Podcasts about automating

Have you ever had one of those moments where you suddenly feel completely motivated to get your money together? Maybe you looked at your credit card balance and decided enough was enough. Maybe you got tired of making good money but still feeling like you had nothing to show for it. Or maybe a new month gave you that fresh-start feeling and you decided this was finally going to be the month everything changed. That motivation can feel powerful. It gives you energy. It gives you hope. And sometimes, it's exactly the spark you need to get started. But motivation usually isn't enough to keep you going. Because eventually, work gets busy. You're tired. Your stress level changes. An unexpected expense comes up. Your friends want to go out. Or online shopping starts looking especially tempting after a long day. That's real life, and your financial plan needs to be able to work there too. In this episode, we're talking about why motivation isn't enough to create lasting money habits and what you actually need to stay consistent with budgeting, saving, spending, and your financial goals. Because if you keep falling off, you may not need more discipline. You may need a better system.   In this episode, we discuss: Why motivation can get you started but usually can't keep you consistent How relying on a feeling can make your financial plan fragile Why I increased my automatic savings and investing transfers instead of relying on motivation to reach my 2026 goal How your environment can make good money habits easier (or harder) to maintain Simple ways to create more friction around impulse spending and less friction around saving Why your budget needs to work for your actual life, not an idealized version of it How making room for the things you genuinely enjoy can make a financial plan easier to sustain Why accountability is a strategy, not a sign that you're bad with money How regular money check-ins can help you reconnect before you completely fall off track Why lasting financial change comes from building systems, routines, and an identity that supports your goals   This episode is especially helpful if you: Start each month motivated to manage your money differently but struggle to stay consistent Know what you "should" be doing with your money but have trouble following through Feel frustrated because budgeting, saving, or tracking your spending never seems to stick Have tried restrictive or generic financial plans that don't fit your actual life Want to stop relying on willpower to make good money decisions Are ready to build money habits and systems you can maintain long-term   Why this matters: It's easy to make inconsistency mean something about you. You fall off your budget, stop tracking your spending, miss a savings goal, or start overspending again, and suddenly the story becomes, "I just need to be more disciplined." But discipline may not be what's missing. If your financial plan only works when you're motivated, energized, emotionally regulated, and able to give it your full attention, the plan isn't built to withstand real life. That's why systems matter. Automating your savings removes a decision. Changing your environment reduces temptation. Building a realistic budget gives you something you can actually follow. Regular check-ins give you a rhythm to return to. Accountability helps you reconnect before a rough week becomes six months of avoidance. None of those things require you to feel motivated every single day. And that's the point. Sustainable money management isn't about creating the perfect version of yourself. It's about creating enough structure around the person you already are that following through becomes easier.   Timestamps: [02:15] Relying on a feeling to support a habit is a problem because feelings change. This means your habits change too. [05:59] Raya shares an example of how she took action towards a goal instantly, instead of leveraging motivation to try to achieve it. [12:51] If you know something matters to you, don't make it hard on yourself by leaving it out of your budget. Cutting those things out is not a recipe for being good with money. [16:03] The goal of Raya's 12 week money coaching program is to build a money management system that sticks long term.   Resources Mentioned: Listen to CGS Podcast #209 – My Personal and Business Goals for 2026 Get a Custom Budget Plan by Raya Request a free money call with Raya City Girl Savings Personal Finance Portfolio Financial Focus Coaching Program   If you've been telling yourself that you just need to try harder with your money, I want you to consider a different question: What would make following through easier? Maybe you need to automate the savings transfer instead of hoping there's money left at the end of the month. Maybe you need to remove the shopping apps that make impulse spending too convenient. Maybe you need a budget that actually includes the things you enjoy instead of requiring you to become a completely different person. Or maybe you know you do better with accountability and regular check-ins. Use that information instead of judging it. You don't need a financial plan that only works when you're at your best. You need one that can support you when work gets busy, motivation disappears, or life simply feels like a lot. The goal isn't to never fall out of rhythm. It's to have a system that makes it easier to come back. And every time you do, you're creating evidence that you are someone who can check in with her money, make intentional decisions, save on purpose, enjoy her life, and still make progress. That's how lasting money habits are built. Just a friendly reminder – you're not behind, you're building! Consistency compounds. The steady work you're doing now is shaping your next level, so keep going.

Nick Holzherr is originally from the Switzerland, but moved to UK when he was 7 years old, to the countryside. He had no real touch to computers until he was 18, when he attended university and everything picked up for him, tech wise. Eventually, he got into building businesses, and of those, he founded a coffee company, HR software company, and whisk.com, which was sold to Samsung in 2019. Outside of tech, he is the father to two young children. He enjoys skiing and snowboarding, and jokingly, he admits that running around ensuring his agents are prompted gets in the way of his family and hobby life. Through his entrepreneurial adventures, Nick spent large sums of money on legal contracts, ensuring that the i's were dotted and the t's were crossed. While he still feels that lawyers are very important and needed for certain scenarios, Nick wanted to create a way to level the playing field for small businesses, in the legal arena. This is the creation story of GitLaw. Linkshttps://git.law/https://nickholzherr.com/https://www.linkedin.com/in/nickholzherr/ Current Sponsors: Tiger Data Protected Harbor Render Fitnexa Perplexity Entelligence Checkout our Stacklist! https://stacks.codestory.co/ Hosted by Noah Labhart | Technical Founder & Startup Mentor. Our Sponsors:* Check out Granola and use my code granola.ai/CODESTORY for a great deal: https://granola.ai* Check out Perplexity and use my code CODESTORY for a great deal: https://www.perplexity.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

This session features Ray Huang of Canopy Connect and explores how insurance technology is evolving from simple document collection into a more connected intake and sales ecosystem. The conversation explains the importance of structured data, giving prospects multiple ways to provide information, reducing repetitive data entry, integrating information across an agency's technology stack, and ultimately allowing producers to arrive at discovery conversations better prepared. For middle-market producers, the larger lesson goes well beyond Canopy Connect. Technology should remove administrative friction without removing the producer from the relationship. Whether producers are gathering declaration pages, preparing applications, working with referral partners, comparing policies, or using AI inside their CRM, the objective is the same: use technology to create more time and better information for the conversations that actually require human expertise. The session also offers an important warning about AI. Producers shouldn't adopt AI simply because it's available. AI can dramatically improve workflows and productivity, but information still needs to be accurate, sourced, and verified. In an advisory business built on trust, efficiency means very little if the technology causes the producer to walk into a meeting confidently armed with bad information. Connect with:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠David Carothers LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ray Huang Linkedin Canopy Connect⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Visit Websites:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Killing Commercial⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Crushing Content⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Power Producers Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Policytee⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Dirty 130⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Extra 2 Minute⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠   

Making more money doesn't always create a greater sense of financial security. In this episode of The Budgetdog Breakdown, Brendan answers real listener questions about teaching children financial literacy, investing without emotion, achieving financial independence, and why people can earn significantly more money than their parents while still feeling financially behind. The conversation explores why financial education needs to become part of everyday family life, how parents can teach children about money through simple conversations, and why financial confidence comes from education and repeated small wins rather than trying to have one perfect conversation. Brendan also discusses why investing should be boring, how automation can remove emotional decision-making, and why constantly checking your portfolio can lead to poor financial behavior. The episode also explores the idea of retiring at 40 and the importance of changing your beliefs and systems before expecting different financial results. Finally, Brendan breaks down why earning more doesn't necessarily mean feeling richer, discussing inflation, purchasing power, and the importance of turning active income into investments and assets. Money isn't just about how much you earn. It's about what you do with it. Episode Timeline and Highlights 00:00 Why wealthy people rely on systems 00:18 Teaching kids about money 03:26 Making financial education part of your family culture 04:21 Taking ownership of your financial education 05:21 Teaching children about ownership and investing 06:39 Why investing should be boring 08:04 Automating your financial system 09:25 Is FIRE at 40 actually realistic? 10:34 Beliefs, identity, environment, and systems 11:57 Building confidence through micro-wins 13:10 Checking your investments too often 14:03 Why emotional investing can hurt your results 14:58 What happens when the market drops 16:37 Why earning more can still leave you feeling broke 17:27 Inflation and the money supply 19:03 Why owning assets matters 20:09 Turning income into investments Key Takeaways • Financial education should become part of everyday life • Parents need to educate themselves before teaching their children • Investing doesn't need to be exciting to be effective • Automation can reduce emotional financial decisions • Constantly checking investments can encourage reactive behavior • Small wins can build confidence over time • Financial independence requires changing both behavior and systems • Higher income doesn't automatically create greater purchasing power • Inflation can reduce the value of money held in cash • Turning active income into assets can help build long-term wealth Quotables "Financial education starts with you, not your ten-year-old." "The game of money and the game of wealth is boring." "If you can prove to yourself that you're going to do what you say you're going to do, you'll build micro confidence." "Your financial future isn't determined by where you are today. It's determined by the system you build from this point forward." The goal isn't to make money exciting. It's to build a system that works whether you're excited, scared, or completely uninterested.

S6:E80 AI, Empathy & Why Humans Still Matter with Nathan Strum AI can answer the phone. It can schedule appointments. It can listen to sales calls, extract insights and eliminate tedious administrative work. But can it make someone who has just lost their cat genuinely feel heard? Nathan Strum doesn't think so. For more than 20 years, his company Abbey Connect has built its reputation around human receptionists. About a year ago, Nathan faced the same decision confronting millions of business owners: how do you embrace AI without destroying the human experience that made the business valuable in the first place? He didn't reject AI. Quite the opposite. Nathan calls the technology a game changer and believes businesses that ignore it are doing themselves a disservice. But Abbey Connect has approached implementation by asking where technology can support people rather than automatically replace them. If people don't trust how AI is being introduced, efficiency alone isn't enough. If employees fear that every new AI tool is ultimately designed to eliminate their jobs, customers may eventually feel the effects of that distrust. And if customers believe they're interacting with a caring human when they're actually interacting with software engineered to simulate empathy, the business introduces an entirely different trust problem.

Automating eligibility and benefits verification takes more than establishing a connection to a payer. Benj Kamm explains how data quality, calibration, payer variation, and exception handling determine whether automation produces a complete and usable result.

A NASA engineer turned realtor breaks down the AI stack behind 140 deals and $100M in Denver sales.Juan Munoz spent six and a half years on the International Space Station program at NASA, with stops at Boeing and Sierra Space along the way, before walking away to sell real estate in Denver. More than 140 transactions and $100 million in volume later, he still runs the business like an engineering project: reverse engineer the outcome, automate the repeatable parts, and delegate anything outside the top 20 percent of his value.In this episode he walks through what is actually in production. That includes the lead intake system running behind his ads, the way he builds campaigns with AI, and a funding program that lets his own sellers profit like investors on their own homes. He also gets into the part most people skip, which is what happens to your data once you hand an AI tool access to everything you use.What you'll learn:How the "list assist" program works, where Juan funds the renovation on a seller's home, coordinates the contractors, and aligns everyone's upside on a percentageHis lead intake stack end to end: Meta and Google ads into short and long form intake forms, Zapier into Follow Up Boss, then AI voice and text follow-up with engagement alertsThe dollar-per-hour math that finally got a self-described control freak to hire an assistant and a transaction coordinatorWhat a Denver agent is seeing right now in short sales, pre-foreclosures, and tightening landlord rules in ColoradoWhy prompting and personalization are the real bottleneck, not the toolAbout the guest:Juan Munoz is a Denver-area realtor and team leader with the Apollo Group at eXp Realty, a roughly 90-person team that ranks number one for large teams within the brokerage. Before real estate he was an engineer on the NASA International Space Station program, Boeing's Dreamliner, and Sierra Space's Dream Chaser.Links:

Would you show up to compete in a Formula One race in a bike?

