights and then grow your business right. Doing this puts you on a path of sustainable incremental growth. Now, as you know, there are so many pain points that an SMB experiences, and I'll be the first to say this, what I'm talking about does not solve all of them. But I have found what it does do is it focuses the people on digestible attainable steps, which shifts the culture and the profits one step at a time. All right, everybody. Thanks for joining and until next time, get into the growth mindset. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

What are the two secrets to helping your small-to-medium business break the Small Size Trap? Would you apply those secrets if you knew them? Welcome to another episode of ClickAI Radio. In this episode, we look at the two secrets to investing in your small to medium business to break the small size trap.   Hi Everybody, thank you for joining. Okay, so the small size trap. What is this? Well, it's when a small company has a challenge of getting or growing larger, right? It's where you have a difficult time breaking the barrier of getting from a small to a medium and ultimately to a large size company. Here's a couple of statistics this came out of a Harvard Business Review. What they found was that until the year 2,020%, of small companies became medium or large each year, right. So every year. Now, the downside is that by 2017, this was cut in half. Wow. Now let's look at that same period until 2,080% of large companies retain their size. All right now by the year 2017. Though, this said not cut in half, but actually had increased to 89%, almost 90%. So in the same period of time from 27 from 20. Yeah, let me try that, again, from 2000 to 2017, small companies 20%, drop down to 10% that actually became medium or large each year, whereas the large companies actually increase right, that that's incredible. That's the small size trap. So Harvard, you know, in this research article, they said, What was it? What was the difference? What allows a company to break the small size trap? So for the small companies that became larger, what were the secrets, and there were three of them that they pointed out. One was something called investment in intangibles. That's number one, number two, a higher asset base. So they're bringing in more revenue Make sense? And number three, they were a younger company. All right. So that's that was the secret sauce for small companies. But what about for the big companies? What was it that allowed them to either retain their size or to grow? And what they found is it was investment in intangibles. So the common thread here is, there's investment in intangibles for small companies to break the small size trap. And for the large companies to become larger, it's investment in tangible. So the question is, what the heck is investment in intangibles? I love this part because it gets to, I think, excuse me, it gets to the core of what it really takes for a company of any size to grow. The number one thing was an investment in research and development, data technology, artificial intelligence, I was number one. Number two was brand equity and marketing. Right? So market awareness. So the ability to create that. So those are the two secrets around investment in intangible. So as a small to medium business owner, you look at yourself and say, Alright, where do I put my scarce resources? Well, according to the study, you know, they found that you did invest it in technology, right? you invested in brand equity and marketing. So those are the two key areas. The interesting thing about this, is that with AI with the big companies, right, it's giving them this leapfrog capability, right? They're jumping ahead, getting bigger and bigger. And it's allowing them to not only just incrementally grow and retain their size, but to actually leapfrog. So one of the most important things to do for a small to medium business owner is to take advantage of the AI that is coming available to you. And there's multiple platforms for that to do that on. Google's got some cool stuff. Clearly, Amazon has some things and certainly as your does as well, there's the cloud based approaches. One that I found really interesting is while while the big cloud providers have an awesome set of platforms, they're still required. Significant team and skill sets around that. There's another group that I've seen that I'm really impressed with. It's called abl AI, B, LA. They're built for how you bring AI into the business right there, they create these views that are incredibly simple for the business owner as well. So one of the things that we find interesting with click AI is leveraging these skill sets so that the small to medium business owner actually doesn't have to worry about the AI portions of it. Rather, he can focus on the questions around, what is it that's affecting my business? What are my pain points? What are the things that I'm trying to move away from? And what are the targets I'm trying to go to? And in fact, I think Russell Brunson put a book out called Traffic Secrets. And I think I think it's page 19. Actually, he's got this cool view there that says, write out your pain points. And write out what your targets are things you want to move towards. When you can answer those questions. That's actually a great entree into a business view around AI right? It helps you to say alright, what problems do I want to go see if I can solve with AI? That's great, a great place to get started. So the trick here as a small to medium business owner, don't shy away from investing in the marketing and the brand equity and don't shy away from investing in AI to leapfrog your competition. Hey, that's it for now. Thanks again for joining and until next time, invest in your INtangibles. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

