Podcasts about Pocs

Dave Goyal, Founder and CEO of Think AI Corporation, is driven to Turn Disabilities into Unique Abilities by using technology to empower disabled entrepreneurs and help businesses unlock the value of their data. After contracting polio as an infant, Dave transformed physical limitations and early adversity into a passion for solving business problems, building companies, and giving back to society. Through Think AI, he helps manufacturing and healthcare leaders use data and AI to generate real-time insights, improve productivity, reduce costs, and create new opportunities for growth. In this conversation, Dave introduces The 3G AI Augmentation Framework—Gap: Where are we losing time, quality, ability, or capacity? Grow: Apply AI to augment people and improve that work. Glow: Institutionalize the solution so humans and AI collaborate effectively. Dave also shares how his private second brain and AI executive agents save him up to 80 hours per month, why human control and security must remain central to AI adoption, and how authority, trust, people, customers, and culture drive business growth. He also discusses his book, Real-Time Business Intelligence Mastery, and his vision for creating a venture studio for disabled entrepreneurs. — Turn Disabilities into Unique Abilities with Dave Goyal  Good day. Steve Preda here with The Management Blueprint. And today my guest is Dave Goyal, the founder and CEO of Think AI Corporation, which helps CTOs and CIOs in manufacturing and healthcare turn siloed data into real-time insights and automation, creating reduced downtime, increased efficiency, and going from weeks to days in project launches. Dave, welcome to the show.  Thank you for having me, Steve.  Well, I’m really curious to learn about you and your company, Think AI Corporation, but first I’d like to ask you about your personal why and how you are manifesting it in your business.  So thank you again, Steve. I’m really excited to be on your show. I’m in the data and AI business, and really tech innovation, for the last 30 years. In this particular company, Think AI, I have a partner, Manish Bhardia, and we both have been working very actively with Microsoft partners, the Microsoft ecosystem, and implementing data and AI solutions for midsize companies and manufacturing companies. You went on why, which is amazing.  My why: I’m a disabled entrepreneur. I have this hunger for building businesses. I’ve built nine businesses. We can talk about it later. And five of them were miserable failures in my books. Not all of them were that miserable, as I say. But five of them were failures, and I learned a lot from them. And I’m really motivated now to expand it further, to give back to small businesses.  We’ve been working with midsize and enterprise clients, but to midsize companies, and then motivate—I have a 15-year-old kid—so motivate young people and also small businesses to make use of the power of their own data and use and consume AI on a day-to-day basis. That’s my why.  Wow. So, to learn the power of their own data and use AI, why is this important to you?  The main reason is I am passionate about technology. Everybody is good at something. I am really good at solving business problems using technology. Being a disabled entrepreneur, I did not have a lot of luxury initially, even walking. Eventually, I started using braces, started going to different countries. So the passion became really the source of energy and motivation, and that passion is now going to a level where I want to motivate people like me who are disabled entrepreneurs and want to go into this kind of business. So my real passion is technology and giving back to society using technology, to sum it up.  Wow. So you mentioned this disabled entrepreneur. I’ve never heard this term. I mean, you talk about minority entrepreneurs, women entrepreneurs, you know, veteran entrepreneurs, and actually the government recognizes these categories, but I never heard about disabled entrepreneurs. So would you mind sharing a little bit about what happened to you and how you got into this entrepreneurship?  Sure, yeah. And that’s really good, by the way. I don’t see anybody else using that term but me, so probably I should keep it with me as a copyright term. Just joking on it. But having said that, every disability brings some kind of ability. That’s why sometimes they call it differently abled. When you have these abilities, you don’t know the source or the channels to use them. So, for example, blind people, they may have a lot of great listening power. That’s why they are into music most of the time.  Sometimes they have amazing reasoning and critical-thinking power, but they don’t know how to channel it, so they fight on a day-to-day basis with these issues. Bringing it back to me, I have polio. When I was six months old, I got hit by the polio virus. Initially, for a few years, I had to just lie down on the bed, had a lot of physical therapy. Then I was able to get up and sit, at least. Then my father was carrying me to school, and I could see the kids were going out and playing. I got beaten up because of that, too, because kids don’t understand. No fault of theirs that I’m not throwing the ball at them and they are playing.  