Can electricity grids built for an earlier era support AI data centers, expanding manufacturing, electric vehicles, severe weather, and rising customer expectations at the same time? In this episode of Tech Talks Daily, I speak with Mark Hollis, utility executive advisor at SAP, about the pressures reshaping the utility industry and the practical choices available to leaders today. Mark spent over 15 years at Duke Energy before moving to SAP, where his work gives him visibility into utility organizations across North America. Mark describes a combination of load growth, disruption, long construction timelines, and regulation. Data centers are receiving much of the attention because of the electricity required by AI, but he argues that they are only part of the story. Manufacturing is returning to parts of North America, transport and heating are becoming increasingly electrified, and utilities must prepare for wildfires, hurricanes, winter storms, and other events that affect generation and delivery. The obvious response is to produce additional electricity, but every option comes with physical and commercial limits. Wind and solar contribute to the generation mix, although output depends on conditions. Small modular nuclear reactors could support future demand, but commercial deployment takes time. Batteries can store electricity and release it later, but they do not generate the power they hold. Customer programs can also reduce pressure at busy periods, including arrangements that allow a utility to adjust connected thermostats by a few degrees. This makes modernization a portfolio of decisions rather than a single bet. Utilities must decide how to divide capital among generation, transmission, resilience, customer systems, and new technology. The people who understand existing processes are often the same employees needed to design and implement replacements. At the same time, information technology and operational technology are becoming increasingly connected, forcing companies to reconsider how business functions share information and how technology decisions support outcomes across the enterprise. AI creates another tension because it contributes to electricity demand while also offering utilities new ways to work. Mark says most utilities he meets are cautiously optimistic. Their questions include where to begin, whether the value is proven, how long adoption will take, and whether poor data must be fixed before useful work can start. He warns against choosing the hardest problem first or judging a business process by the forgiving standards people accept from consumer AI tools. His advice begins with the business problem. Automating an inefficient process can make it more expensive and harder to correct. Utilities should define what they need to improve and why, establish connected data with the right business context, set clear quality requirements, and retain human review where errors could affect customers, safety, finance, or regulatory obligations. Mark brings this to life with several utility AI use cases. AI could summarize customer interactions across field service and contact center systems, allowing the next employee to understand what happened previously. It could review billing exceptions during unusually hot or cold periods and support earlier customer communications when consumption is likely to produce a much higher bill. He also discusses using AI to summarize lengthy rate-case rulings before approved changes enter billing systems. During outages, an AI system could help dispatch crews by considering skills, equipment, parts, certifications, location, safety, and customers with medical needs. A human dispatcher could then review and approve the recommendation rather than building the complete schedule manually. The opportunity is real, but so are the limits. Utilities operate regulated infrastructure where reliability, safety, auditability, and public trust cannot be treated as optional features. Where should the industry begin, and which use case offers the right combination of low effort, meaningful impact, and manageable risk? Listen to the conversation and share your thoughts with me.  

Episode SummaryWhat if SEO belonged in the earliest business conversations; not just in a checklist after the strategy is already set? In Part 2 of Crystal Waddell's conversation with Will Soprano, Will reframes SEO as a company-wide philosophy that connects search behavior, customer insight, product-market fit, brand messaging, and revenue.Will and Crystal also unpack why small businesses get into trouble when they copy companies like Amazon, Apple, or Nike. Instead of duplicating big-brand tactics, they explain how to identify the intended impact, use the advantages of being small, and exploit market inefficiencies. Crystal brings the lesson to life with the story of growing her handmade senior-night numbers from a $30 product into a premium $300 offer by refusing to compete on price and continually disrupting her own business.What You'll LearnWhy SEO should inform executive, product, brand, content, email, and social decisions.How semantic alignment reveals when different channels are telling different stories.Why deterministic fact-checking and human review still matter in AI-assisted workflows.How Google Search Console can expose gaps between why people arrive and what a business intends to offer.Why product questions and answers may serve a small ecommerce business better than replacing them with a chatbot.How small businesses can use speed, focus, and self-disruption as competitive advantages.Why knowing your ideal customer and your own strengths is essential to finding an inefficient market.Timestamps0:00  SEO as a company philosophy3:19  A fast check for semantic alignment across channels4:05  Will's tool-agnostic SEO stack and internal tools4:37  Building a deterministic fact-checker5:25  Automating audits, reporting, and SERP documentation7:01  ICP and persona artifacts that help content teams7:41  Spotting product-market fit problems in Google Search Console8:11  Crystal's practical LLM workflow for a small business9:44  Why copying Apple, Nike, or Amazon fails10:34  Product Q&A versus chatbots for ecommerce12:22  Start with the impact—not the tactic12:55  Netflix, self-disruption, and the small-business advantage15:15  Using resources differently at large and small companies17:09  Crystal's $30-to-$300 product evolution19:21  How to find and exploit an inefficient market21:38  Where to connect with Will22:31  Final thoughtsEpisode Highlights“SEO is a philosophy of finding our customers and helping them find us.” — Will Soprano“The only company that gets to act like Apple is Apple. The only company that gets to act like Nike is Nike.” — Will SopranoListener Action StepsCheck whether your website, email, and social channels describe the same customer, problem, and promise.Choose one tactic you admire from a large brand. Write down the impact it creates, then design a smaller, more appropriate way to create that impact in your business.Review the real queries bringing people to your site in Google Search Console. Compare them with what you intended each page or product to be known for.Add or strengthen customer questions and answers on one important product or service page.Identify one market inefficiency: a need competitors overlook, serve poorly, or address only by competing on price.Resources & MentionsConnect with Will Soprano on LinkedIn: linkedin.com/in/willsopranoBook a mentoring conversation with Will: growthmentor.com/mentors/will-sopranoRead Will's writing: williamsoprano.com/blogLearn more from Crystal Waddell: simpleandsmartseo.comAbout the GuestWill Soprano works at the intersection of SEO, digital products, brand strategy, content, and technology. Drawing on experience with startups and Fortune 500 companies, he helps businesses make their products and services discoverable—and helps customers find them.About the HostCrystal Waddell is an SEO educator, Shopify seller, and visibility strategist for creative entrepreneurs. On The Simple and Smart SEO Show, she shares practical tools that help business owners turn visibility into meaningful business results.Text me your questions or comments!Hey, Shopify store owners! (Especially if you're selling on Etsy, too!)Here's a quick question: Are people actually finding your products on Google?If SEO feels confusing, overwhelming, or like something you'll "get to later", this is for you.I'm hosting a free, seven day Shopify SEO challenge that breaks it down into simple, doable steps.No tech headaches, no fluff. Join us at  Hey, Shopify store owners! (Especially if you're selling on Etsy, too!)Here's a quick question: Are people actually finding your products on Google?If SEO feels confusing, overwhelming, or like something you'll "get to later", this is for you.I'm hosting a free, seven day Shopify SEO challenge that breaks it down into simple, doable steps.No tech headaches, no fluff. Join us atSupport the showFree checklist: Is your Shopify store quietly losing sales? Run the 5-minute self-check →Book a Strategy Call With Crystal!Want to follow up on what you've heard? Search the podcast!AFFILIATE LINKS:SEO TOOLS :Keywords Everywhere: My favorite keyword research tool!Get SurferSEO! The ultimate content creation tool!E-COMMERCE TOOLS:Start your Shopify Store! (use my link)Klaviyo: The leader in Ecommerce EmailGrid and Pixel: Get John and his team's help in setting up your flows and campaigns!SOCIAL MEDIA TOOLS:Metricool (Awesome and easy to use social media scheduler!)Note: If you make a purchase using one of my links, I ...

HOW CAN AUTOMATING YOUR FINANCES HELP BUILD WEALTH? WATCH ON YOUTUBE TYLER KLUGE CFP®, ChFEB℠, CPWA®, CDFA®, CEPS Senior Financial Planner TESSA HALL Media and Communications Specialist About This Episode Building wealth does not always require making more financial decisions. In fact, constantly checking your investments, reacting to market movements, or deciding what to do with extra cash can create more opportunities for emotional mistakes. In this episode of Healthy, Wealthy & Wise, Tessa Hall speaks with BWFA Senior Financial Planner Tyler Kluge about the financial habits that can make staying on track easier. They discuss what to automate, how often to review your investments, where to keep emergency savings, and why a well-designed financial strategy should not require constant adjustments. Tyler also explains how regular planning conversations can help account for changing goals without abandoning a long-term investment strategy. Learn how BWFA can help you build a financial plan that keeps you on track without constant attention. Explore our Financial Planning services or schedule a complimentary consultation to discuss your goals. Frequently Asked Questions About Building Wealth Through Financial Habits How can automating your savings help you build wealth? Automating savings can help make consistent progress toward financial goals without requiring a new decision every month. Tyler recommends first establishing appropriate cash reserves and then considering automatic contributions to retirement plans, IRAs, brokerage accounts, or other investment accounts based on your goals. Automation can also reduce the chance that money intended for long-term savings is unintentionally spent. How often should you check your investments? How often you check your investments should depend partly on how you respond to market movements, but Tyler suggests that most investors do not need to check them daily. Constantly monitoring markets can create stress and tempt investors to react to short-term changes. He suggests that monthly may be sufficient for many people, while broader financial planning reviews can occur every six months or at least annually. Does reviewing your portfolio mean you need to make changes? No. A portfolio review does not automatically mean your investment strategy needs to change. Tyler explains that major shifts are relatively infrequent, while regular check-ins provide an opportunity to discuss changes in income and expenses, upcoming purchases, retirement withdrawals, or other financial goals. Those changes can then be evaluated within the existing long-term strategy. How much money should you keep in your checking account? The appropriate amount depends on your spending and financial situation. Still, Tyler suggests roughly one to one-and-a-half months of expenses as a general guideline for a checking account used to pay bills. Additional emergency savings may be better suited to an account that earns a competitive rate, such as a high-yield savings or money market account, while remaining accessible when needed. Why can paying less attention sometimes make you a better investor? Paying less attention can reduce opportunities to make emotional decisions based on short-term market movements. Tyler points to periods of rapid market declines followed by sharp rebounds as a reminder that reacting to a single difficult day can interfere with a long-term strategy. The goal is not to ignore your finances, but to combine intentional reviews with a strategy designed to withstand normal market fluctuations.

Trucking is spending a fortune on AI and automation. But what if fleets are automating the WRONG damn thing?AI can quote freight, process tenders, move data, predict maintenance problems and eliminate hours of back-office work. What it CAN'T do is lead your people.On this episode of Brake Check, Charles puts both sides of trucking's technology revolution in the hot seat. Grace Sharkey of Orderful has lived freight from nearly every angle .... brokerage executive, FreightWaves journalist and now freight-tech insider. We break down AI in trucking, freight automation, EDI, back-office technology, carrier productivity and how small fleets can separate REAL technology from another expensive demo with “AI” slapped on the front. What should a 20-truck carrier automate FIRST? What should stay human? How do you prove a technology investment actually delivers ROI? And how can you tell the difference between a real AI product and somebody who simply wrapped a shiny dashboard around an API?Then Dr. Vernard “Doctor V” Jenkins of Knight-Swift Supply Chain takes the other side.With 29 locations and roughly 10 million square feet of operations, Doctor V tackles leadership, employee retention, frontline management, AI implementation, change management and developing the next generation of transportation leaders. Because a dashboard can tell you WHAT happened. It still takes a leader to figure out WHY. And then we put $100,000 on the table:SOFTWARE OR PEOPLE?You can only choose one.If you're a fleet owner, trucking executive, owner-operator, freight broker, logistics professional, operations leader or anyone considering AI and automation, watch this before buying another piece of technology.The future isn't about choosing humans OR machines.It's knowing which damn job belongs to which.AUTOMATE THE WORK. NOT THE PEOPLE.#Trucking #AI #ArtificialIntelligence #FreightTech #TruckingIndustry #Automation #Logistics #FleetManagement #TruckDrivers #Leadership #SupplyChain Learn more about your ad choices. Visit megaphone.fm/adchoices