Is AI relevant to my marketing funnels? If so, how do I apply AI to my funnels? Can I move my customer away from pain toward pleasure using AI? Hey, thank you for joining another episode of click AI radio. All right. So I was looking at Russell Brunson his book called Traffic Secrets. I don't know if you've looked at that. It's got this he's got this great these great insights on how to drive traffic to your business interior to your sites and such. Alright, I wanted to call out something in there. So he's got this activity, it's on page 19. If you have the book, open it up to page 19. If not get the book. He gives it away for free. So Alright, so page 19. He's got this, this little activity on there. I'm just flipping through it right here as we go. All right. And and what he's focusing on here is moving customers away from pain and towards pleasure. And he's got this, I'm gonna read something. I'm gonna quote him right here. He says, He says that the next step to entering the conversation inside your customer's mind is to understand which direction they're moving, right and quote, so in essence, he goes on to say, hey, they're moving away from pain towards pleasure. So he's got this framework, he draws this line. And it's he's got this little person moving away from pain, towards pleasure. And he makes the point that as humans, we're either walking away or running away from pain, or we're moving towards pleasure. And it's that way for our customers. And it's important for us to get into the heads of our customers so that we know which direction they're moving. Well, when I was a kid, my family spent many summer vacations at Lake Powell. It's this lake at the southern Utah, Northern Arizona area. Anyway, we did a lot of activities like you'd imagine right swimming, waterskiing, cliff diving, so forth. For the most part, we were moving towards pleasure, right, I'd like people to use Russell's framework. But one time, though, there is this massive storm that hit the lake. And we my parents had this really small ski boat is like an 18 footer. We were about an hour away from our camp on the lake. So we were kind of a ways away. And so the big storm comes in, it's not like you can get out and get on a road because the big cliffs there, there's just no way out. So you're sort of stuck down inside these big, high Cliff areas. And the waves got really massive, so big that they're coming over the front of the boat. So it started to get a little scary for us, right. So for about an hour of watch, my dad tried to navigate through these massive waves trying to get us back to our little Cove and get to some safety. So I would say in that case, we were moving away from pain. Now the interesting thing here is that in both scenarios, it's the same boat. It's the same Lake, it's the same location. Sometimes we route we were moving, of course towards pleasure. And other times we're moving away from pain, right? So sometimes in business, we are moving toward pleasure, or we're moving away from pain. It's the same quote unquote boat, metaphorically, right with our businesses. What changes as we know around us is the context, right? So like, like in this lake Powell experience. When the weather was great, we were focused on moving toward pleasure, right? We wanted more skiing, more snorkeling, more eating food, and so on. But when the weather course turned bad on around us, then of course, our focus shifted to moving away from pain. Now, what I'd like you to do is draw two lines. All right, take take out a piece of paper, draw two lines, like the ones on page 19, and Russell's Traffic Secrets book. So you're gonna have two lines. And the first one is going to represent the spectrum of moving away from pain towards pleasure for our customers, right? So that first line represents our customers, we want to move them away from their pain towards their pleasure. And as you know, this is generally where we start our focus as business owners And it's the right thing to do, right, we want to serve our customers. And so we want to help them move away from their pain and towards pleasure. And then if you look at the second line, draw that. Alright, that's going to represent the spectrum of moving away from pain, towards pleasure for ourselves, meaning the business owners. Now, there are things in our business that are painful, of course, and we seek to course move away from those, and certainly there are business pleasures that we move toward as well. Well, my experience has been that the more closely we assert, we understand our customers and we seek to serve them, the closer these two lines come together, right. And in fact, at times, they may, they may even lay on top of each other. So I'll give an example. The more we see business impact on our customers, in other words, the services and products that we're providing, if we see that that's impacting our customers, in other words, if we see that it's reducing their pain, and increasing their pleasure, in general, that translates into a greater reduction of pain and a greater increase of pleasure in our own businesses, right. So these lines can in fact, get very close to each other. So the question is, what does this have to do with funnels and AI? Well, the answer is everything right funnels track the footprints of customer behavior, right, as we're looking to bring them into our products and services. Ai analyzes those footprints, meaning the customer behavior. Now, one of our pain points as a business owner, is to misuse and Miss apply our resources in the customer acquisition process. This manifests itself in lost sales or sales reductions, you know, when we perform in a not so good manner, this creates a pain situation, right. And of course, as business owner, we want to move away from that AI analyzes customer behavior in our funnels, right. And in doing that it uncovers the pain points, that ultimately means that you may not be serving your customers most optimally. Now, this is great insight for business owner to have this clarity, and to be able to pivot or adjust. You could look at what's happening with that funnel and the results of it and to discover, oh, things don't go well. In these situations. We want to move away from that because that creates pain for us as business owners. And in a similar manner. Ai analysis of customer behavior and funnels uncovers pleasure points that ultimately mean that you are serving your customer very well. And in this case, ai provides guidance on which of those activities you should do more of now by applying AI to our funnels, it means we can emphasize the productive behaviors, and de emphasize the unproductive behaviors. And by doing this as a business owner, you're able to more effectively deliver more of your products and services. In other words, helping your customers to move away from their pain and move towards their pleasure. So in this manner, your direction moving from pain to pleasure is directly connected to your customers movement away from pain towards pleasure. So these lines do get very close. Applying AI on your funnels unlocks the doors to continuous insight to help your customers on their journey which is course what we're all about as small to medium business owners. Now eating frybread late at night, while watching the moon rise over the cliffs at Lake Powell. That was always a treat. And I would call that moving towards pleasure. And I actually think we could all use a bit more of that. Alright everybody. Thanks again for joining. Until next time, move your business and your customers away from pain towards pleasure by applying AI to your funnels. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook, visit ClickAIRadio.com now.