And so that brought a lot of negativity in me. Eventually, my grandfather and my father helped me get over that, and I started channeling that into building businesses. So I started teaching music. I learned music through some of my friends. I started teaching music during my college days and started making money. And I had a blind friend, and he needed money because he was abandoned by his parents, so I had to help him out. I started making some money. I was doing well with my family, so I could just pay everything back to him. So that seed got planted there, and I didn’t know what to do back then, right?  Still a 14-, 15-year-old kid or a teenager, in this case. So I started getting into that mindset of, how about I build businesses for me and then start helping out the community? I’m still not there yet. I’m going towards helping that community. But I want to identify disability in three ways, not just physical. So those three are physical, but the bigger one is mental. A lot of people are really mentally blocked, and you see people, you know, “Oh, I can’t change anything in my life.” People die by suic*de. Kids get into depression. This is a form of disability, by all means.  I don’t think education, parents, and community are doing so much about that other than having a cliché thing that, “I was a victim of depression, so I’m doing that,” just to show off. But really, to help out the community in a methodical manner, that doesn’t exist. Second, physical disability, like I said, given by God sometimes, like war veterans and others, then you feel really limited. So what to do with that? And I come into that category, so I know that really well. Third is financial disability. So a lot of financial disability is in the mind, too. I’ve heard a phrase called, “You don’t die by hunger; you really die by indigestion.”  So you would find ways and means to make money even if you’re a completely disabled person. So I don’t think finance is an issue in general. So these three areas, to me, are the real disability areas. I’m obviously only working on one today, which is physical disability: how to identify the potential of people who can create something different within that limitation and then make a change in the world. So that’s the motivation. That’s my Life 2.0, where I’m moving now. Love it. Love it. So how did you have time to build nine businesses?  I started it in 1993, ’94, I believe, or ’95, I think. And they’re one at a time. Today I have about three. I sold one. And yeah, I did not have time. One of the big challenges when I built these three in the last six, seven, eight years, the biggest challenge I faced is I do not have time for working with customers, which I love to do—talking and listening to their business problems, solving those problems. I end up doing a lot of operational work.  Post-2020, and it’s a very blunt thing to say, people got lazy. They want to change jobs, make more money, do moonlighting, do multiple things, but not work hard like we did back in the days. And that kind of pushed all of us small businesses to do a lot more management of resources, especially human resources, in a distributed environment. I have teams in India, the Philippines, Canada. So that became a bigger challenge. But having said that, AI came as a savior. In the last 18 months, AI has changed quite a lot.  And if you don’t go into a debate of whether AI is good or bad, or you’re a skeptic or an enthusiast, AI can really help you if you really put together how it can help you. It should not replace you, but it should give you an additional helping arm. In my business, I started deploying C-suite. So I still have a VP of operations. My business partner is into sales. But then I started filling in other functions, like a fractional CFO, as an example. My fractional CFO is monitoring my top line and bottom line. I call him Felix. I have to give names to AI agents.  So Felix is actually looking on a weekly basis at what invoices are billed, if we have vendors or employees, what we need to pay, where our expenses are going, what’s the monthly or maybe six-month cash flow run. Are we within limits? Do we have borderline cash availability so that we can survive? So it started to do a lot of things. But not only that, because we are feeding our own data, our own mind, I have built my own second brain. It started to read off of that and started giving me insights that a human would not give me. And even if I hire a fractional CFO, he will only hear what I have to say, look into some of my books, and then give me some blanket suggestions.  Here, this is really tailor-made to our problems, our situation, and it worked phenomenally well. So I’m building that as a product now. It’s not done yet. Then I also deployed my own CMO called Sasha, and she started to look into my marketing angles, my branding, my voice, my identity. I love writing, but now AI can help me—not just create a blanket AI post or something, but really read how I write, what I write. So I create ideas. It helps me research, does a factual check on it. So I give 100 words. It can take those ideas and then expand into newsletter articles or a big campaign.  