A former Marine who did eight combat deployments in eight years now spends his time teaching people to stop pitching and start asking better questions.Keith Gillispie, founder of REI Automated, left the Marines in 2020 to build a real estate investing and coaching business that's helped more than 1,200 people, with close to 580 of them closing their very first deal. He broke down exactly how he qualifies, questions, and consults his way to the close.Why "Selling" Is the Wrong Mindset (Stop Being House Horny)Keith's blunt term for pushy sellers: house horny, chasing the deal so hard that they force the wrong solution onto someone just to close it.His fix is simple: stop selling, and start asking great questions that guide the prospect to the answer on their own.He reviews sales calls with his own team every week, and the biggest issue he catches with newer reps is a used-car-salesman energy that shows up the moment they stop asking and start pitching.Qualify for the Right Industry FirstKeith gets over a hundred Facebook messages a day, and his team has to pre-qualify people before he ever gets on a call.A recent example: a rep flagged someone as "qualified" who actually wanted coaching on commercial real estate, which Keith doesn't teach at all. Same broad label, completely wrong fit.Even inside a niche that sounds close enough, being in the wrong specific industry wastes everyone's time.Qualify for the Right Problem: Your Core Competency Must Match Their Core ConstraintKeith's rule: everyone has problems, but that alone doesn't make someone a fit. What he's good at has to be exactly what the prospect is bad at.He calls this matching your core competency to their core constraint. If there's no real gap for him to fill, there's no real reason for the two of them to work together.This is what separates a win-win engagement from a sale that shouldn't have happened in the first place.Qualify for the Right Money (Or Every Call Becomes Charity)Even with the right industry and the right problem, a prospect still has to be able to afford the solution, or the call turns into free coaching.Keith shared a recent example of two prospects who badly wanted to join his program but couldn't spend even $300, when his coaching runs into the thousands.He's direct with his own team about this: sending him financially unqualified leads turns every call into charity work, which isn't sustainable as a business, even though he's genuinely happy to hand out free value when it makes sense.Diagnose Before You PrescribeKeith estimates that more than half of the people who come to him think they know what they need, and they're wrong.His example: prospects ask for automation when they don't even have leads coming in yet, or don't know how to structure a deal. Automating a broken process just scales the chaos faster.He compares it to a patient self-diagnosing before seeing a doctor. The seller's job, like a doctor's, is to ask enough questions to find the real issue before recommending anything.Get Comfortable Asking Uncomfortable Financial QuestionsKeith walks straight into questions like current income, whether that number is gross or net, and total debt, early in a call, because he can't build a plan without knowing where someone actually stands.He compares it to Google Maps: you need a point of origin and a point of destination before you can build the route in between.Roughly one in twenty-five or thirty people push back on how direct these questions are. Keith's response is straightforward: if they can't handle these basic questions now, the program itself is going to be a rough fit.Build Yes Momentum With Small AsksKeith structures his qualifying questions to build what he calls yes momentum: each small, reasonable question makes the next one easier to answer.He starts broad, what do you do, how long have you done it, before narrowing into income, debt, and financial specifics, so the conversation earns its way to the harder questions instead of opening with them.That same sequence doubles as training. The financial questions he asks on a discovery call mirror the exact questions his students will later need to ask sellers and private lenders.Ask Questions, Don't Make StatementsKeith's rule for himself: don't make a statement for the first 60 percent of any sales call, aside from short softening statements like "I'm glad you asked that."The only exception is a brief acknowledgment before turning right back around and asking another question.Whoever is asking the questions is steering the conversation, and Keith treats that as true for the entire first half of every call he runs.Never Fully Answer a Question Without Asking One BackKeith borrows from Socratic questioning: when a prospect asks a direct question, like how fast he can close on a house, answering too quickly and too specifically can blow up the deal in either direction.Instead, he gives a partial, honest answer, then asks a question that uncovers the real reason behind their question, like whether they need to move fast or need more time.That extra step means his eventual answer is actually calibrated to what the person needs, instead of a generic response that might scare them off or fail to meet an urgent situation.Become a Lifelong Student of Your CraftKeith has taken more than ten thousand sales calls over eleven years and still says the more he learns about sales psychology, the more he realizes he doesn't know.His advice for anyone serious about improving: invest in a coach or mentor who can review real calls and point out specific gaps, rather than trying to learn everything for free through trial and error.His framing: you'll pay for the lesson either way, either in money to a mentor, or in wasted time and missed opportunity learning the hard way."People don't care how much you know until they know how much you care." — Keith GillispieResourcesConnect with Keith Gillispie on LinkedIn.Visit REI Automated to learn more about Keith's education, software, and coaching community for real estate investors.Join our LinkedIn cohort and learn to prospect the right way.Visit Blue Mango Studios for help creating podcast production content.Sponsorship OffersThis episode is brought to you in part by Hubspot.With HubSpot sales hubs, your data tools and teams join a single platform to close deals and turn prospects into pipelines. Try it for yourself at hubspot.com/sales.This episode is brought to you in part by the TSE Sales Foundation.Improve your connection on LinkedIn and land three or five appointments with our LinkedIn prospecting course. Go to the salesevangelist.com/linkedin.CreditsAs one of our podcast listeners, we value your opinion and always want to improve the quality of our show. Complete our two-minute survey here: thesalesevangelist.com/survey. We'd love for you to join us for our next episodes by tuning in on Apple Podcast, Google Podcast, Stitcher, or Spotify. Audio provided by Free SFX, Soundstripe, and Bensound. Other songs used in the episodes are as follows: The Organ Grinder written by Bradley Jay Hill, performed by Bright Seed, and produced by Brightseed and Hill.

In this episode, Kyle sits down with John Smolen of Atmosphere TV on the floor of the National Restaurant Show to talk about the most overlooked real estate inside a restaurant: the TV screen. John breaks down how Atmosphere turns idle screen time into a monetizable, hands-off asset for operators.Connect with John on LinkedIn:https://www.linkedin.com/in/smolenjohn/Restaurant Real Estate Profitability Calculator:⁠⁠https://calculator-app-softmind-solutions-projects.vercel.app/⁠⁠Email: ⁠⁠kyle@10rep.co⁠⁠ | Instagram: @kyleinserra | LinkedIn: Kyle Inserra |

“If you feel that a product is hard to eval, or you feel like, ‘I don't even know how to eval this,' it's a strong smell that your product isn't good.”— Hamel Husain, on AI product designAn AI data agent tells you last quarter's net revenue. It doesn't show the metric definition, source tables, filters, query, intermediate calculations, or assumptions. You can't trust the answer without asking a data scientist to reproduce it.Hamel Husain argues that the eval problem is evidence of bad product design. If the user can't inspect the work well enough to decide whether the answer is right, another scoring pipeline won't rescue the experience. The fix begins by exposing the evidence and checks a domain expert actually uses.Follow that problem far enough and you arrive at a broader argument: AI isn't killing data science. It's creating more noisy, black-box systems that need hypotheses, experimentation, search expertise, and judgment. Hamel suggests that the people doing this work may eventually be called AI scientists.This episode connects those two ideas. Building AI products people can verify and understanding whether those products work are becoming part of the same job.“AI has made data science way more valuable than ever before, because now you have way more data and way more noisy signals that you need to reason about and debug.”— Hamel Husain, on the rise of the AI scientistYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.

Travis Chappell and producer Eric break down a simple but powerful financial concept: how small daily spending decisions can add up to thousands of dollars over time. Inspired by a clip from Mel Robbins, they discuss the reality behind spending $27.40 a day, the power of compound interest, and why “paying your future self first” can be one of the simplest ways to build wealth. They also explore the tension between saving and enjoying your money, from expensive movie nights to family activities and everyday purchases. On this episode we talk about: How spending just $27.40 a day adds up to $10,000 over the course of a year How investing small amounts consistently can grow into significant wealth through compound interest Why financial advice can be simple without being easy The importance of “paying your future self first” and automating your savings How to balance financial discipline with spending money on experiences and the things you enjoy Top 3 Takeaways Small daily expenses add up quickly. $27.40 might not feel like a huge amount in isolation, but spending that amount every day adds up to $10,000 over a year. Pay your future self first. Automating money into investments or savings before you have a chance to spend it makes building wealth a priority rather than something you hope to accomplish with whatever money is left over. Simple doesn't mean easy. The math behind saving and investing is straightforward, but actually choosing to delay gratification and consistently prioritize your future can be difficult. Notable Quotes "Sometimes things in life are simple but not easy." "You pay your future self first." "It's not fun. But it'll also make you think twice about spending money that you know you shouldn't be spending." Connect with Travis Chappell: Instagram: https://instagram.com/travischappell Other: https://travischappell.com A Word from Our Sponsors: - The most successful business owners don't do it all themselves — they delegate. Upwork lets you build a team of highly skilled specialists for every function your business needs, so you can focus on what you do best and let experts handle the rest. Visit Upwork.com right now and post your job for free! - Go to Leesa.com for 30% OFF select mattresses (through September 13, 2026) PLUS get an extra $50 off with promo code TMM, exclusive for my listeners Learn more about your ad choices. Visit megaphone.fm/adchoices

You can spend a year building a “better” process and still end up with more work, more frustration, and a team that feels stuck. We've both lived it, and the painful truth is simple: when you optimize efficiency without a clear destination, you just drift faster. This conversation is about stopping that cycle before your tools and automations lock in the wrong future.We walk through the Clear Destination Framework to replace chaotic drift with real alignment. I share why the first job of leadership is naming, not delegating, and how leaders unintentionally “hand over the keys” to great hires, shiny AI tools, or new systems before clarity exists. Then we get practical: using AI to interview you until your vision is clear, capturing your raw truth through voice dictation, and turning messy thinking into a usable direction your team can follow.From there, we talk about context and why it is the missing ingredient in most AI workflows. By uploading your real sources of truth, like website URLs, playbooks, onboarding docs, transcripts, reviews, and product materials, you help large language models analyze your plan for misalignment and friction. Finally, we dig into Gemini Notebook (formerly NotebookLM) and why visual communication, like mind maps, slide decks, and infographics, can speed adoption and reduce confusion more than a 20-page manual ever will.If you want less guesswork and more traction, listen through and build toward the right destination, not just a faster version of today. Subscribe, share this with a leader who is “improving” everything, and leave a review with the biggest place you're seeing drift right now.Send us Fan MailSupport the show

Host: Lalo Solorzano & Andy Shiles Guest(s): David Smason Published: September 3, 2026 Length: Approx. 35 minutes Presented by: Global Training Center Summary Artificial intelligence is rapidly changing global trade—but what happens when the technology is placed directly in the hands of customs and border agencies? In this episode of Simply Trade, Lalo Solorzano and Andy Shiles sit down with David Smason, co-founder of CargoSeer, whose technology was recently acquired by BigBear.ai, to explore how AI is being used to modernize cargo inspections, customs enforcement, and border operations. David explains how CargoSeer began with a focused challenge: helping operators analyze cargo X-ray images faster and more effectively. That concept evolved into an AI-powered decision-support platform capable of bringing together imaging, trade documentation, supply-chain information, and other data to help frontline operators identify higher-risk shipments and make better-informed decisions. The conversation explores reducing cargo release times, automating the analysis of empty containers, improving revenue collection, identifying counterfeit goods, and creating greater consistency across ports and inspection teams. They also discuss an important principle behind successful government AI adoption: technology needs to support the operator rather than force the operator to adapt to the technology. For trade professionals, this episode offers a fascinating look at the other side of the compliance equation—and how increasingly sophisticated technology could reshape customs enforcement around the world. Main Topic / Discussion AI is moving beyond private-sector trade compliance tools and into customs and border operations. David explains how CargoSeer developed AI technology around the workflows of frontline operators. Rather than creating technology first and searching for applications afterward, the company studied how operators actually inspect cargo and make decisions, then built technology designed to augment those processes. The result is an approach that can combine cargo imaging with trade, documentation, supply-chain, and other available data to help agencies prioritize higher-risk shipments while accelerating the review of lower-risk cargo. One example discussed is empty-container inspection. David explains that operators can traditionally spend several minutes adjudicating an empty container. CargoSeer's AI was designed to analyze these scenarios in seconds, allowing operators to focus more attention on shipments requiring human expertise. The conversation also examines how AI can improve consistency. Experienced customs officers often develop instincts after decades on the job. AI creates an opportunity to capture patterns from effective inspection methodologies and apply them more broadly across an agency. David also discusses CargoSeer's work in El Salvador and how highly customizable AI can help customs administrations address their specific enforcement, revenue, and operational objectives. Looking ahead, David sees AI supporting a new generation of customs systems in which information from multiple agencies and sources can be analyzed in the background while operators receive the specific information they need to make a decision. Key Takeaways • AI can help customs agencies prioritize higher-risk cargo instead of relying heavily on randomized inspections. • Combining cargo imaging with trade documentation, supply-chain data, and other information can give operators a more complete picture of each shipment. • Automating straightforward inspection scenarios—such as identifying legitimate empty containers—can free officers to spend more time on higher-value enforcement activities. • AI could help customs administrations capture the knowledge of experienced operators and apply successful inspection methodologies more consistently across ports, modes, and teams. • Better targeting doesn't necessarily mean inspecting more cargo; it can mean making the cargo selected for inspection more relevant to an agency's enforcement objectives. • Customs AI must be customizable because air, ocean, land, and express environments have different operational requirements. • Successful government technology adoption depends heavily on designing systems around frontline operators and their existing workflows. • Greater predictability in enforcement can benefit compliant importers by creating clearer expectations while increasing the likelihood that noncompliant shipments receive additional scrutiny. Resources & Mentions • Global Training Center • BigBear.ai • David Smason - LinkedIn • BigBear.ai - CargoSeer Acquisition Announcement Credits Host: Lalo Solorzano Andy Shiles Guest(s): David Smason - LinkedIn Producer: Lalo Solorzano

What happens when decentralized blockchain technology meets artificial intelligence's reasoning capabilities? In this throwback episode from 2025, we sit down with Jonathan King, Principal Investor at Coinbase Ventures.JK breaks down the origin story of Coinbase Ventures, from its lean, ecosystem-indexing beginnings in 2018 to managing over 500+ investments and deploying capital via the Base Ecosystem Fund. He provides a deep dive into Coinbase Ventures' flagship thesis on the convergence of crypto and AI.Discover why Jonathan believes autonomous AI agents using crypto rails (like stablecoins and self-custody wallets) will become the primary drivers of economic activity. He also highlights portfolio breakouts like Vana Network and Sapien, shares how AI developers like Devin and Cursor will spark an on-chain app explosion, and reveals what he looks for when evaluating technical co-founders.Support us through our Sponsors! ☕ Want to make content like ours? Sign up with Castmagic to make your creative process easy: https://bit.ly/CastmagicReferral Work smarter, grow faster. Automate your SEO, get AI insights, and manage all your clients in one place with Helm. Start today 50% off your first month at helmseo.comDouble your team's efficiency with COCO. Hire dedicated AI employees for copywriting, research, and CRM. Use code REF-W8CBVH for an exclusive 5% off your first order: https://coco.xyz/dashboard/hire/plan?ref=REF-W8CBVH Do you want to grow a business? Go from an idea to livebusiness in minutes. Use our Referral code: edgeof to 50% off your first month at https://www.willo.ai/When you purchase through these links, we may earn a commission. ____