We look at the question, How can I use AI if I don't have enough data? How much data do I need to get value from AI?   Hi everybody welcome to another episode of ClickAI Radio. This is Grant. All right. So this is a very common problem, especially in the AI machine learning world, right? So you got a business, you want to be able to apply AI to your business, but you look at your data and you're like, I don't have enough data? Or do I? That's always a big question around AI, how much data does one actually need? We were working with a newly formed company, they were providing coaching to their clients. And they wanted to have insights on their client base so that well, they could improve their messaging, as well as their client acquisition. However, they only had a handful of clients. So they didn't really have a sufficient amount of data to leverage AI. And so we went about the business of building out a framework. So I'll introduce that framework to you later, as I walk through this. First and foremost, what is the lack of data? Well, one of the biggest problems around AI is those darn computers need a ton of information, to be able to start extracting the patterns and the predictions from it. So the very nature of AI requires this now, what would be some ways to get around this, one of the groups out of kt nuggets stated, hey, if you want to get rid of or overcome this problem of low data, well, you can always reduce the number of classifiers. Holy smokes, what does that mean? That is such a nerdy talk. What that really means is when you're looking at your business, and you're breaking down the business problem, you want to reduce the amount of category. So you might have sales, for example. And you might say, well, I want to look at sales by geography, or I want to look at sales by salesperson, each of those would represent a category. So the fewer categories you have, typically the less amount of data that you need. So you could actually reduce your categories significantly, and have a lower or smaller amount of data and you can get started sooner rather than later. Now as time goes by, and as your data amount crows or the volume grows, you can certainly introduce more categories. Each category itself needs a sufficient amount of information or data in order for the AI to be effective at it. So what would be some of those numbers? Well, here's here's some examples. If you're going to do some classical machine learning AI work, typically you'll need maybe 1000 rows of data. Now I'm not talking about AI that's used for images, or voice or text or things like that, what I'm really talking about is AI specifically for business information. So might be sales information, or might be information coming from manufacturing or payments, or whatever that might be. So at the very least, for your classical machine learning where you're looking to do some linear dependencies, you want to understand the input and the output relationships, generally 1000 rows of information of information would be would be the lowest amount that you would go with. Now if you're gonna go for some of these other kinds of AI, right, where you're looking to do advanced neural networks, then you're going to do well over 1000 bits or pieces of information, right? Whether that's images or whatever, you're going to get 10,000 50,000 100,000 of those. But for the purposes of this conversation, what I'm going to focus on is not the image style kind of AI but rather the AI to help someone with, say a sales problem, or a refund problem or something like that, that's going to pull out of their business transactions, the information that they're getting from that. Now, the problem with a small amount of data is that it creates a problem called overfitting and overfitting creates this situation where it starts to create predictions that are too tightly coupled to a very small amount of information such that the the, the algorithms are too tightly connected to just a small set of data. What it means is you're getting predictions and analysis that are not generalized enough, right? Hence, they're over fitted. All right. So what can one do to do to address it? So the first thing is, is reduce the number of categories that I've mentioned. There's another technique also. And it's called data generation or data augmentation. And this is a technique that's being used by some organizations where let's say that you had 1000 rows of information, then you apply some techniques where you generate or augment or, or artificially, if you will create additional data based off of the set of data you already have. And this allows organizations to get started sooner rather than later as well. In fact, the open AI group, they use something or they produce something called GPT, two or GPT. Three, that's a technique for generating human language, data augmentation. But I'm not talking about that I'm talking about business information, as it relates, in this case, to your sales or to refunds or to your payments and your payment processing. Well, this data augmentation technique is certainly something that you can do. But one of the things that I found most useful without getting overly complex for the business, because going through these techniques that I've that I've mentioned briefly, right, such as data augmentation, that's fairly invasive set of activities to do. A lot of businesses, especially small and medium businesses don't want to nor should get deeply entrenched in that it takes up a lot of time. So there's another approach that we've developed that I call the continuous feed framework. And the continuous feed framework, its purpose is to line up your business for AI, right? It's Think of it like a drip system in your garden, right, where you're continually dripping little bits of water to a plant or something like that. The approach is you connect your business systems up to the AI engines, and like this drip system in your garden, you slowly start dripping data into the AI engines, the AI engines, then over time, continue to run and rerun on the data that's available. But what's it look what it's looking for, is for overfitting, it's looking to determine, Do I have enough information to in fact, have a generalized set of patterns that are now predictable and reliable. So when the model begins to develop this accuracy, and these predictability characteristics, then AI analysis and prediction activities can begin to be harvested, which produces insights for your business. And I know I sound like Cloudy with a Chance of Meatballs right there. You know, it's Science, Science, Science, Science, right? All the guy hears is bigger, right? Bigger is better. Let me say it another way. If you have a small amount of data in your business, then you can easily and non invasively get started with AI by connecting your business systems up to these AI engines. And then like, like I said, like this drip system slowly build up that information. And when the AI starts to exhibit non overfitting, and highly accurate models, that's when you know, alright, I have enough that I can start taking the insights out of this and and using it to impact my business, which actually, ironically, has the effect of producing more data for you in the future. So it starts to become this nice, iterative and symbiotic relationship. All right, everybody, thank you for joining and until next time, get a continuous feed framework. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