I can start building different case studies for our customers, proof of concept, building podcasts such as these. So this started freeing up—I only talked about two executives, and I have seven of them—but they started to help me free up my time. And believe it or not, I am getting about 40 to 60 hours, and in good months, about 80 hours per month. So 30 to 50% of my regular time is freed up. So that time is now going into this movement that I’m thinking about, which is disabled entrepreneurship.  Wow, that is impressive. So tell me a little bit about this. This is a podcast of frameworks. So do you have a framework for maybe launching an AI agent like that?  I definitely do, and I want you to expand on it. But in terms of what I have, I look into three things. Where is the gap in terms of human? Where I see either performance issues, quality issues, or availability and capacity issues. So what are those things which don’t hit my security side of things, don’t interact with my customer, and still help me in my operations? That’s the gap we look into. How can we use and fill that gap to grow what we need to work on? And then last, so I use three Gs with my last name, Goyal, right? So Gap to Grow to Glow.  So now how can we use this in our business to glow and create an environment where even humans can interact with this AI persona? So we are always big on human-in-the-loop or human-in-control with any AI solution. So it always starts with the gap. Where is the gap? Where is it taking time from one of the human sides of our team?  I like that. I like that you isolated those things which are less risky to develop, because I think a lot of people are held back by this idea that it’s a black box, you don’t know what you’re getting into, you don’t know what you don’t know, and it’s risky, and then they don’t do anything.  But you actually isolated that customer interaction is a risk you don’t want to live with right now, and security is another one you don’t want to, which, I mean, it’s obvious. But if those are not hurt, then really what is the risk you’re running? So I like it. So how do you fire up an AI agent like that?  So in terms of technology, I’m using a few things. I’m using Claude Code, the full Claude environment. So we build it off of that. Back in my days, I worked as a white-hat hacker, so the security angle we mentioned, I’m always so worried about hacking and security. So I have a completely isolated environment at my home on a Mac Mini, a really powerful Mac Mini, and that cannot go out on the internet and do things. And nobody can inject anything.  But then I still need to feed information to it, so I have another machine where the only job of that machine is to provide information to this system. So I have my own second brain mapped into Obsidian, which is a note-taking application, but it’s really organized. So I first fed all my knowledge. I’ve recorded lots and lots of audios and documents, and it has learned. So I built that system first, like Dave’s second brain. And my second brain has learned everything about me. Nobody can see it but me. That’s my initial basis, right?  After that, I built a working memory for my agents, for my company, and I’m doing it for one company at a time. Think AI is not completely live on the system, but Data & AI Studio is, which is a solo entrepreneurship business that I have. It is learning everything about that business as we speak. Even the transcripts from these podcasts and other places go into it, and it learns from it. There will be some insights which it will find, so it retains them. So Claude Code, Mac Mini, Obsidian—these are the basis. And then I’ve deployed my own personal models, like DeepSeek, and that is sitting locally on that machine.  So the model is local. The downside is it’s not getting updated, so I only update it when I feel that it’s right. Not risk, it’s really the downside. There’s no risk in it. So you’re not on the latest and greatest, but you don’t have to be on the latest and greatest all the time. So Claude Code is on the latest and greatest, but when we deploy, it may not apply certain features that Claude Code is making available. And that’s fine. That’s the risk I’m taking. That’s the trade-off I’m taking. And the system is working great for the last six months.  In the last 18 months, even though I started AI about 28, 30 years ago, the last 18 months is when I learned the new-age AI, and the last six months is when I started building this in an iterative manner. And it is pretty stable now. It can do a lot of things like I mentioned. So your agents are running on your Mac Mini off the grid?  Yes.  And then you’re feeding information with another computer to it to essentially give them the raw material from which they can build stuff, right?  Right. A good example there, if I may expand: we use QuickBooks in our accounting system. It cannot read QuickBooks directly, but we, being a Microsoft partner, understand technology. I can write a job which can push data into the Mac Mini. It doesn’t read off of it. My Mac Mini, which has the agent, doesn’t know where that data has come from. It has the data, so it’s already synthesized.  