Itamar Syn-Hershko has been a tech guy since he was a wee lad - IE he was teaching his kindergarten teachers how to use their computers. He learned how to "do stuff" tech wise through playing games back in the day. Back in the day he was one of the original developers on a document based database, as well as building a consulting company. Outside of tech, he is the father of 5 kids, all of them 7 and under... so his hands are quite full. That said, he does enjoy learning new things and traveling, when he has the time. Itamar founded a boutique consulting agency, helping businesses succeed in data related projects. Like most smart agencies, they built tools to help them to do their jobs better. One of these tools became highly popular, and after 1.5 years of selling this tool to customers, they hit a $1m in annual recurring revenue. This is the creation story of NeverBlink AI. Linkshttps://neverblink.ai/https://bigdataboutique.com/https://www.linkedin.com/in/itamar-syn-hershko Current Sponsors: Tiger Data Protected Harbor Render Fitnexa Perplexity Entelligence Checkout our Stacklist! https://stacks.codestory.co/ Hosted by Noah Labhart | Technical Founder & Startup Mentor. Our Sponsors:* Check out Perplexity and use my code CODESTORY for a great deal: https://www.perplexity.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Automating a bad process doesn't make it better. It makes the same problem move faster—and can leave your PI firm paying for technology it wasn't ready to use. Yuval Goren is the founder and CEO of Kobargo IT, which provides technology and AI leadership specifically for high-volume personal injury firms. After two decades in traditional IT, Goren shifted from reactive support toward treating technology as a lever for productivity, revenue, and competitive advantage—a model Kobargo IT now calls “IT and AI Leadership as a Service.” In this episode, Yuval joins Chris Dreyer to unpack what a PI firm must put in place before it deploys AI. They discuss where AI belongs in intake and lead follow-up, why undocumented workflows undermine automation, how firms should consolidate and secure their data, and why a firm's referral strategy can include cybersecurity.  You'll learn: Why process mapping and documented SOPs need to come before AI automation in a personal injury firm. Where AI-powered intake can strengthen lead follow-up without sacrificing the human connection behind client trust. What PI firms need to address when consolidating fragmented data for AI and automation. How cybersecurity and AI governance can protect sensitive client information while strengthening firm reputation. Head over to Rankings.io and see for yourself how we can help take your business to the next level.  Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

Welcome back to another episode of Data Driven! This time, hosts Andy Leonard and Frank La Vigne sit down with Jim Spignardo, Director of Cloud Strategy and AI Enablement at ProArch, to explore the transformative role of AI and data engineering in critical industries. From the challenges of bringing modern AI solutions into traditional sectors like power generation and manufacturing, to the real-world impact of tools like Copilot, this episode dives deep into automation, return on investment, and the importance of outcome-driven technology adoption.Along the way, Jim Spignardo shares hands-on stories and key lessons learned, such as how modernization happens gradually in high-stakes environments, and why ROI modeling and project management are crucial to successful innovation. Whether you're curious about Azure AI Foundry or the future of workplace automation, this is an episode packed with practical insights and tales from the cutting edge of AI implementation.LinksJim on LinkedIn -https://www.linkedin.com/in/spignardo/Watch on YouTube -https://youtu.be/XBDpsi4f0YYTime Stamps00:00 ROI Journey with Copilot04:57 Building with Azure AI Foundry08:23 Implementing AI for Power Plants12:22 Discussing 19.2K networks in power plants14:00 Ensuring data security in devices17:59 Cloud integration for IT operations23:30 Discussing RPO, RTO, and ROI modeling24:48 Switching from hobbyist to manufacturing30:15 Introducing our AI platform Vector34:34 AI Spend and ROI Dashboard36:47 Improving meeting notes integration40:48 AI in Manufacturing for Troubleshooting45:41 Managing AI Tool Adoption47:51 Tracking ROI and user intensity52:03 Consulting firms spotting product chances55:17 Increasing delivery speed with AI56:55 Automating the house routine

Elon Musk says a secretive new SpaceX foundry will let him cast his own turbine blades and get gas power online 18 months faster than anyone else — but it's a bet on a fuel source that's already triggering lawsuits and health studies everywhere his (and others') turbines have gone in. Caterpillar has spent decades putting autonomous machines to work at remote mining sites. It's now bringing that experience to AI deployment. Learn more about your ad choices. Visit podcastchoices.com/adchoices

What happens when an AI agent follows your documented process perfectly, but that process bears little resemblance to how decisions are actually made? In this episode, I speak with Bill Wilson, Executive Head of Data and AI Solutions at NTT DATA UK&I. Bill oversees AI globally for NTT DATA's public sector work, giving him a close view of how governments are using AI while trying to manage risk, accountability, public confidence, and constrained resources. Bill offers a refreshingly practical test for any proposed AI system: is it competent, and what is the worst thing that could go wrong? He describes this potential consequence as the system's "blast radius." An AI assistant helping somebody understand a grant application presents a very different level of risk from an agent making decisions that affect employment, justice, taxation, or access to public services. We also discuss why companies can make a mistake before deploying their first agent. Automating an inefficient process simply allows the organization to perform the wrong work faster. Bill argues that teams should examine complete workflows, identify where several AI capabilities could produce a measurable result, and remain prepared to redesign the process as they learn. Another major problem is tacit knowledge. Employees frequently make decisions using experience that was never written down. An agent trained solely on formal documentation may therefore understand the official process while missing how the work gets done in practice. Bill explains how targeted questions, behavioral traces, feedback, and supervised learning could capture some of that reasoning. Public sector AI provides several useful examples. Bill discusses systems that process volumes of information beyond human capacity, emergency response work in Tennessee, and case management applications that gather information before a human reviews it. In these situations, AI can reduce administrative work and waiting times while leaving consequential decisions with people. But human approval alone provides no guarantee. If employees lose direct experience of the work, they may eventually approve whatever the system recommends. Bill compares this with airline pilots maintaining manual flying skills and describes how known test cases can reveal when reviewers are becoming overly trusting. For CIOs deciding which AI pilots should reach production, the advice is equally direct: choose work with measurable returns, group related use cases where their combined effect can be seen, learn from a varied set of deployments, and avoid building something a software provider is about to include in an existing product. As AI agents gain access to external information, internal data, and operational tools, how should your organization decide what they may do alone and when a person must intervene? Listen to the conversation and share your thoughts with me.

What happens when you combine frontier AI, agentic systems, and quantum computing?In this episode of NEXT with John Koetsier, we chat with Mykola Maksymenko, co-founder and CTO of Haiqu, about how AI agents are dramatically accelerating quantum research and potentially scientific discovery as a whole.Maksymenko shares how an AI system was able to reconstruct months of his own PhD research in a fraction of the time, even identifying a bug in one of his formulas. He also discusses experiments involving genomics, molecular simulation, quantum chemistry, and condensed matter physics.The bigger question: do we really need to wait for fault-tolerant quantum computers with hundreds or thousands of logical qubits before quantum computing becomes useful?Maksymenko argues that useful quantum applications are already emerging today, particularly when AI agents help scientists discover algorithms, orchestrate workflows, and handle the complexity of working with noisy quantum hardware.We also explore the risks of increasingly capable AI systems, scientific guardrails, quantum utility versus quantum supremacy, and what happens when researchers can test ideas in a weekend that previously might have required months or years.Topics include:• Agentic AI for scientific research• Quantum utility vs. quantum supremacy• AI-assisted genomics• Quantum chemistry and molecular dynamics• Automating quantum software workflows• AI as a scientific collaborator and educator• The risks and guardrails of frontier AI• Why scientific discovery could accelerate dramatically00:00 — “I think AGI is here” 00:40 — Meet Mykola Maksymenko 01:01 — Why a quantum physicist started experimenting with genomics 02:18 — Reproducing years of research with AI and quantum computing 03:07 — The convergence of AI, agents, and quantum computers 04:26 — Are we entering the singularity? 04:45 — AI reconstructs six months of PhD research 05:28 — The risks of AI-assisted genomics and scientific discovery 06:46 — Pandora's box and the race for frontier AI 07:02 — What researchers are doing with the technology now 08:29 — Can quantum computers already do useful work? 09:21 — From quantum supremacy to quantum utility 11:06 — Molecular dynamics and quantum chemistry 12:12 — The minimum viable product of quantum computing 13:00 — Why Haiku moved deeper into agentic AI 13:30 — How much faster can AI make scientific research? 15:47 — AI as a scientist's educator and collaborator 16:51 — Running research experiments over a weekend 17:16 — What happens next for AI + quantum computing 17:38 — How AI agents operate quantum computers 18:59 — Closing thoughts

Your customer database might be worth more than you think.In this episode of Owned and Operated, John Wilson sits down with Faraday founder Alex Coleman to break down how home service companies can turn the data they already collect into better marketing, smarter dispatching, and more replacement opportunities.They cover how Wilson uncovered millions of dollars in opportunities hidden inside old customer records, why equipment data becomes increasingly valuable as a contractor scales, and how back-office automation can tackle permits, warranties, rebates, and other processes that often fall through the cracks.John and Alex also dig into AI in the trades, where automation is actually useful, why AI still needs a human in the loop, and why contractors should think twice before building their own software instead of focusing on the core business.━━━━━━━━━━━━━━In This Episode━━━━━━━━━━━━━━• How old customer data can uncover millions in replacement opportunities• Why equipment age matters for marketing and dispatch• Turning service calls into long-term marketing assets• Using existing customer databases to drive more revenue• Automating permits, warranties, rebates, and back-office work• Why rebates can become a major source of missed profit• How data becomes more valuable as a home service company scales• Where AI is overpromised in the trades━━━━━━━━━━━━━━Connect━━━━━━━━━━━━━━John Wilsonhttps://www.linkedin.com/in/johnbwilson1/Alex Coleman / Faradayhttps://www.faraday.so/https://www.linkedin.com/company/faraday-ai/Owned and Operatedhttps://www.ownedandoperated.com/━━━━━━━━━━━━━━Sponsors━━━━━━━━━━━━━━Profit & GritRunning a $3M–$15M home service business? Profit & Grit helps contractors improve cash flow, pricing, forecasting, and profitability with guidance from an operator who's built and sold a $25M service business. Book your free 20-minute strategy call: https://www.profitandgrit.comSend Us Mail!More Ways To Connect with O&OJohn's Podcast YouTube ChannelOwned and Operated Newsletter Bonus Videos From JohnLeave a ReviewJohn Wilson, CEO of Wilson CompaniesJack Carr, CEO of Rapid HVAC