What is the one simple hack to increase my revenue? If you could apply a 3-step framework to overcome the main reason for business failure, would you? Hey, welcome, everybody. This is Grant Larsen. Thank you for joining. So okay, been looking at this problem around revenue, right. And as a small to medium business owner, this is constantly a challenge. In fact, I was reading recently, some common reasons why small to medium businesses fail. In fact, this was something I saw on investopedia. And of course, was looking at other places as well, I came back with these four reasons, right? These are the most common reasons, one, financing challenges or hurdles to inadequate management, three, ineffective business planning and four marketing mishaps. So let's talk about the first one financing challenges or hurdles. Right. So some of the challenges, of course, running out of money. That's always a big challenge. In fact, the first startup company I got involved with, that was clearly the problem. You know, not only were we working, not smart, but we of course, didn't even think through what sort of runway we had on the finances. And so that clearly means that on a day to day basis, we have to be tracking, what's the revenue stream looking like, and how are we producing this. In fact, one of the things about this first one, right, the financing hurdles, it's what's strikes me is that if we address it, it actually gives us more runway to then address those other three, right management challenges and effective planning or, or even bad marketing decisions that we're making. So one of the things that I noticed here from the Small Business Administration, some results that they had, they said, 20% of small businesses fail in the first year 50%, go belly up. After five years, and for only 33% make it to 10 years, those are amazing odds. So this first, this first challenge here around financing hurdles, really stands out as the critical one to address. So I started to look at it. So what are some of the issues in here, and one of those is around missing the mark on how we price our products. And often we get stuck down into these pricing wars. That gets into how do we how do we define products? What's a funding model for those and so forth? Well, what led me to this next piece I'm going to go over is after working with some small to medium businesses, I started to hear this common theme about what do my business numbers mean? What do they What do they translate into? Where should I be investing my time and energy as well as my scarce resource? So I noticed over time, that simple framework started to appear out of this, right. And the thing about frameworks is, they help us to focus on the things that are most important. And then help us to de emphasize the things that aren't important. And so this framework has appeared over some period of time working with organizations doing this. So I wanted to introduce this framework. Now a framework generally has a series of steps. And so in this framework, there's three steps. I call this framework, the PIE framework, spelled p, e, maybe there's another way to spell pi. That's the way I know how to spell pie, the pie framework P-I-E. Alright. And again, this came out of addressing the question, How do I solve these funding hurdle challenges, right? And then in order to do that, what do I have available to me as a small to medium business owner that I can apply into this framework to help me resolve it? So the PIE framework came out of this out of these groups, right, that I was working with, and here's what P means ready? P means problem and information preparation, like what the heck is that? Right? So for problem and information preparation, one common thing that I've noticed is that some organizations will not literally write down what are the serious problems We're facing How would I prioritize those? Right? What, what are the most important problems we're facing? And then what's the information that I have available to me to help me serve as insights? Tell me solve that problem. that's step number one of the framework sounds simple, right? Sometimes life is just the simple things. The second step is the I pee. Okay, so the ideals with intelligence augmentation. Now, this is a course, where artificial intelligence comes in. Now, I intentionally use the term intelligence augmentation, because I personally think that's where AI is today. I think it's at the point where we apply it intelligently. And so the insights that come to us that have that have examined our our information and the problems that we're looking to solve, that serves as input then to the great intelligence you already have, and so P problem and information prep, step two, intelligence augmentation, right, and then step three, is executing on guidance. So that's the E, execute on guidance. Now you think, why would I have that in there, I find that interesting that over a period of time, a lot of organizations won't execute on the guidance. So you're been given this guidance from AI, and they won't do the execution on it. Here's the key about AI. First of all, the reason why we use the term intelligence augmentation is we're not expecting that we apply it blindly. So we do want to obviously, apply it intelligently, within the context of business constraints that we have that that is critical. On the other hand, though, while that's being said, I noticed that some organizations will do nothing, right, they won't make any adjustments. And hence, I put E as the third step to the framework. So if you want to have a simple hack for improving your revenue, you apply the PIE framework PIE, identify your problems and the information preparation that's needed to address those, run those in through artificial intelligence to then give insights that then augment your own intelligence and then make a plan to execute. On the guidance, three simple steps. I have found that when we've applied these in organizations, and they've executed on it, they'll discover things that they didn't even know that they didn't know. And that permits them to grow with the right kinds of customers without taking on new resources, which is a big challenge, obviously, for small to medium business owners. So there were four common reasons why SMBs failed, right? That it started with one financing hurdles to inadequate management, three, ineffective business planning and four marketing mishaps. So today, I've talked about the PIE framework to address this first one, right, which is around financing hurdles. The other three items of course need to be addressed. But with continued funding through AI inspired guidance, it's been my experience that it gives a runway right gives us a much longer runway to address those remaining three items, but we'll discuss those at another time. For now let's focus on overcoming the financing hurdles through the pie framework. Hey, thanks for joining and until next time, get some PIE. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.  