It cannot communicate with others. So that’s the calculated risk we take, right? Getting the data from one angle, one way, and then it’s synthesizing and analyzing data and getting insights out of it. So that’s the balance of systems that I have.  I love it. That’s very clever. And what is your main business anyway? Because you talk about three businesses right now. What is your core business? What is your flagship business?  The flagship business is Think AI. It’s a consulting organization, a three-time Inc. 5000 winner in terms of growth. We have our own team, but then we also use a lot of vendors which are qualified by us throughout the world. And we are Microsoft Advanced Specialization partners. What that means is we are in the top 2% of the worldwide partners within the Microsoft ecosystem, which is about 500,000 partners. And we work mainly with midsize manufacturers, and sometimes healthcare if they are okay and open to AI, and if not, data. So we go in there, look into whether they have a data and AI strategy.  If they do, we work on their initiatives. If they have the initiatives. If not, we create the initiatives for them by doing some POCs and whatnot. And once we get engaged, we do deliverables like consulting services. But it’s not like typical consulting services where you place a resource. It’s really a value-based delivery model where we try to understand two business imperatives. One is what can help them make more revenue. And if we cannot find that, what can help them be more productive and have cost-cutting in one way or the other.  So these are the two main business imperatives we work on. When and if we align with that, then we give them a roadmap, a phase-wise approach, which they can do with us or with somebody else, and then we keep delivering on it. So that’s the whole model.  So what drives growth in the Think AI business?  I mean, finding more customers, to say the least. And that becomes difficult because today everything is becoming a commodity. So one good learning, by the way, I need to share with the audience here. When you’re a small business, you think your brand is the value that you have. It’s the founders who are the brand. So it’s Manish and me. Manish, my business partner, is really big in productivity, project management, and that kind of thing. And I’m really good at building solutions using tech. And together we have about 55 years of experience.  And then our key team members are ex-Microsoft or MVPs, Microsoft Most Valuable Professionals. So we hire a really strong key team. And the team below, we can either fill with our members, hire our own members, or go to the vendors also, and we tell it to our clients also. So our delivery model is we are the ones who are delivering. The guarantee is taken by Manish and Dave, not by Think AI. We have gotten into that situation. We are about 95% successful, so there’s a 5% failure. And the failure is either because we have the wrong team member, the communication between the client and us was not clear, the scope was not clear, the definition of value and done was not clear, and we have learned from it.  So our business model is towards that, and that brings us growth because we work with a number of partners. Manish is part of a lot of Microsoft channel partner networks. We provide complementary services to those partners. So one channel is we work with a lot of partners because the trust is there. Authority and trust are the two factors we have understood which establish your business, and it’s the founders’ authority and trust, not the company. Company will build on its own. So we started building our own authority and trust, and that gets us growth. It’s not at the level we’d like, but we are happy.  You are happy. Okay. So what is your vision? What would you like to make out of this?  So we have an exit plan, at least on Think AI. And like I told you, Manish has his own. That is up to him. For me, I want to create this venture studio for disabled entrepreneurs, get the funds from here, and then harvest, go across the world. So three hobbies I have.  One is travel. Second is reading, writing stuff, books. And third is music. And entrepreneurship comes in this whole surroundings, in this whole ring, so it’s the foundation of it. So we’re going to build this disabled entrepreneurship venture studio with a little bit of funds from our exit, and hope to grow there and hope to retire or die with that thinking.  Love it. Love it. It’s fascinating. So you have a book that is on your LinkedIn page, Real-Time Business Intelligence Mastery. So tell me about this book. Why did you write it, and what’s it about?  Sure. So we went into a coaching program. Up until 2022, we were arrogant enough to say, “Oh yeah, we can do everything on our own.” And then slowly we realized we need help, and we started taking help. We went to a couple of coaches in India where they were coaching us either on how to manage operations and operational excellence, and then another coach who’s like a life/building-your-brand marketing coach, and he inspired us to write a book.  