In part one of our conversation with John Godlove and Piyush Agarwal, co-founders of Fusion Hive, we talked about starting with the workflow instead of the AI. Before choosing a model or building an agent, you need to understand the problem. You also need to understand the process, the data, and what you're trying to improve. In part two, we took that conversation further. We looked at what happens when you move from an AI demo into something a business actually depends on. That's when governance, data privacy, testing, guardrails, monitoring, and cost become much more important. Piyush made a comment that sums this up well. If your workflow is already broken and you throw AI at it, you're probably going to accelerate the chaos. About John Godlove and Piyush Agarwal John Godlove is co-founder of Fusion Hive and has more than a decade of experience helping organizations from startups to Fortune 500 companies build and deploy automation. His background includes work in the Salesforce ecosystem, AI, data, and technology implementation. Piyush Agarwal is co-founder of Fusion Hive and has more than a decade of experience in artificial intelligence and machine learning. His experience includes financial fraud detection, document intelligence pipelines, generative AI multi-agent architectures, and production machine-learning engineering. Together, John and Piyush founded Fusion Hive to bring enterprise AI and automation experience to small and mid-market businesses. Their approach focuses on practical workflows, measurable outcomes, governance, and production-ready AI systems. AI Has a Way of Exposing Problems You Already Had We've been talking all season about AI exposing the cracks in businesses and software development. This conversation was a great example of what we mean. Many businesses don't have perfectly documented processes. The official procedure may say one thing, while the person doing the job follows a slightly different process because they know what works. Sometimes that critical business knowledge isn't documented anywhere. It may be sitting in someone's head, buried in an email, or handled through an informal process. Humans are pretty good at filling in those blanks and making judgment calls. Software needs us to be much clearer about the rules. John explained that small and mid-market businesses don't always have mature governance structures. Regulated organizations tend to have more structure because regulations require it. Other companies may not discover these governance problems until they start looking seriously at AI. The AI project doesn't necessarily create the crack. It often shines a light on something that was already there. That can actually be a good thing. An AI project may force you to understand how your business really works. It can push you to document processes, identify ownership, and clean up your data. You may get significant value from that work before the first AI workflow ever reaches production. AI Governance Is Bigger Than AI When I asked John and Piyush about governance and protecting customer data, one thing became pretty clear. You can't treat AI governance as something that exists by itself. Your business already has systems, users, data, security policies, vendors, and access controls. The AI system has to live inside that environment. Piyush explained that Fusion Hive tries to work within the systems a customer already uses. They don't automatically introduce another platform. A business may already be heavily invested in Microsoft, Google, Azure, AWS, or another ecosystem. Those existing systems may already provide infrastructure that can support the new workflow. John added that internal IT departments and managed service providers also need to be part of the conversation. AI governance overlaps with data governance, cybersecurity, access controls, and existing policies. You can't build one without considering the others. I think that's an important way to look at this. AI may feel like an entirely new technology problem, but it's still another component in your business systems. You still need to know who can access information, where that information goes, and who owns the process when something fails. Know Where Your Data Is Going Data privacy becomes even more important once AI enters the conversation. Before connecting business information to a model, find out where that data goes and what happens to it. Does the provider retain it? Can the provider use it for model training? Does the platform provide the protections your business needs? Those questions become critical in healthcare, financial services, and other regulated environments. Piyush talked about cases where businesses may need specific configurations or agreements with providers. These protections can prevent providers from using company data for model training. He also pointed out that not every use case requires a frontier model. A smaller or open model may work for some problems. That option may also give the business more control over where and how it processes information. The exact solution depends on the business. The important thing is to ask these questions before implementation. You don't want to connect company data to an AI system and then ask where that information went. The Wrong Use Case Can Make AI Expensive Fast When I asked John and Piyush about mistakes they've seen businesses make with AI, John's answer wasn't some crazy technical failure. One of the biggest mistakes is simply choosing the wrong use case. A business leader hears that the company needs AI. Somebody gets a directive to "get AI live," and the team builds something impressive for a demo. But nobody defines what business metric the project should improve. John suggested working backward from the business outcome. Maybe you want to improve margins or reduce turnaround time. Maybe you want to shorten lead qualification or improve the lead-to-cash process. Once you know the target, you can trace that goal back to the workflows that influence it. This is where I think many AI projects can get into trouble. "We implemented AI" isn't a business result. If you don't know what you're trying to change, you may spend money without knowing whether the investment helped. You Need a Baseline Before You Can Measure ROI Of course, that creates another problem. Some businesses don't know how their existing processes perform today. They know people are busy and a workflow feels slow. But they haven't measured the time, exceptions, rework, or actual cost. John talked about measurements such as cycle time, quote turnaround, lead qualification, and lead-to-cash timing. Those numbers give you something to compare after you introduce automation. If a process took 40 hours before AI and takes 20 afterward, you have a measurable improvement. The same applies if errors drop or turnaround improves. Without a baseline, you're guessing. Six months later, you might have a cool AI system. But when somebody asks what it saved the company, the answer can't just be that everyone seems to like it. Don't Automate a Broken Process This is where Piyush made one of the strongest points of the interview. If the workflow is already broken, AI will accelerate whatever already exists. That includes the chaos. Bad data doesn't suddenly become good because an LLM reads it. Unclear ownership doesn't disappear because an agent performs the task. If nobody can tell you whether the result is correct, you're probably not ready to hand that process over to AI. Before you automate, you may need to clean the data or document the rules. You may need to fix the process or determine who owns the outcome. That work can slow down an AI project at the beginning, but I'd rather find those problems before production than after. This reminds me of what we see in testing and automation. Automating a bad test or a bad process doesn't make it better. It just lets you run the wrong thing faster and more often. AI gives us more capability, but that basic engineering principle hasn't changed. Evaluation, Guardrails, and Monitoring This was probably my favorite technical part of the conversation because it lines up with my testing and integration background. Piyush identified three areas that matter when moving an AI workflow toward production: Evaluation Guardrails Monitoring Before production, you need to evaluate the workflow against known data. Piyush described maintaining a holdout or "golden" dataset with known expected results. You can run the workflow against that data and check whether the system produces acceptable results. For developers and testers, this should sound familiar. We've been doing versions of this forever. Give the system a known input, define the expected output, run the test, and compare the results. AI changes how predictable some outputs may be. It doesn't remove the need to test them. In fact, I think AI's variability makes evaluation even more important. Put Guardrails Around What AI Can Do Testing the output is only part of the problem. Once an AI system can take actions, you need to decide what it's allowed to do. We've already seen examples of public-facing AI systems producing ridiculous results because nobody properly constrained them. Guardrails should define where the AI has authority and what requires validation. They should also define when the system needs to stop and ask for help. Some decisions may require deterministic rules, while others may need human approval. This is another place where I don't think we should look at the problem as AI versus humans. A better solution may combine AI, deterministic software, validation rules, and people. Give each part of the system the job it's actually good at. Deployment Isn't the Finish Line Piyush's third piece was monitoring. I think this will become increasingly important as more companies put AI into production. You don't test the system once, deploy it, and forget about it. You need to watch what happens after release. What questions are users asking? What does the system return? What new exceptions appear? Where does it get confused? Those production interactions can become part of the feedback loop that improves the system. That's not very different from what we've learned from building software for years. Production teaches you things your test environment never will. AI can make the behavior less predictable, which makes monitoring and ownership even more important. Don't Forget the People Using the System You can choose the right use case, design the right architecture, and test everything correctly. The implementation can still fail if nobody uses it. John talked about enablement and training as a critical part of the rollout. This becomes especially important when AI changes an existing business process. Employees need to understand what changed, how to use the new system, and why the organization made the change. You also need to deal with the obvious concern that employees may think AI is there to replace them. If that's what people believe, don't be surprised when adoption becomes difficult. We've dealt with change management in software projects forever. AI hasn't removed the human side of implementation. The Most Powerful Model Isn't Always the Right Model Our bonus conversation moved into another area that more businesses are starting to discover: running AI in production can get expensive. Most people first experience AI through ChatGPT, Claude, Copilot, or another chat interface. Production workflows can look very different. An agent might ingest emails, read PDFs, extract information, transcribe documents, and call other systems. Now imagine doing that thousands of times. John explained that businesses need to understand these cost drivers because AI expenses can vary much more than a traditional monthly SaaS bill. Piyush made another good point here. The biggest and most capable model isn't automatically the right model for every task. If you're doing a simple email summary, do you really need the most expensive frontier model available? Probably not. Route the Work to the Right Tool This brought us back to something we discussed in part one. Sometimes the right solution isn't AI at all. Piyush described task-based routing, where the system decides what capability a particular task needs. A simple request might go to a smaller model. A complex reasoning problem could go to a more capable model. A deterministic task might go directly to traditional code. This approach can improve cost, speed, and predictability. John also talked about making systems LLM agnostic. You don't want to lock an entire business process into whichever model happens to be popular today. This market moves incredibly fast, and today's best choice may not be the best choice a year from now. That's more than a technical architecture decision. If you're building something the business expects to run for years, you need to think about changes in pricing, vendors, and technology. Build for the Business, Not the AI Trend The biggest thing I took away from this second part of our conversation is that successful AI implementation looks a lot like good engineering. Understand the problem, know where your data comes from, establish ownership, and define the expected outcome. Then test the system, add controls, monitor production, and make sure the people using it understand the change. AI gives us some incredible new capabilities, but it doesn't make those fundamentals disappear. If anything, it makes the cracks more obvious when we skip them. Before you automate that next process, take a hard look at what you're actually automating. If the process is broken, the data is bad, nobody owns the outcome, and you can't measure success, AI probably isn't your first problem. Otherwise, you may not be automating your business. You may just be automating the chaos. Stay Connected: Join the Developreneur Community

The Customer Service Department Is Dead: What the Future Service-Centric Organization Looks Like Customer service cannot remain the department responsible for cleaning up problems created by the rest of the company. If an organization is serious about becoming service-centric, every department must understand how its decisions shape the customer experience—and one accountable leader must ensure the entire system works. In Episode 268 of the Customer Service Revolution Podcast, Denise Thompson and John R. DiJulius III examine what the future service-centric organization will look like as AI reshapes customer behavior, frontline roles, organizational design, and the economics of service. The future will not belong to the company with the fastest chatbot or the fewest employees. It will belong to the organization that uses technology to remove friction behind the scenes while making the experience more human in front of the customer. Customer Experience Must Be Enterprise-Wide—but Someone Still Has to Own It "Customer experience is everyone's responsibility" sounds inspiring, but it can become an excuse for having no accountability. John argues that successful organizations need a clear experience champion—someone who loses sleep over the experience and has the authority, KPIs, and executive access to challenge decisions that could hurt customers or employees. In a large enterprise, that may be a chief experience officer supported by a customer experience department. In a smaller organization, it may be a shared role assigned to an HR, training, operations, or other senior leader. The title matters less than the clarity of the mandate, the time committed to it, and the metrics tied to it. The role should also extend beyond the customer. A truly service-centric organization manages the entire experience ecosystem: customer experience, employee experience, and vendor experience. AI Should Remove Friction, Not Humanity AI is changing customer service, but using it only to reduce headcount can create expensive unintended consequences. Gartner predicts that by 2027, half of the companies that cut customer service staff because of AI will rehire people to perform similar functions under different titles. Gartner has also reported that only 20% of customer service leaders had reduced agent staffing because of AI. As AI handles routine questions, human employees inherit the escalations, emotional customers, sensitive conversations, and high-stakes decisions. That work can be more meaningful—but also far more demanding. Organizations must protect employees from empathy fatigue, make access to a human easier, and train people in the skills technology cannot replace: empathy, curiosity, listening, rapport building, judgment, and the ability to defuse an upset customer. Your Customer May Send an AI Agent Instead of Visiting Your Website The customer journey may no longer begin on a company's website, app, or contact center. Gartner found that customers were approximately three times more likely to use a third-party generative AI tool than a company-provided chatbot when trying to resolve a service issue. Among customers who already used generative AI, 58% had used it to complete a task—not merely find information—and that figure reached 74% in B2B settings. That creates a new strategic threat: the company can become invisible while an outside AI platform recommends brands, compares choices, completes purchases, and resolves problems. Technology is easy for competitors to copy. Human connection, trust, community, and a distinctive brand experience are harder to duplicate. Access to a Human Could Become the Next Competitive Advantage Pega research found that 77% of consumers believed they always or often achieved better outcomes when dealing only with a human, while two-thirds preferred human-led support. Nearly half said they did not trust businesses that used AI to handle customer service interactions completely. Despite that preference, many organizations continue to make people fight through layers of self-service before reaching an employee. John's answer is blunt: a customer should be able to reach a human when the customer wants to. Companies that preserve convenient human access—and equip those employees to deliver empathy, expertise, and judgment—may be able to turn humanity itself into a powerful brand differentiator. Personalization Without Integrity Becomes Exploitation More customer data creates more opportunities to personalize an experience, but not every technically possible use of data is ethical. The public controversy over Delta Air Lines' AI-supported pricing illustrated how quickly personalization can be perceived as surveillance pricing. Using customer knowledge to anticipate a need or make someone feel cared for is service. Using a customer's identity, vulnerability, income, or presumed willingness to pay to extract the highest possible price is exploitation. Customers should be allowed to choose and pay for clearly defined service levels; businesses should not quietly decide that a particular customer can be charged more. Integrity cannot become collateral damage in the race to personalize. The Best Model Is People-Led and Technology-Powered Walmart and Starbucks offer a more promising blueprint. Walmart has introduced AI tools designed to support approximately 1.5 million U.S. associates, including real-time translation and technology that reduced shift-planning time from 90 minutes to 30 minutes. Starbucks' Green Apron Service model combines staffing, workflow improvements, and technology to give employees more time for craft and customer connection. These organizations are not presenting technology as the experience. They are positioning it as the infrastructure that helps people deliver the experience. That is where AI creates real value: transcribing workshop notes, organizing information, handling repetitive tasks, accelerating internal processes, and giving employees more time to think, connect, and solve. Stop Measuring Speed at the Expense of Loyalty Average handle time, ticket volume, and cost per contact can reward speed while quietly damaging the relationship. Fast service that makes a customer feel dismissed is not a win. Neither is a warm interaction that fails to solve the problem. The future service-centric organization must measure both efficiency and experience. John recommends tracking earned sales growth—the percentage of business generated through repeat customers and referrals rather than purchased through advertising—along with the operational and experience measures that explain why loyalty is rising or falling. The most important question is not simply, "Was the issue resolved?" It is, "How did the customer feel after doing business with us?" What Leaders Should Build Now The future service-centric organization will: Assign one accountable experience champion with clear authority, priorities, KPIs, and executive access. Make every department responsible for understanding its internal or external customer and its effect on the end experience. Use AI for repetitive, administrative, and low-risk work while preserving human judgment for sensitive, ethical, financial, and health-related decisions. Train employees continuously in AI readiness and service aptitude skills. Give frontline employees the authority to solve problems without unnecessary permission-seeking. Protect easy access to a skilled human whenever a customer wants or needs one. Measure loyalty, repeat business, referrals, complaints, certainty, and customer outcomes—not speed alone. Refuse uses of customer data that exploit vulnerability or presumed ability to pay. Build strong customer service systems first, then layer AI on top of them. Customer service may stop being a department, but service must become the operating system of the entire company. Chapters 00:59 — Why the customer service department is dead 01:51 — Enterprise-wide ownership still needs one accountable champion 05:06 — Does every company need a chief experience officer? 07:34 — Breaking silos through cross-functional CX leadership 08:26 — Are companies using AI to improve service or cut headcount? 11:14 — The new role of the human service professional 13:30 — Preventing escalation overload and empathy fatigue 15:17 — When customers send third-party AI to handle your company 19:36 — Will access to a human become a premium service? 23:08 — Why community and brand experience are returning 26:11 — AI costs, disappearing entry-level roles, and the talent pipeline 30:14 — Walmart, Starbucks, and the people-led, tech-powered model 34:03 — Personalization, surveillance, and the integrity line 38:59 — Which traditional customer service metrics now work against CX? 42:23 — A practical blueprint for leadership, employees, technology, and measurement 48:05 — The first question every CEO should ask Key Takeaways Customer experience can be enterprise-wide without becoming leaderless; one person must still own the system and its results. AI creates the most value when it removes repetitive work and gives employees more time for judgment, empathy, and connection. Automating simple interactions can leave human agents with a relentless stream of emotionally difficult cases, increasing the risk of empathy fatigue. Third-party AI platforms may become the new front door to the customer journey, making a distinctive human brand experience even more important. Easy access to a knowledgeable human can become a powerful competitive advantage. Personalization crosses the line when customer data is used to exploit vulnerability or presumed willingness to pay. Metrics such as average handle time can produce unintended behavior when they are not balanced with loyalty, customer outcomes, repeat business, and referrals. A company needs clear service systems before it layers AI onto the customer experience. Quotes "Someone has to lose sleep at night over the experience the company is providing." — John R. DiJulius III "When answers are everywhere, questions become the scarce resource." — John R. DiJulius III "When the world gets more artificial, we need to become more human." — John R. DiJulius III "The more digital we become, the more human is the competitive advantage." — John R. DiJulius III "Integrity shouldn't be something that is outdated." — John R. DiJulius III "EX equals CX. Employee experience equals customer experience." — John R. DiJulius III "Customer service may stop being a department, but service must become the operating system of the entire company." — Denise Thompson Resources Mentioned in This Episode Gartner: Half of companies that cut customer service staff because of AI will rehire by 2027 Gartner: 85% of service leaders are expanding human-agent responsibilities Gartner: Customers are three times more likely to use third-party GenAI than company chatbots Pega: Consumers demand more from AI-powered customer service PwC: 2025 Customer Experience Survey Walmart: AI-powered tools for 1.5 million associates Starbucks: Green Apron Service and improved service performance Delta Air Lines: Response regarding AI-supported pricing Links: ROX Dashboard:  https://thedijuliusgroup.com/rox-dashboard/ The DiJulius Group Methdology: https://thedijuliusgroup.com/x-commandment-methodology/ Company Service Aptitude Test:  https://thedijuliusgroup.com/c-sat-forms/individual-c-sat/ Schedule a Complimentary Call with one of our advisors:  tdg.click/claudia Ask John!  Submit your questions for John, to be aired on future episode:  tdg.click/ask Customer Experience Executive Academy: https://thedijuliusgroup.com/project/cx-executive-academy/ Experience Revolution Membership:  https://thedijuliusgroup.com/membership/ Books:  https://thedijuliusgroup.com/shop/ Contacts:  Lindsey@thedijuliusgroup.com , Claudia@thedijuliusgroup.com If you want to learn how world-class organizations build cultures customers cannot live without, explore The Experience Revolution Membership. Inside the membership you'll gain access to livestream workshops, practical frameworks, and proven strategies used by organizations around the world. Learn more at https://thedijuliusgroup.com/membership/ Learn More If your organization is working to improve customer experience but struggling to connect it to measurable business outcomes, The DiJulius Group can help. Visit: https://thedijuliusgroup.com Listen to more episodes: https://thedijuliusgroup.com/the-customer-service-revolution-podcast/ Subscribe We talk about topics like this each week; be sure to subscribe wherever you listen to podcasts so you don't miss an episode.