One simple hack to increase your revenue. In this episode we discuss how to increase your revenue through your business numbers. Grant Hi everybody this is Grant Welcome to another episode of ClickAI Radio. In this episode, I have the opportunity to discuss with the infamous Blake nubar on how to get a grip on your business numbers. Everybody, welcome to another episode of click AI radio. This is Grant Larsen, I cannot tell you how excited I am today to have a visitor here with me someone that I've been dying to interview for a long time. I have really admired this man and what he's done. Blake nubar. Blake I appreciate you having me here, man. It's a pleasure. I'm ready to have some fun and and thanks for having me on the podcast. Grant Thanks for taking the time to do this. You know, I've chased you for some time, right? Just keep trying to track you down wherever you are on the planet, right? Trying to get a moment of your time. But thanks so much for doing that. In fact, I was remembering the first time that I met you. I happen to actually be sitting right next to you, right. And it was at a funnel hacking live. And I think it might have been the first one that I ever went to. And I'm sitting next to you. And we're just chatting for a minute. And then you were so humble all sudden, they're like, Blake nubar, please come up to the front. They call yell, I'm like what the heck, you had won this award for everything you've done to the business. How cool is that? Blake Yeah, it was wild, I would award the growth on that thing was insane. Actually, that was on the product. And that was really cool. And yeah, I remember sitting next to you specifically. And I was like, Wait, are you Steve's Dad? And you're like, yeah, I'm Steve's dad. I'm like, man, he talks about you all the time. Like in such an awesome way. It's fine. Nice to meet you. So, ya know, collecting the first is my first time walking across stage getting the the two comma club award. And that was done with an online business that it was called the b2b formula. I was working with a guy named Brian page on that. We kind of started that thing from ground zero type thing. Brian had a he had a course out really. But you know, wasn't making sales didn't know what a funnel was. All that good stuff. And we kind of like teamed up, you know, and we found each other through this thing. We started building this thing out. And you know, next thing you know, by implementing funnels and getting the right traffic sources in place. We did a million dollars in 43 days with that offer. And we were there at that funnel hacking live collecting that award that was actually here in Orlando, Florida, I believe, which is funny enough, because it's back here again, this year in 2021. Which should be exciting. So yeah, that that was a it was an exhilarating experience. A lot of crazy stuff happened. But on all Yeah, crazy to like help grow a business like that. Grant I think about how you got started, I've heard a little bit of your origin story. But could you just take a moment to talk about how did you get started into this? Yeah, so Blake Um, that my whole online journey and my funnel journey is different. My funnel journey is probably more applicable for this. So I was working in the nine to five, I was working in a fitness company. And we were working on building out this certification program for people that want to become personal trainers. If you want to become a personal trainer, you had to pass this exam. It's just like a standardized thing that you have to do. I don't know if it varies from state to state, maybe it does, maybe it doesn't. But we're building out the certification. And we want to like really go all in on this thing. So we spent we like really transformed how a training program should be. And we like we finished this thing. Like, it was crazy worth like nine months on this thing. It was a ton of time, but it was it was awesome. I had like a mobile app. It was this book. It was an online program and like all 360 views and amazing product. And we go and I hand it over to the marketing department and I'm like, Hey guys, like we're ready to rock and roll and start selling this thing and I almost like hand over the torch and they just couldn't sell it. Like nothing was moving and I just remember getting frustrated cuz I'm like, hey, like, Why is this not working? Like I thought that's your job. You're supposed to like market this stuff now and they're like driving traffic to like a website just like nothing was working. And I remember go I went home one night. And I was like frustrated Of course and I'm sitting there it's like three in the morning. I'm staring at my ceiling fan. And on my laptop on my on my bed. So I just remember pulling it open and looking through it. And I see this ad Come up. And I just remember the ad it just said weird marketing experiments to increase traffic conversions and sales online. I'll never forget the saint of it. And it's this crazy guy on it. His name is Russell Brunson. I had no idea who he was. And he was just like, full of energy. So I clicked on this ad. And next thing I know, I went in in a sales funnel. I didn't know I was in a phone, I thought I was on a website. But lo and behold, I was in a sales funnel. And it's basically Russell just being like, you want to grow a business online, you want to start a business, you need a funnel, you need a funnel, and he's like, going through all these different types of funnels. And I remember I was like, so intrigued by it. I was like, a 90 minute presentation, and I was so captivated. I remember, I watched it. Again, I watched it twice. So I watched this thing twice. It's like six in the morning, I go straight into the office, I started sketching out a funnel on the board, and the marketing department like, what are you doing, man? I'm like, I know how we're gonna sell this. We're gonna use a sales funnel, like, what's the sales funnel? And I'm like, I don't really know yet. But this guy Russell talks about it. And I think I think this is how we can sell this thing. So I start sketching this thing out. I call up one of our celebrity trainers, who is like the poster child of this brand new certification. I'm like, hey, so and so we need a webinar. Like I bought the perfect webinar stuff. I sent it over to him, he like records this webinar, he sends it back like a week later, I build my first funnel out ever inside of clickfunnels. I put this thing in, and I go to the marketing department say, Hey, guys, instead of like what you were doing, just drive traffic, I do what you do, but just send them to this thing here. And they're like, Alright, so they send people there. And people start often. And people start watching this thing, right? When we go deliver the offer. No one buys like not a single person purchase this thing. webinar ends, we're done. I go outside. I'm like, I'm like Kirsten to myself. I'm like, Oh, another fail What idea. And I remember how like one more last, like lackluster hope of trying to get this thing to work. And I walked back inside. And I just went to my computer and I clicked on the refresh button. And we made our first sale for like, it was like 797 bucks like $800. And I went nuts. And it's like I this Russell stuff works right funnels work. And I walked in the next day and I quit. And I set out on this journey for the for the next year of trying to understand how you know, basically the science of selling stuff online, like funnels and offers and messaging and all that good stuff. And the rest is history. Grant One sale. That's amazing. That's it. Blake There's no I think that's one thing I think a lot of us will relate to. And if you if you haven't relate to yet, you know, some people are watching and listening, they have it. The one