Now, I’ve been writing in my own native Hindi language, songs and compositions, but writing a book was a dream, and I thought it’s a big undertaking. But with their little bit of motivation and help, not in writing, but in the angle of what a book can bring. So I have a lot of experience working in midsize manufacturing organizations, working with CTOs and CIOs, and business intelligence is delayed. So it’s either a one-day delay or a week delay or a month delay, and it’s more reactive in nature.  So the book was more about how you can build a real-time business intelligence culture so that you can get the insights from your data, make actionable insights, take actions on it, and grow your business for those two imperatives I talked about, which is grow your revenue or increase your productivity and decrease your cost.  So are you writing about some of the things that you talked about? Leveraging AI, building AI agents?  It has more about—so I wrote it in 2022, I believe. It has a lot more detail about. AI was not as popular, right? I mean, I did write about AI in it, but it was more about building a data culture than AI. It does talk briefly about AI because real-time is going very closely with AI. That’s the enabler for AI insights or data insights through AI. So it does talk a little bit about AI, but it talks more about tech leaders like CIOs and CTOs. What do they need to do? How do they need to build a culture around harvesting data, bring the data, build the team, where to take it? So it has those details.  Okay. That’s fascinating. Who is this book for? Is it for founders? Is it for C-level executives? Who is the target?  Like I said, it’s for tech leaders, CIOs, and CTOs of midsize organizations.  Okay. That’s awesome. And these are the people that are your target customers as well at Think AI?  Yes. That’s our true ideal client profile too, and that’s whom we have worked with all our lives. So they’re close friends, target audience, and customers. Future customers and current customers.  All in one. All in one. That’s so nice when you write a book to your friends. That’s a very cool concept. So let me ask you this, Dave. If you had a magic wand, you’ve done a lot of things in your business, you built nine businesses. You learned from some of the failures that you had, which is part of entrepreneurship, and now you created AI agents, and then you have a second brain, and you’re leveraging all that technology. So if you had a magic wand and you could fix one thing in your business in the next 12 months, what would that be?  I wish I had more senior leadership. Any business works with delegation. We have a couple who are really amazing, and they wear a lot of hats. But growth depends on three things, right? Being in front of the right customer, having the right team, and having the right product. Our product is people, unfortunately and fortunately. Customers, we are very happy and excited, and they trust us. We know how to get to them. We know how to create value for them. We are very satisfied. Everybody would say, “I need more customers,” and we would do that too.  But I think more important is what product you are offering. So then people are what we are offering, and we are competing against big ones like Accenture and Avanade and Cognizant of the world in our business, the tech consulting business. But then we are not competing against cost; we are competing against value. So how do you create value? You find a valuable customer. They understand our language. The next level is, where is the product, which is the people? And that management becomes quite difficult. And harvesting and getting the right people in place is a job by itself.  So kudos to those large companies if they’re harvesting one, although that’s debatable because when we go to the client, they complain a lot about their resources as well. So harvesting the right product and the right team is the key. And how do you do that? If you have the right leaders on top. Two partners alone cannot do that. So building more leaders underneath is the key. We are able to build a few, and I wish we could build a lot more. So when you have the right core team, your growth comes in, is my belief system. It could be different for everyone else.  No, I think it’s a very deep insight, and very few people actually talk about this idea that the purpose of a business, especially in today’s AI age, is to build leaders. That’s your purpose, because people will take care of everything. They’re going to run your AI agents. They’re going to manifest your vision. But you can’t have just AI agents in a company, right?  Because then the mental load is so much on the leader, and then the single-person dependency becomes critical. So is this what you mean by this? Where do you come from with this idea of harvesting people and leaders in the business?  Absolutely. You said it well. Building leaders doesn’t just apply to an organization, whether small, medium, or big, but even to countries. If you don’t have the right leaders in place, it’s going to bite you back. And all cultures, some of the top management consultants will teach you to go into succession planning. That is what they really mean by that.  It’s not succession planning by, okay, replace a CEO with a CEO. It’s the mindset. Apple is a great example of it. Steve Jobs hired Tim Cook from Compaq, from that world, and he had that vision. Obviously, he had a mission, but he had the vision—who to take, where to take, and what they would do. And that legacy continues even today. So Apple didn’t change a single bit in their model. And people would argue and debate, and that’s fine.  