#385 | Five B2B marketers share their single best-performing webinar play - with the real numbers to back it up. This Exit Five Live session breaks down the exact tactics behind a signup flow that 5x'd conversions, a post-webinar follow-up system that closed $1M in pipeline in a week, and a series format built to compound results instead of resetting with every session. You'll also get a look at running a webinar program like an in-person conference, and how to borrow an influencer's audience to solve the cold-start problem. Real plays, receipts, and step-by-step mechanics you can steal for your own program.Timestamps(00:00) - - Why Exit Five Live exists, and today's webinar bracket format (06:01) - - Turning the thank-you page into a qualifying survey to 5x conversions (13:06) - - Building a webinar series that compounds instead of resetting each time (20:01) - - The post-webinar follow-up system that closed $1M in pipeline in a week (23:14) - - Building AI agents to personalize that follow-up at scale (27:31) - - Structuring a webinar series like an in-person conference (33:09) - - Borrowing an influencer's audience to solve the cold-start problem (35:55) - - Automating content repurposing from one webinar transcript (40:09) - - Why first-party research is the real lead magnet (40:49) - - Live vote, the winning play, and closing announcements Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Optimizely - the AI platform for marketers. Build your own AI agents or pull from a directory of 50+ pre-built ones for marketing use cases. Their new Virtual Teammates can join meetings, complete tasks, support campaigns, and keep your website optimized. Learn more at optimizely.com/exitfive.Webflow - A website platform built for the agentic web, letting modern marketing teams build fully custom sites that perform in AI search with no developer needed. Learn more at webflow.com/for/exitfive.Zoom Webinars & Events – The virtual event platform built to help B2B marketers run webinars that actually drive pipeline, with branded registration pages, live engagement features, and built-in tools to repurpose sessions into clips and content. Learn more at zoom.com/exitfive.Compound Growth Marketing - A full-funnel demand gen agency helping high-growth cybersecurity and enterprise software companies show up earlier in the buying journey, combining AEO, modern paid advertising, and a dedicated go-to-market engineering team. Podcast listeners get two free media planning sessions to find out what channels are driving the best ROI. Learn more at compoundgrowthmarketing.com/exitfive. ***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Rupert from DocsAutomator demonstrates an advanced e-signing suite designed for seamless Airtable integration. Unlike traditional manual workarounds, this tool provides a legally compliant way to manage business contracts with full audit trails and seven-year document retention. The demonstration features the DocsAutomator AI agent, which can build complex legal templates through a simple chat interface. The agent identifies Airtable data structures, automatically maps fields, and generates necessary automations to handle the entire contract lifecycle. Watch the end-to-end workflow in action, from initial template generation and data mapping to sending signing requests and receiving the final, signed document back into Airtable, complete with a compliance certificate containing IP addresses and hashes. ⏱ In this cut: 02:14 — Overview of the e-signing solution and compliance 03:10 — Generating contract templates with the AI agent 07:15 — Automatic Airtable data mapping and template creation 12:15 — Managing the end-to-end signing process 17:30 — Legal audit trails and document compliance

Corporate social media is much more than publishing posts, and employee advocacy is much more than giving employees content to share. The systems, governance, enablement and strategy behind the activity are what make it work.Michelle J Raymond is joined by Chris Downey, Head of Social Media & Editorial at Elastic, to unpack what effective corporate social media and LinkedIn employee advocacy look like inside large B2B organisations. From why the tool is only 5% of an advocacy programme to the roles of Company Pages, employees, and opt-in participation, Chris shares what he's learned while building these programmes from the ground up.Key moments in this episode - 00:00 Corporate social media strategy: Pages + people02:00 What businesses misunderstand about social media teams05:00 Can one bad LinkedIn post damage your brand?07:00 Building a corporate social media programme from scratch09:00 Why social media governance matters12:00 Why employee advocacy tools aren't the strategy17:00 Why employee advocacy should be opt-in21:00 LinkedIn Company Page vs employee content25:00 Measuring the real value of employee advocacy27:00 How to keep employee advocacy momentum goingCONNECT WITH MICHELLE J RAYMONDMichelle J Raymond on LinkedInBook a free intro callB2B Growth Co newsletterConnect with Chris Downey on LinkedIn

How My Agency Went From 40 Employees to 3 (AI Updates for Agency Owners, Q3 2026) AI won't do your strategy — but it will eat every hour of admin, reporting, and manual work in your agency if you let it. In this solo episode, Jordan Ross (founder of 8 Figure Agency, the first AI consulting and development firm built for agencies) shares what's actually working in marketing agency automation right now — including the inside story of quietly converting an agency in his own portfolio from 40+ employees across the world down to a 3-person AI-native team. You'll learn: - Why systems you build today compound forever — and why the SOP-and-training playbook that ran agencies from 2019–2024 is dead - The exact build process Jordan runs with his engineer: scoping documents, hyperscoped sprints, "golden cases," and the QA loop that turns AI output into a self-sustaining service - Vibe coders vs. 10x engineers — what non-technical founders get wrong about building with AI, and why product management is quietly becoming the highest-income skill an agency owner can develop - The AI SDR that made Jordan rethink his roadmap — and the build-vs-buy question every agency owner should be asking before writing another line of code - The real result: automating admin, reporting, split testing, and data insights saved one ad-agency client 40 hours a week in a single month Chapters 00:00 — There's never been a better time to own an agency 01:56 — The hidden portfolio agency: 40+ employees to 3 03:55 — Doing work vs. building systems (why most founders are stuck on low leverage) 06:04 — Golden cases: how to QA an AI system until it runs without you 08:16 — Why I'm exiting my portfolio (again) — stress, kids, and lifestyle maxing 10:18 — Vibe coders vs. 10x engineers, and the product-manager skill founders need 12:12 — The AI SDR that shattered my brain: build it or buy it? 14:26 — Automating agency admin: the 40-hours-a-week result Links & Resources

Join the Millionaire University AI Mastermind at ⁠⁠⁠⁠⁠MillionaireUniversity.com/AI⁠⁠⁠⁠⁠ #1051 What really makes a business profitable — better technology, or simply getting your team to execute the fundamentals? In Part 2 of this two-part episode, host Brien Gearin continues his conversation with Ryan Wimpey about scaling Tip Top K9 and using AI, automation, and virtual assistants without losing sight of the numbers that actually matter. Ryan explains why he still prefers humans for emotionally driven sales calls, where AI agents could soon make sense, and how his custom software will help automate everything from scheduling to commission and sales reports. He also pulls back the curtain on the Tip Top K9 franchise model, including its $48,000 franchise fee, six-week training program, and average annual revenue of more than $600,000 for locations open a full year. Plus, Ryan shares one of his biggest lessons for new entrepreneurs: set clear expectations, coach your people to meet them, hold them accountable, and don't be afraid to make the hard personnel decisions when they don't! What we discuss with Ryan: + AI agents for customer calls + Automating repetitive franchise tasks + Building a custom CRM + Focusing on profit-driving metrics + Holding teams accountable + Tip Top K9's franchise model + $600K+ average unit revenue + Starting without dog training experience + Franchise vs. starting from scratch + Hiring and managing employees Thank you, Ryan! Check out Tip Top K9 at ⁠TipTopK9.com⁠. Follow Ryan on ⁠Facebook⁠ and ⁠YouTube⁠. Get your FREE 5 Minute Business Plan at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/Plan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/Sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Are you prepared for the busy season, or are you headed straight for burnout? In today's episode, I'm chatting with photographer and business coach Emily Woodall Gbadamosi about the 3 systems you can set up now to give yourself an easier fall season. Plus, we share what a difference these systems have made in our businesses.The Shoot It Straight Podcast is brought to you by Sabrina Gebhardt, photographer and educator. Join us each week as we discuss what it's like to be a female creative entrepreneur while balancing entrepreneurship and motherhood. If you're trying to find balance in this exciting place you're in, yet willing to talk about the hard stuff too, Shoot It Straight Podcast is here to share practical and tangible takeaways to help you shoot it straight.This episode is brought to you by More Money, Less Burnout, a 14-day business reset. Each day includes short audios plus one tiny action step to help you create more income, better boundaries, and some breathing room in your business. Grab the free reset today! Review the Show Notes: Meet Emily (1:20)What fall looks like without systems (3:04)When Emily realized she needed systems (6:27)Create a consistent booking process (11:51)What to tackle first in your booking process (18:41)How long it takes to get your booking process ready to go (22:07)Have an AI editing system (23:55)Automating your communication after the session (34:28)Why photographers avoid boundaries (40:39)Deciding on your capacity and time boundaries (47:16)Where to start now on building your systems (55:45)What busy season looks like when you have systems (57:58)Mentioned in this Episode:More Money, Less Burnout: sabrinagebhardt.com/business-resetSabrina's Imagen AI affiliate link: imagen-ai.com?ref=sabrinaSabrina's Dubsado affiliate link - save 20%: dubsado.com/?c=sgplovesdubsadoConnect with Emily:Website: woodallcreative.coInstagram: instagram.com/woodallcreativecoSystems Style Quiz: woodallcreative.co/systems-style-quizEmily's Honeybook affiliate link: share.honeybook.com/emilywoodallphotoConnect with Sabrina:Website: sabrinagebhardt.comInstagram: instagram.com/xo.sabrinagebhardtTikTok: tiktok.com/@xo.sabrinagebhardt Hosted on Acast. See acast.com/privacy for more information.