thing I think a lot of people will tell you is that when you make your first ever sale online, whether it's $5 or 500, there's this feeling that happens where you just like there's no turning back, I want more of this experience again and again and again. Yeah, Grant Yeah. I love that, that so so your secret sauce as a business owner, it sounds like it's a combination of things. One, you stuck with this clearly right? And it took lots of tries to do. But I've watched you build from that, because I've seen some of the things you've created since then, which is just incredible. I think you've really been perfecting the art of the launch the product launch. He talked about that for a moment. Yeah. Blake So anytime you have a really creative idea, right? I'm never an advocate anymore. I used to be right of building it out first and then launch it. And I've kind of flipped that on its head. Now it's more like launch it first and then build it out. Because a lot of things that prohibit entrepreneurs from moving forward that stopped them in their tracks as they think they have to go build out these products and services first. And it's really difficult, right? There's a lot of friction in that. And almost it's like a recipe to fail and quit and give up and not want to do it again. So I'm more of a fan of See if you can go sell the idea that you have first and then go spend the time building it out. So anytime we have an idea, what we like to do is really go really hard in the paint, when it comes to launching because launching is definitely it's the way to prove your concept. It's the way that it gives you the short term capital to inject into building the process and fulfilling on it. And even putting more even gives you the capital you need to put into evergreen strategies like paid media and things like that. So we're just like what we do really well here my business partner and I is that we when it comes to a new idea, we really like to watch hard and aggressively like we like, we don't just like to send an email. We don't like to just make a Facebook post or a couple ads. Let's say we like to be the loudest people in the room where by the end of it, I'm hoping people look at me and like man, that Blake nubar dude, he's super annoying, because then I knew we did a good enough job because you couldn't escape us during that launch. So launching is one of those things where it's like, it's you got to become a master of it. Like you have to know how to do it. But you never want to stay in it. It's like learn how to launch and then learn how to move that stuff to the Evergreen model where it just keeps producing for your business day in and day out. So watching is it's it's something you have wasted you have to learn it you have to become really good at it and There's so many different types of launches you could do honestly, it's like, fall one of the frameworks and put your idea into that framework and then just see how it does and see if the markets willing to judge and pull out their credit cards to pay for it. Grant Are there certain social media platforms that you tend to focus on for your launches? Yes, so we, I'm a fan of Facebook. Blake I like have this love/hate relationship with Facebook, because it's like, as much as as great of a place it is, it's like you have very little control as an advertiser. So Facebook's a really good spot we enjoy. I think the all our biggest launch we've ever done was only by utilizing Facebook, which is crazy, right? It's just because Facebook to me is the hub. Like if you're on social media, you're on you have Facebook, on your phone, or you use Facebook, you might have the other ones too, you might have like tik tok and Instagram and all the other ones. But Facebook is definitely the core. So I'm like, Okay, let's focus on Facebook, which a reason behind that was a we didn't want to get overwhelmed. I didn't want to like every additional platform, you have to like fake have to. It's just more stuff you have to create. It's different type of content you have to create because the platforms have different ways of posting and stuff. So we're just like, let's do Facebook. That's our favorite. But now as we've expanded the business, and we've grown, we really focus on Facebook and Instagram, those are kind of the the two big honey holes, I would say when it comes to it. We do email marketing and things like that. But it's really Facebook and Instagram, because like I said, that's where every I mean, everyone's on Facebook, I mean, and you take it literal ticket, figuratively, the world's on Facebook. So what better platform to use, and the one where everyone's kind of congregating. Grant Okay, that's awesome. So you know where you're going to find your people. So you grew this business and you become expert at launches and launches. And you got to a certain point in your business, there was some time or place or condition you got into Lake where it led you to reach out and try to track down someone like myself, what were those conditions? Blake Yeah, so I remember, you know, the business was growing, we were doing really big revenue numbers. And I remember when I was working with Brian, right, previously, I remember, he had a consultant that was really advocate about understanding your data. And I didn't get it at first, I'm just like, What does any of this stuff mean? And as you really start to see, when your business grows, if you don't have a grip on the numbers, you're kind of in trouble. And I remember we were getting to a point where our revenues were, you know, high six figures every month. And I'm just over here. And I remember I think I reached out to I don't know, if I reached out to Steve, or Steve reached out to me. But I remember one instance happened in regards to conversations like, you need to talk to my dad. And I'm like, he knows this stuff. And he's, it seems like it's what he does. And I'm like, Okay, done deal. And the reason I reached out to you is because I realized that in order for your business to have a grip on it to have a pulse on what it's doing to scale it right with predictability. You're you have to understand these numbers, and especially having understanding numbers in a way where you can make really educated decisions based on that data. See, most entrepreneurs, when you're first getting started, you let your intuition guide your decisions. That's great. Like you have a gut reaction. Oh, this is a cool idea. Let's go watch it. But as that thing works, you need to use less intuition and more data to kind of guide where you want to go. Now, if you want to develop new products sure where your intuition run wild, right. But when you want to make really educated decisions on growing your company and what different verticals to go after what different traffic sources, it should no longer be this game of guessing right, which a lot of entrepreneurs start making the mistake of it's like I touched one thing it turned to gold, we think we can touch some more turns to gold. And it usually always backfires. It has to be the data. So I started thinking, Okay, I don't want to make that same mistake where it's like you think you're the end all be all, and everything you're gonna do is going to be magical. It's not, who can I contact that knows data because I'm staring at this stuff. And I it's like a foreign language. I'm like, looking at like, I don't even know, but I'm just staring at these. And I'm like, this is this way too much data here. And like, no one had a really easy way of organizing it so led me to contacting you. And that's kind of where things began. Grant That freaked me out at first and I'm like, Oh, yeah, we're gonna do some AI on this. That just sound weird to you. Blake Yeah, I was just like, you know, I've heard of AI before. And