But when I see it from my eye, he built a great leader. When he did that, the company stayed the same, right? So it’s not about what products Apple is creating today, whether it’s iPhone or iPad or Apple Vision Pro or some of the other things that they are doing, but it’s really that leader. Same thing went with Google, or Microsoft, Satya Nadella. And you see the right leaders were built by these founders. And by any means, we are not that big, and hopefully we can get to some place which is pretty good in our books.  But finding and building the right people, it gives you a lot of satisfaction, happiness, bliss, if you can give it back to somebody who’s capable enough. And I’m always in hunt of the right people, building the right team in place.  It’s so interesting you mention Apple because when Isaacson came out with the Steve Jobs biography, he said, basically, I think it’s in the preamble of the book, that Steve Jobs wanted people to remember him not for the products that he created, but the company. So his biggest contribution was creating a company. And I didn’t get what he meant by it.  But if you witness the last—since he died 13 years ago—the last 13 years, this company has gone from strength to strength. Ninety percent of its market capitalization has been created since he died, right? So it keeps growing and keeps going from strength to strength, and that is the culture and the people that he built. This is the company he built. So it’s quite an amazing idea.  I was about to comment on company. Something came back to me or reminded me that companies generally build on three pillars: customer, people, and culture. And if you don’t have the right balance of it—so, the right people, but if you don’t have the right culture, they’re going to run away. If you don’t have the right customers, you should have the ability to say no to certain types of customers too. Like Apple never targeted small, cheap products.  And when I say cheap, meaning which doesn’t have the right quality in place, not about the cost. It’s always cost versus quality. So they have really struck the right balance in those three angles, and I think that’s the right way to do it. Some are able to do it, some are able to push through to do it, and some are not. But that’s where I think the focus needs to be if you are a founder. Find that right balance of people, client or customer, and culture.  Yeah.  And that is your core values too.  Yeah. I agree with you. That’s wonderful. If you are listening to this conversation with Dave Goyal, and you would like to learn more about him and what he does and Think AI Corporation, where should our listeners go to learn more?  Thank you for this opportunity, first of all. And people can find me on LinkedIn by my name, Dave Goyal. I’m very active there. I recently started a YouTube channel with the name Dave Goyal, so you can find me on YouTube. And mainly on LinkedIn, I have a newsletter on AI, and I’m pretty passionate about what’s happening in AI.  So I even publish AI news this week, but with a different angle, a builder’s angle in mind. And last but not least, you can connect with me through LinkedIn for a 15-, 30-minute call. No angle there. I will just come and help you if you really want to do something with AI. I can listen to your challenges or your fear of missing out, if that’s the case, and tell you if AI is the right fit for you or not, and what you can do on your own also. And if you need our help, we are happy to.  That’s fantastic. So take Dave up on his offer, which I think is very generous. And obviously, Dave, you know what you’re talking about. You built a second brain. You’re running AI agents. Your C-suite is chock-full of AI agents, which is very impressive. I’d love to learn more about this myself.  So if you’re curious about that, make sure you book a call with Dave or check out his stuff. Where is your newsletter? Is it a Substack? Where can people find your newsletter?  It’s on LinkedIn. It’s called Data & AI Demystified in my profile.  Okay. So that’s easy. So we can go to Dave’s LinkedIn profile. And if you enjoyed this conversation, make sure you subscribe and follow us on Apple Podcasts and YouTube. Give us a review because every week I bring in a couple of exciting entrepreneurs like Dave who share their favorite frameworks with you. So Dave, thanks for coming, and thank you for listening. Important Links: Dave's LinkedIn Dave's website