“The bottleneck isn't really having ideas; the bottleneck is execution,” Gautier Cloix, CEO of H Company, tells Bloomberg Intelligence Senior Technology Analyst Anurag Rana. “What we do is basically [make] execution infinite so we can focus on intention and ideas and strategy.” In this episode of Tech Disruptors, the pair discuss how computer-use agents can operate enterprise software through existing user interfaces, enabling companies to automate workflows without first rebuilding, migrating or integrating their technology stacks. Cloix also explains why H Company combines specialized, lower-cost models with forward-deployed engineers to target measurable ROI, turning legacy software into an asset and creating an execution layer across disconnected enterprise systems.

In this podcast episode, MRS Bulletin's Sophia Chen interviews Vivek Chawla of Sandia National Laboratories about his research group's automation of a nanoindentation machine, which probes materials locally to characterize mechanical properties. The work paves the way to incorporating an automated nanoindenter for a self-driving laboratory for materials discovery. This work was published in a recent issue of Review of Scientific Instruments.

We're diving back into the vault today to bring you a foundational episode on one of my favorite ADHD strategies: Automation. We all know the struggle—if it's interesting, we're all in. If it's boring? It's nearly impossible to start. Because so much of "adulting" is inherently repetitive, automation allows us to frontload our work so our "future selves" don't have to rely on willpower alone. Top Tips from this Rebroadcast: Frontload your effort: Save time and brainpower by making decisions once and letting tech handle the rest. Systematize first: Learn why reducing steps in a task is often more important than the gadget you use to do it. Automated Accountability: How to let your teammates know you've finished a task without having to remember to send that "done" email. Smart Home, Clear Mind: Exploring the possibilities of smart speakers and sensors to keep your household running on autopilot. Support the show on Patreon Find the show notes at HackingYourADHD.com/automation

The United States is on track to win the AI race — and hollow itself out in the process, says Gina Raimondo, former Governor of Rhode Island and US Secretary of Commerce. In this unflinching look at the threat of AI-induced economic disruption and social unrest, she offers a concrete blueprint to prepare workers for what's coming next. "AI is a 100-year technology and needs a 100-year response," she says. Is America up to the challenge? After, Modupe talks about why people shouldn't be punished for taking time to find new ways to work. Hosted on Acast. See acast.com/privacy for more information.

Can organizations trust an AI recommendation when they cannot understand the evidence, relationships, and previous decisions behind it? In this episode of Tech Talks Daily, I welcome back Jim Webber, Chief Scientist at Neo4j, to discuss the company's acquisition of GraphAware and its move from graph database provider to graph intelligence platform. GraphAware has worked with Neo4j for many years and developed Hume, an intelligence analysis platform used to connect and examine complex information. Bringing the two companies together gives Neo4j a direct role in applications serving police forces, governments, intelligence agencies, and other organizations handling connected data. Jim explains why context has become one of the biggest requirements for dependable AI. Enterprises already possess enormous volumes of data, but facts alone provide endpoints rather than the complete path leading to a decision. An agent needs to understand the knowledge available, the conversation taking place, and the record of previous decisions. It also needs to know which actions produced good outcomes and which produced poor ones. Jim compares these information layers to SimCity. Each can be viewed separately, but their greater value appears when they are combined. Knowledge, conversations, and decision traces can then help an agent understand why something happened and learn from the result. This introduces an interesting lesson from scientific research. Positive outcomes are frequently published, while failed experiments receive less attention. An AI agent needs both. Recording the breadcrumbs behind good and bad decisions provides the material required to improve its future behavior. We also discuss why large language models cannot understand every organization by themselves. Jim describes a model as a lossy compression of the internet. It can generate impressive natural language, but it does not automatically understand a company's policies, customers, history, evidence, or operating environment. Retrieval augmented generation can introduce relevant organizational information into the process. Graph RAG adds relationships between facts, helping the system understand how people, events, products, accounts, and other entities connect. According to research Jim references from the National Innovation Centre for Data, Graph RAG can improve accuracy while reducing costs by using fewer, higher-quality tokens. Explainability becomes especially important when AI supports decisions across policing, cyber defense, taxation, intelligence, banking, and government. A fluent answer may sound authoritative while containing a serious technical mistake. Jim shares an example from his own work where an agent confidently warned him about a "committed minority" inside a fault-tolerant computing protocol. The statement sounded plausible, but only a majority could commit within that protocol. Someone without Jim's technical knowledge might have accepted the recommendation and removed working code. This leads us to human oversight. Jim argues that the correct level depends on the consequences of the action. Automating a routine banking process with monitoring and safeguards may improve the customer experience. Ordering someone's arrest based solely on an agent's conclusion demands human involvement. We also consider digital sovereignty and why control over data has become a strategic concern for governments and large enterprises. Geopolitical instability, overseas technology dependencies, privacy requirements, and changing national policies are forcing leaders to ask where their data resides and whether they can retrieve or move it. Jim explains how Neo4j intends to offer organizations flexibility over where their information is stored and how it is deployed. The discussion also examines the opportunity for Neo4j and Hume to provide an alternative within a market where Palantir has held a powerful position. Looking ahead, Jim imagines intelligence analysts directing swarms of digital agents. Those agents could search data, connect evidence, identify relevant patterns, and present findings while humans retain responsibility for consequential decisions. If AI can connect information at machine speed, how do we ensure the person making the final decision can inspect the evidence and challenge the conclusion? Listen to the episode and share your thoughts with me.

Mike coaches a struggling Pilates studio owner on phone sales live: avatar building, referral strategy, and a real walk-in prospect. This pilates studio sales coaching session picks up where Part 1 left off. In Part 1, Mike walked through Jess Landar's churn, lifetime value, and pricing. In Part 2, they build her customer avatar, run a full gym sales role play on her actual phone script, and design a gym referral program built around feeding children. Halfway through filming, a real prospect walked into Jess's studio while the cameras were rolling. Mike coached her through that conversation live, in real time. It has never happened on this podcast before. The gym membership sales tips in this pilates studio sales coaching session are not Pilates-specific. The same principles for how to sell gym memberships, plant a belief before price ever comes up, and control a sales call apply to any boutique gym. This is Part 2 of a continuing series. Jess has 30 days of ads running before she and Mike reconnect for Part 3. In this episode you'll learn: — The exact way to build a customer avatar most gym owners skip — A full gym sales role play using Jess's real phone script, line by line — What happened when a real prospect walked into the studio mid recording — How to plant a belief before a prospect ever asks about price — The moves that control a sales call instead of getting steered by it — A gym referral program built around feeding children instead of a discount — The welcome gift bag move that gets new members texting back after their first class — Why customer acquisition cost matters more than cost per lead — How to turn a low priced trial into a full membership without extra ad spend — The AI assistant setup Mike uses to manage ads, CRM, and follow up — The homework and 30 day timeline before Part 3 If you have ever fumbled a walk-in prospect or lost control of a sales call, this live session shows you exactly what to do differently. Timestamps: — 0:00 Welcome back for Part 2 — 4:54 Building Jess's avatar, meet Brenda — 10:05 Finding Brenda's biggest insecurity — 12:17 The fishing analogy for hooks, lines, and offers — 17:46 How many members her studio can actually hold — 24:31 Live roleplay: selling the $39 trial over the phone — 29:41 Planting a belief before the close — 32:04 Finding out how ready a lead really is — 41:12 The VIP referral pass strategy — 45:33 Amateurs add, professionals multiply — 49:39 The double call and video text combo — 1:00:26 Why customer acquisition cost matters more than cost per lead — 1:08:39 Designing a scarier, higher converting offer — 1:16:05 What value stacking actually means — 1:19:20 Setting up an AI assistant to run the business — 1:21:29 Automating follow up with Go High Level Connect with Jess Landar: Instagram: https://www.instagram.com/thereformerlibrary 100K Plan: https://www.youtube.com/watch?v=uMPx7b3_LOA Behind Gym Doors Podcast Playlist: https://www.youtube.com/playlist?list=PLnwaMl7Us7dAkfE6idQW56EYkEEQ2E5eE Go High Level (CRM Mike uses and recommends): https://www.gohighlevel.com/?fp_ref=gsd-company11 Viktor AI Assistant (referral credit link): https://ref.viktor.com/mike-arce Mike builds Jess's entire offer and sales script live, the same way GSD Gyms helps studios everywhere sell what they already have. The plan behind how we get gyms to $100K a month is right here. Turns out a $39 trial can do a lot of talking.

Jacob Kosior has over ten years of experience in the multifamily housing industry, spanning operations, marketing, and centralization efforts across conventional, student housing, affordable housing, and build-to-rent communities. He previously served as Vice President of Centralized Services at Cardinal Group Management and has held leadership positions with BH Management, The Dinerstein Companies, and CA Ventures. In 2022, while founding a centralized services team at Cardinal Group, Jacob began testing AI tools across the housing space and co-developed a delinquency AI product for rent collection with the EliseAI team. Today he is at EliseAI, where he works with operators of all sizes to implement AI tools, manage the change required for the technology to stick, and rethink how work gets structured across a portfolio. In this episode, Jacob Kosior breaks down how multifamily operators are actually deploying AI, where to start, and what agentic AI changes about the way workflows get built. Drawing on a decade in operations before moving to the technology side, Jacob explains why leasing is the natural entry point, why speed is becoming the metric that matters more than conversion rate alone, and why owners who call their own properties tend to misjudge voice AI. He and John also work through the harder question operators face once automation is in place: what the human team should be doing instead.     Make sure to download our free guide, 7 Questions Every Passive Investor Should Ask, here.     Key Takeaways Start with leasing, where the tasks are repetitive and already documented Build AI workflows on the same follow-up cadence you train your onsite teams on Track speed alongside conversion rates, because faster cycles compound Decide where human touchpoints serve the customer, not just the org chart Redeploy the hours AI frees up into experience or revenue generating work Ask questions early, since lacking internal AI talent is the most common barrier     Topics From Operations to the Technology Side Jacob spent over a decade in multifamily operations before moving into AI Founding a centralized services team at Cardinal Group in 2022 pushed him to demo every tool available He co-developed a delinquency AI tool and a centralized call center product with EliseAI before joining the company What Agentic AI Actually Changes Generative AI answers prompts; agentic AI follows a workflow toward a defined goal Goals map to work teams already do: book the tour, collect the rent, sign the renewal Operators now control the steps the AI follows, mirroring how they train staff onsite Why Leasing Is the Place to Start Leasing represents the repetitive, high-volume work teams handle every day The old five point follow-up has become 8 to 9 touches per prospect to convert AI works the weekend leads so agents start Monday on tours instead of backlog Who EliseAI Serves The platform crossed 6 million units earlier this year, roughly 1 in 6 US apartments Clients range from top 50 operators to regional operators with a few hundred units Over a billion AI conversations give the platform context on industry edge cases Speed as the Metric Owners Miss The industry tracks conversion rates closely and speed almost not at all A 20% lead-to-tour rate achieved in two hours beats the same rate in two days An assistant manager calls one delinquent resident at a time; AI calls the building Faster cycles give owners earlier visibility into collections, renewals, and leasing velocity Rethinking the ROI Question Operators who never calculated the ROI of a leasing agent struggle to value the AI doing that work The 2025 conversation was about ROI; the 2026 conversation is about creating value Freed capacity opens the door to restructuring teams, not just cutting tasks Deciding Where Humans Step In Human Touch Automations let operators choose where staff enter the customer journey One example is a personal call two hours before a scheduled tour The test is whether the customer wants that touchpoint, not whether the team is used to it Automating administrative work finally gives teams room to deliver the service they advertise The Voice AI Misconception Owners calling their own properties judge voice AI against old press-one phone trees Consumers using AI daily are increasingly comfortable talking to it on the phone Voice AI routes callers to the right person instead of forcing them through a menu Regional dialects lifted both engagement and tour bookings in beta testing What Voice AI Replaces After-hours answering services and overflow call centers become optional Call scoring rates every leasing agent call against a rubric, replacing quarterly mystery shops Self-learning feeds those conversations back into the AI to improve automation rates    

What happens to tomorrow's leadership pipeline when employers automate the entry-level tasks through which beginners learn? In this episode of Tech Talks Daily, I speak with Gary Flowers, Chief Information Officer for Transformation and Technology Services at Year Up United, about AI fluency, human skills, economic mobility, skills-first hiring, and the future of entry-level work. Year Up United prepares young adults without bachelor's degrees for meaningful careers while helping employers reach skilled, career-ready talent. Gary says the organization has over 35,000 alumni working across companies ranging from the Fortune 1000 to the Fortune 50. Gary challenges the assumption that younger workers will automatically understand AI because they grew up with technology. Access to a chatbot does not create workplace readiness. Young adults also need training, support, ethical awareness, judgment, communication, adaptability, and experience applying tools to real business problems. He argues that AI may redefine entry-level work rather than eliminate it entirely. Candidates who understand how to work with AI may gain an advantage over those who do not, but employers must also reconsider which tasks beginners need to develop business knowledge and professional confidence. We discuss the risk of another technology divide. AI could widen economic opportunity, but unequal access to tools, training, mentorship, and workplace experience could reinforce existing inequalities. Gary believes organizations must teach workers when AI should be used, rather than limiting training to what the technology can do. Year Up United combines AI fluency with workplace and career readiness. Gary describes its 17 durable skills, six-month curriculum update cycle, close employer feedback loops, and participation as an inaugural host partner in Anthropic's Claude Corps Fellowship program. For employers, one of the hardest decisions involves balancing immediate efficiency with future capability. Automating junior work may reduce costs today while weakening the pipeline of experienced professionals and leaders required later. Gary recommends creating a culture of continuous learning, supplying employees with appropriate tools, building communities of practice, sharing successful use cases, and treating AI as a company-wide responsibility. He also distinguishes between AI as a workforce skill and AI as an organizational capability capable of changing how functions operate. Can employers capture the value of AI while preserving the career pathways that allow inexperienced workers to become tomorrow's experts and leaders? Listen to the episode and share your thoughts.