like my geeky brain can like understand it to an extent. But I'm like, I have no idea what you're talking about. Grant, just do your magic because this sounds crazy to me still. Grant Alright, so it's important to you to understand your business numbers, like you said, so that you can figure out some predictability. It helps you what influenced some of your planning or your next like, if you don't know your numbers in your business, right? Blake You can skate, you can look you can get to you can just run a start a successful business and grow it to an extent flying blind. Like you can do that. You could you know, you can get a little lucky. You can have something that's pretty stable. But if you really want to grow your business and you're wondering why you're capping out and everything, your tribes not working, there's a good chance you have no idea what your numbers are, right? So if you really there's I don't know another Way to scale a company then by really understanding your data, because when you understand your data, you know, what's converting what's working, what's not. And you can focus more energy on the things that are working and put more money behind those, hence, scaling those. And like kind of divesting out of things that aren't working so well. And the only way to do that is to really have a grip over the data inside of your business, which honestly, is probably the last thing entrepreneurs do, right? Because it's just so overwhelming. You just like the one thing you want to avoid. But I can't express at least in the last few months, how much I like realized how important it really is, to having a grip on it and what it can do for your business. It's the difference maker in your business. Grant Okay, that's, that's awesome. So when you got started working with us, how much did you have to know about AI? That, were we shoving it down your throat? We're like, hey, learn logistic regression. Come on Blake. Blake Great, I had no idea of anything you you you had, like, I knew nothing going into it. And I still didn't have to know that much. Because working with you, you're able to articulate the data in a way my brain my, you know, kindergarten brain could understand with pictures and awesome things like that. So going into it. No, I knew absolutely nothing. I didn't have to know anything, which was great. Because you knew exactly how that data worked. You knew how to show me and you basically just were withdrawal. You're like, this is what this means. I'm like, I understand that instead of me trying to figure out what to do. Or the guess is it literally taught you told me what, on this day or this time or after a holiday or before holiday or during this season? Or during this quarter? This is what you should do? and not do. I was like, I can understand that. So it was simple. Grant So let me ask you about that in terms of ways to make something like this easier for a business owner. Now that you've gone through this experience, what would be some some tips that you would share with others to help them in their journey going through this? Blake Yeah, just look, data is one of those things. Again, I can't stress it enough anymore. And I don't think many people geek out on it. Like, the thing is, always have a grip on your business when it comes to the numbers, because then you can have the ability to have someone like grant come in and help and help you and show you on what to do with those numbers. Right. Having those numbers is great. You might tell me all day, I know my conversions in my business. And I know, I know my opt in rate. And I know this grants the person that can tell you what that really means and what to do with it. And that's the difference. Because it's one thing, knowing about the numbers, the next is being able to take action on what that data means. And if you look at most things, right? There's every software on this planet will tell you numbers, right, here's your conversion rate, here's your OPT in rate, here's what won the split test. But there's another layer after that. And that was like that was what I was really impressed working with you is that you were the second layer, you were the the team that came in, I was like, Hey, this is what this means. That's great. But this is what it means to do with it. So I would advise anyone, that's whether you're just getting going or you're looking to grow your business, start to get a little grip on your numbers, because then you're able to, you know, work with someone as amazing as grant to help you really scale those numbers by making those decisions. Grant Blake, you've been more than generous with your time with us today. I really appreciate that. Thank you so much for doing this. Any final tips or comments to people who are starting to grow their business? Yeah, so starting to grow your business, always make sure a I'll start from the beginning, you're gonna solve a big problem, right? Blake Find a big problem, if you find out that you're capping a lot, right? There could be a lot of reasons, like I said, data could be one of them. But just remember, like, solve really big problems, right? Because then it's gonna give you room to grow. And as you start growing, get a real grip on those numbers. Because seriously, there's no other way to scale. You can't scale a business without that predictability. And having that predictability is going to give you the chance to grow your business on a whole new level. Again, understanding what those is and work yourself like grant or some this AI technology. That's absolutely amazing, right? It tells you exactly what to do in order to make those decisions. So no longer do you have to guess anymore. I mean, we've been guessing our whole lives as marketers with split testing and things like that. It's amazing to know that you can go into situations right where the data is, this is kind of how this is how it works. And these are the these are the ways you need to react based on that on on all that information. So that's my final words. I know it's more like this is the geek mind going but solve big problems, get a grip on your numbers and then find a way to take that those numbers and use them to make those decisions to grow your business on a whole new level. Grant Hey, thanks again, Blake for joining us. And thanks everyone for listening in. Until next time, get a grip on your numbers. Thank you for joining grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we're asking the question, How do I improve the quality of my leads? Do I use special language? Is there a framework I should use? Hey, everybody, welcome to another episode of ClickAI Radio. This is Grant. Okay. So, one summer, during my college days, I was selling long distance phone service, right? That definitely dates me, I was constantly looking for ways to find people who wanted to save money on their long distance service right now back then, as you probably know, the big fish in the ocean was AT&T. And so there is this other company that had bought a ton of their capacity and had got the rights to sell that service or that capacity. But at a reduced rate. Right. They clearly didn't have all of the infrastructure that at&t did. So they could undercut him. So I was selling this service and I was looking for customers, how am I going to find a customer? There were times that I stood in the local mall. This was in western Colorado, and I would try to solicit people, right? I'm trying to figure out how do I do this, right. So I went, I went to the mall and set up a little booth there. And you know, the drill, I'm trying to make eye contact as people are walking through the mall, and they were trying to avoid eye contact. And of course, we've all been there, right? We've all avoided the contact, I know I contact It was