In this episode of Loaded and Rolling, host Thomas Wasson sits down with Greg Akselrod, Chief Technology Officer at Outpost. They dive deep into the often-overlooked world of yard management, exploring how modern technology, AI, and computer vision are revolutionizing traditional gate operations and check-in workflows. ⁠Follow the Loaded and Rolling Podcast⁠ ⁠Other FreightWaves Shows⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of Loaded and Rolling, host Thomas Wasson sits down with Greg Akselrod, Chief Technology Officer at Outpost. They dive deep into the often-overlooked world of yard management, exploring how modern technology, AI, and computer vision are revolutionizing traditional gate operations and check-in workflows. Follow the Loaded and Rolling Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

Watch the YouTube version of this episode HEREIn this episode of the Maximum Lawyer Podcast, Tyson sits down with Wouter IJgosse, founder of Decision Vault, to talk about one of the biggest hidden time drains inside law firms: collecting client information.Wouter shares how watching his wife manage an estate planning client with 37 beneficiaries exposed just how inefficient traditional intake can be. From information scattered across PDFs, emails, and meeting notes to endless copy-and-paste work, they break down how better workflows can save time, reduce errors, and create a smoother client experience.They also dig into where AI actually helps, where the hype falls short, why all-in-one legal software may not be the answer, and how law firms can start thinking more like product companies when building their systems.Listen in to hear how Wouter is rethinking client intake, AI, and legal tech to create better workflows for both law firms and their clients.What You'll LearnHow a client with 37 beneficiaries exposed major inefficiencies in traditional legal intake.Why integrations do not solve the problem if your firm cannot collect structured client data in the first place.Where AI can actually improve law firm workflows and where it may create more work.Why Wouter believes the “all-in-one” software platform is often the wrong goal.How better client data can reduce double entry, errors, and time spent chasing information.Why AI still needs humans to create the instructions, guardrails, and final review.Timestamps00:00 — How 37 beneficiaries led to the idea behind Decision Vault04:00 — The legal workflows lawyers accept simply because “that's how it's done”06:30 — How AI is speeding up software development and product experimentation10:45 — The problem with trying to build an all-in-one legal platform15:30 — What Decision Vault actually does for law firms and their clients21:40 — Why intake is really a client experience, data, and workflow problem23:35 — Automating client follow-up and reducing the need to chase information29:25 — What's next for Decision Vault, including family law and AI workflows32:10 — Why AI-powered intake can sometimes be slower than a traditional form34:20 — Using AI to extract information from documents and handwritten forms37:00 — Automating estate planning diagrams and reducing manual data entry39:50 — The future of AI, legal software, and human judgmentConnect with Wouter IJgosse:LinkedinFacebookEmail

The Narrative Conversion Engine: Hollywood Storytelling Mechanics, Organic Search, and Automated CRM Architecture with Drew DorenfestIn a recent episode of The Thoughtful Entrepreneur Podcast, host Josh Elledge sat down with Drew Dorenfest, the Founder of Client Magnet CRM, to examine how business leaders can apply high-stakes entertainment marketing principles to eliminate client acquisition friction. Drew, a former Hollywood trailer editor turned technology founder and growth consultant, details his strategic transition from editing blockbuster film campaigns to building automated lead-generation engines for local service enterprises and mid-market firms. This conversation delivers an intentional blueprint for business owners who want to refine their core messaging, build compounding organic search authority, deploy automated email workflows, and convert customer reviews into predictable local search dominant rankings.The Narrative Conversion Engine: Hollywood Storytelling Mechanics, Organic Search, and Automated CRM ArchitectureThe fundamental error made by many scaling small businesses and professional service providers is relying on volatile social media algorithms or disruptive cold outreach to drive sustainable revenue growth. Drew Dorenfest explains that earning customer attention in a crowded digital marketplace requires applying cinematic storytelling mechanics—condensing complex value propositions into clear, high-impact hooks that communicate brand differentiation within the first five seconds of a site visit. Rather than interrupting passive users on social feeds, business leaders must focus their capital on capturing high-intent search traffic via search engine optimization. Investing in long-term technical and on-page SEO ensures that when prospective clients are actively searching for solutions, the business appears prominently, building compounding inbound lead momentum that outlasts temporary paid ad campaigns.To maximize customer lifetime value and eliminate pipeline volatility, scaling enterprises must pair organic search acquisition with automated customer relationship management workflows. Relying solely on one-off purchases or sporadic manual follow-ups creates unnecessary customer churn, whereas establishing automated email flows—such as post-purchase onboarding sequences, re-engagement triggers, and targeted review collection prompts—creates a direct, owned communication channel. Segmenting email audiences by past purchase history and maintaining a consistent monthly sending cadence allows companies to drive predictable repeat revenue without paying additional customer acquisition costs. Automating these backend touchpoints keeps the brand top-of-mind, transforming one-time buyers into loyal, high-value brand advocates.Furthermore, dominating local market search results demands that business owners treat client review collection as a standardized, automated operational process rather than an afterthought. Search algorithms heavily favor local companies that demonstrate high review volume, recent feedback, and proactive business responses, making customer testimonials a primary driver for local search engine visibility. Integrating direct review links into automated CRM follow-up sequences ensures a steady stream of verified social proof that builds immediate buyer trust. When clear cinematic messaging, disciplined search engine optimization, automated email marketing, and systematic review collection are synthesized into a single growth framework, an organization eliminates client acquisition bottlenecks, protects its profit margins, and predictably expands enterprise valuation.About Drew DorenfestDrew Dorenfest is the Founder of Client Magnet CRM, an expert digital marketing consultant, and a former Hollywood trailer editor with over two decades of creative storytelling experience. Having edited marketing campaigns for major film franchises and television series—including Spider-Man, Star Trek, and Power—Drew now specializes in helping small business owners and regional service providers build automated client acquisition engines. He is a recognized growth strategist focused on helping business leaders leverage search engine optimization, email marketing, and automated CRM technology to scale predictable revenue.About Client Magnet CRMClient Magnet CRM is an elite marketing technology advisory and automated CRM platform engineered to help small-to-mid-sized businesses optimize lead acquisition and client retention. The company specializes in delivering custom search engine optimization playbooks, automated email marketing flows, local review generation systems, and brand messaging refinement. Through tailored growth blueprints and marketing automation tools, Client Magnet CRM enables business owners to eliminate lead generation debt, build direct audience relationships, and maximize market equity.Links Mentioned in This EpisodeClient Magnet CRM Official Website: clientmagnetcrm.comDrew Dorenfest on LinkedIn: linkedin.com/in/drew-dorenfestKey Episode HighlightsThe Five-Second Value Proposition: Applying Hollywood trailer editing techniques to condense brand messaging and hook website visitors immediately.Organic Search vs. Social Media Fatigue: Leveraging high-intent search engine optimization to attract inbound leads actively looking for solutions.Automated CRM Email Workflows: Building predictable repeat revenue through segmented post-purchase email sequences and automated re-engagement flows.Systematic Local Review Collection: Utilizing automated CRM prompts to generate Google reviews that boost local SEO rankings and build social proof.Compounding Inbound Lead Infrastructure: Shifting marketing capital away from temporary ad spend to construct permanent digital assets that drive ongoing traffic.ConclusionThe conversation with Drew Dorenfest underscores that achieving sustainable business growth requires a deliberate synthesis of narrative clarity, organic search visibility, and automated customer nurture systems. By standardizing internal marketing workflows, building owned communication channels, and automating review acquisition, business leaders can transform an unpredictable lead pipeline into a highly structured, self-sustaining growth engine.More from The Thoughtful Entrepreneur

Rahul Sood has run the application security business at Harness for almost a year. He describes application security as one of the core pillars of the company, part of a vision of building a DevSecOps platform. A year ago Harness publicly announced that security accounted for a quarter of its revenue. Sood says the figure is now considerably higher. The company did not start there. Founder Jyoti Bansal thought about Harness for a decade before founding it, Sood says, after seeing the problem inside one of the largest banks. Harness launched as a DevOps platform, then merged with an API security company Bansal had also funded. The result is a platform that treats security as part of the developer workflow rather than a bolt-on, and covers it from code all the way to runtime. What changes when security lives inside the developer workflow? Developers stop leaving their own tools to chase findings. Harness aggregates results across scanners, deduplicates them, and puts remediation, assignment, and exemption requests in one place. Security teams get the other half of the picture through a policy engine that shows which policies fire on every build, which ones break a build, and where a developer asked for an exception, with a full audit trail behind it. Where is the bottleneck now that AI writes the code? It moved downstream. Sood says nearly every development team is generating more code with AI, while the volume actually reaching users has not risen at the same rate. By his estimate coding is 20 to 30 percent of the software development life cycle, and the remaining 70 to 80 percent happens after the code is written. That includes agents. Harness has extended the platform to support the Agent DLC, with native capabilities for AI evaluation and prompt testing alongside security coverage for agents from code to runtime. Sood points to two differences. The span from code to runtime covers agents while they are built and while they run, and skill scanning and prompt scanning were added to the same scanner already looking for code vulnerabilities rather than shipped as another tool to buy. The operational payoff shows up in release cadence. Sood describes a tier one US bank that maintained 150 separate policies and convened a team to confirm each one had been met before it could launch its banking app. Automating that review removed the meeting, and the bank now runs multiple launches within weeks. With 80 to 90 percent of software now assembled from third-party packages, libraries, and open source components, the same policy engine can block any build that pulls in a package which is end of life or malicious. This is a Brand Briefing. A Brand Briefing is an on-location conversation recorded on site at Black Hat USA 2026, putting a spotlight on the guest and their company and pairing it with the editorial reach of ITSPmagazine. Learn more: https://www.studioc60.com/performance/#briefing GUEST Rahul Sood, General Manager, Application Security at Harness On LinkedIn: https://www.linkedin.com/in/rssoods/ RESOURCES Black Hat USA 2026 event coverage from ITSPmagazine: https://www.itspmagazine.com/black-hat-usa-2026-cybersecurity-event-coverage-in-las-vegas Harness: https://www.harness.io/ Harness customer case studies: https://www.harness.io/customers Securing the Agent DLC, by Rahul Sood: https://www.harness.io/blog/securing-the-agent-dlc Harness AI Security: https://www.harness.io/products/ai-security Harness Application Security Testing: https://www.harness.io/products/application-security-testing Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight ▶︎ Get your own Brand Briefing at an upcoming event: https://www.studioc60.com/buy-brand-briefings KEYWORDS rahul sood, harness, sean martin, brand briefing, brand story, brand marketing, marketing podcast, black hat usa 2026, application security, devsecops, agent dlc, ai security, api security, policy engine, software supply chain, open source risk, prompt scanning, skill scanning, code to runtime, developer experience, release velocity Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Join the Millionaire University AI Mastermind at ⁠⁠⁠MillionaireUniversity.com/AI #1032 What if the biggest thing holding your business back isn't your strategy — but the fear, uncertainty, and busywork keeping you from taking action? In Part 1 of this two-part episode, Justin and Tara Williams open up the very first Millionaire University coaching call and work through real challenges alongside members of the MU community. You'll hear practical advice on getting your first customers, pushing through fear and uncertainty, knowing when to stick with an idea, and building momentum through imperfect action. Plus, their son Brogan shares how he's using AI tools like Claude Code to automate repetitive tasks, build his own software, and turn an appliance resale business into a largely hands-off operation — all without a technical background! What we discuss on the call: + Overcoming fear and taking action + Getting your first customers + Building momentum through experimentation + Finding your entrepreneurial “genius zone” + Turning AI into a business tool + Automating repetitive tasks with AI + Building software without coding experience + Creating a more hands-off business + Knowing when to push through or pivot + The compounding effect of consistent action Thank you for listening! Check out Millionaire University at ⁠⁠⁠MillionaireUniversity.com⁠⁠⁠. Get your FREE 5 Minute Business Plan at ⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/Plan⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/Sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

"How do you use AI every day?"