painful. Well, there must be a better way, you know, I thought so I had to find some other channel or avenue to find more qualified leads. So I started looking to small to medium businesses, I started thinking Wait a minute, they're going to be making a lot of phone calls. And they would most likely welcome this ability to reduce their costs. So I started literally going through the Yellow Pages, alright, that dates me again to write and I'm going through these businesses, and I'm showing up on like, I can save you some money on your bills. And they're like, hey, alright, suddenly, I found this vein of value, right? So I would go in, I would talk with the business owner. And I'd say I can save you about 25 to 40%. And they just started jumping in on it and signing up. And interestingly, while I was in those businesses, signing up the business owner, individuals, right employees in the business, they would come forward and they'd say, Hey, you know, is this for personal stuff, too? And I'd be like, Yeah, absolutely. So then I would sign up their employees as well for their own home service also. And so it was this wonderful connection that I sort of stumbled into? Well, it helped me get through college, right? The difference? We know, when I look back at that, right, it was finding the high quality lead, right, it was always interesting to me, that the individuals would come forward and sign up as well, meaning I could have approached those same individuals in the mall, and they would have likely walked the other way, right, they would have not made the eye contact. So when we're looking for high quality leads, it's particularly about finding the right individual, but even more important, it's where and when we find that individual. So in that scenario, you know, when I was finding the individual, they were clearly more comfortable buying the service in the context of their workplace, as their boss was buying it, then they were buying it standing in the mall. So discovering the buying scenarios for our leads, I think is one of the most important ways to improve the quality of our leads. And as you know, it's often that exact same person or business when you know when we get them into the right scenario that they'll purchase and become a customer. So the question then is, how do we discover those scenarios? Right? What are the techniques for doing that? If a scenario is that critical to reduce the time to value, then how do we find it? So in one of my startup companies, we had been developing some software solutions and at one point, I sold a two week consulting project to a large company. So me and my team went in and we did the work, things went, well, this large company liked it, and we got paid. That was all good. But I thought, Wait a minute, you know, always better to go back after a hot market and a warm market than it is a cold market. So I started showing up to that same company for the next six weeks after that, every few days, I just show up, and I check in on it. Hey, how'd you like what we did for you, hey, did that go pretty well don't want to be a pain. But we would love to do more stuff for you. Right? So I started walking the halls. And pretty soon I was having Hallway Conversations. Now, in a COVID. world, that would be a little more difficult. But there are virtual hallways, right that we can walk. So this enabled me to, you know, gather their scenarios, right, understand what their pain points were. And then that further enabled me to design a solution. So I crafted a solution that I felt would be meaningful to their scenarios. So not long after that, I had the opportunity to get a pitch that solution in the context of their scenarios, right, addressing their pain points, and we ended up closing a seven figure deal. So it's all about knowing our customer. Alright, that's that's real key thing. But it's, it's knowing the scenarios in which they're willing to, in fact, bring on and understand the value that it is we can deliver to them. If I address them directly in the mall, it's not going to go well, metaphorically. Now, the stories that I just shared, I think demonstrates some of the things that we can do to improve the quality of our leads. But I think that there's a challenge with these stories that I shared. And one of them is that I think they take a lot of time, right? That's a lot of effort to find the people to go, you know, cultivate the lead, but it's still a good effort. It's the right effort to do. But I often wondered, is there a way to reduce some of that effort. And one of the one of the techniques that we have used leverages AI. And so for most businesses, of course, their customer base shares some common characteristics, which brings them to the business. So creating a profile, right? If you sit back and think if I created a profile of my customers, right, and I fed them into some AI models, which is what we do, then you search for the people that align well with those profiles, that actually improves the quality of the leads. But I'll get nerdy here for a moment. Most important thing I think, in all this is that it never replaces the human touch in building business relationships. So I don't think that'll ever go away, at least for a long time, right. But what it does do is it puts the leads and our efforts into a quadrant. And so allows us to reduce the amount of effort that we do spend, so that we can have that human touch in the more important set of people that are more profitable, highly qualified leads. What it does is it introduces a quadrant, okay, fact some call it a confusion matrix fact, I hope I don't confuse you with the next 30 seconds. But it basically has these two variables. One is, is the quality lead predicted to be a quality lead, yes or no? Or is it actually going to be a lead? Yes or no? So those are the two case you need both of those. So in some cases, you'll have a person and is the person predicted to be a quality lead? No. Will they actually become one in real life? No, then the answer is we don't ever really want to spend our time least talking to them about what it is we have to sell anyway. So AI helps us to take that whole set of people and move them away. We don't want to spend our time there. The next the next layer, there's four of these is is this person predicted to be one? No, the AI said no. But in real life, it turns out, they would have been. And so that's a lost opportunity. We call that an opportunity cost, right. So those would be some that hey, just the AI got wrong. All right, the next area, the next two areas, though, is where we will spend our time, it's where it is predicted that person is predicted to be a quality lead, in reality, they won't be a lead. And so one of the ways we found helpful to mitigate that is to be very clear and crisp, on the profile of the customers know the customers the message there, right. And that keeps that quadrant and that section of people very small. All right. And then the last area is the course one that we really want to focus on. This is where the AI says you're predicted to be a quality lead, and in reality, you are quality lead and you will actually purchase. That's the goal, right? That's the vein of value that we're pursuing. So the real the real goal here is to let us get to those high quality individuals or businesses where then we can build those connections, those human relationships, figure out what the scenarios are for them and then make it really effective at delivering the kind of service that they need. Hey, if you're interested in reducing in reducing the effort to find high quality leads and reach out to us at click ai radio.com My name is Grant. Thank you for joining. Until next time, go get some quality leads. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.