Elena Burger is joined by a16z's Andy McCall and Joe Schmidt to break down two very different ways AI startups can go to market: the lighthouse and the landgrab. Should founders win a handful of marquee customers whose credibility unlocks an entire industry, or move quickly across a broad market where the ROI already speaks for itself? Drawing on Joe's Lighthouse or Landgrab framework and Andy's experience building sales organizations at Samsara and Meraki, they explore how founders can determine which strategy fits their market, when social proof matters more than math, and why the current rush to adopt AI has created a rare window for startups to sell big software again. They also get tactical on POCs, pricing and ACV, hiring early sales teams, moving from mid-market to enterprise, and why founders shouldn't spend too much time perfecting their GTM strategy before talking to customers. As Andy puts it: spend 1% of your time on strategy and 99% executing.   Resources: Read Joe Schmidt's "Lighthouse or Landgrab": https://a16z.com/lighthouse-or-landgrab-how-to-pick-your-ai-sales-strategy/ Follow Andy McCall on LinkedIn: https://www.linkedin.com/in/amccall/ Follow Joe Schmidt on X: https://x.com/joeschmidtiv Follow Elena Burger on X: https://x.com/VirtualElena Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of HFS Unfiltered, Saurabh Gupta, President of HFS Research, is joined by Anuj Krishna, Cofounder and President - Technology & Growth of MathCo, to discuss one of the least understood reasons enterprise AI stalls: the absence of context. The pattern is everywhere. Enterprises have sky-high AI ambitions, yet pilots multiply and almost none reach production, what Saurabh calls “death by a thousand POCs.” In research HFS and MathCo ran together, roughly two-thirds of enterprises admitted they don't know where their knowledge lives; it's trapped in people's heads, undocumented processes, and scattered data. Anuj's argument: organizations obsess over models when the real advantage lies in context, who they are, how they work, and the knowledge behind their decisions. The conversation goes deep on what context actually is, why it can't be static, and who should own it, including the shift toward a federated model with “context stewards” by function. Anuj also lays out how MathCo differs from the platform owners and SIs racing to be “context kings”: rent the tech ecosystem, but own the intelligence, because you can't own your intelligence unless you own your knowledge, and an organization's context is its personality. Saurabh closes with a sharp thesis: models and agents will become a utility, and neither will differentiate you, but context is your identity, and owning it is the only sustainable advantage going forward. Read the associated Take 5 Report, titled "Fund the context layer and close the 2.5x AI ambition gap," here: https://www.hfsresearch.com/research/fund-layer-ai-ambition-gap/ #EnterpriseAI #ContextEngineering #AgenticAI #OwnYourIntelligence #MathCo #HFSResearch #AIStrategy #DataGovernance #DigitalTransformation #FutureOfWork

Este conteúdo é um trecho do nosso episódio: “#279 O que ainda merece uma Spike?”.Nele, Renan Azalim, Tech Manager na dti digital, e Alan Luigi, Líder Técnico na dti digital, discutem por que Spike, POCs e estudos técnicos continuam relevantes mesmo com a IA acelerando o desenvolvimento de software. Eles apontam que o critério deixou de ser apenas a viabilidade técnica e passou a considerar fatores como impacto arquitetural, escolha de ferramentas e contexto do negócio. Ficou curioso? Então, dê o play!Assuntos abordados:Spike;Escolha de ferramentas;Arquitetura de software;Decisão técnica;IA no desenvolvimento.Links importantes:NewsletterDúvidas? Nos mande pelo LinkedinContato:  osagilistas@dtidigital.com.brOs Agilistas é uma iniciativa da dti digital, uma empresa WPP #inteligenciaartificial

When vulnerability disclosures shrink the wild exploit window down to less than 24 hours (or even minutes), traditional manual patching and WAF rule creation simply cannot keep up.In this episode, Ashish sits down with Ammar Alim (Product Security Engineering Lead at Adobe) to break down how to build an automated, agentic WAF pipeline. Ammar shares how his team manages the scale and complexity of seven commercial and open-source WAFs (including AWS WAF, Cloudflare, Akamai, Azure WAF, Wallarm, and ModSecurity) by leveraging AI agents.Discover how to construct an agentic harness, orchestrate deep research agents to gather exploit POCs, and utilize multi-model architectures (e.g., Anthropic for rule generation and OpenAI as an LLM judge) to eliminate false positives and safely deploy virtual patchesGuest Socials -⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Ammar's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction & The Currency of Speed in Security(02:00) Ammar Alim's Background: From Data Centers to Product Security at Adobe(05:00) The Multi-Vendor WAF Nightmare: Managing Scale Across 7 Products(08:30) Understanding False Positives vs. False Negatives in WAF Management(12:30) Why

Se criar um protótipo leva minutos, ainda faz sentido gastar horas discutindo antes de escrever código? Neste episódio, recebemos Renan Azalim, Tech Manager, e Alan Luigi, Líder Técnico, ambos da dti digital, para discutir como a IA está mudando práticas tradicionais da engenharia de software, como Spikes, POCs e sabatinas arquiteturais. A conversa ajuda a entender em quais cenários elas continuam gerando valor e quando experimentar passou a ser mais eficiente do que debater. Dê o play e ouça agora!Assuntos abordados:Spike;POC;Sabatina arquitetural;Agentes de IA;Decisão técnica;Arquitetura;Experimentação;Gestão de risco.Links importantes:Vagas disponíveisNewsletterDúvidas? Nos mande pelo LinkedinContato:  entrechaves@dtidigital.com.brO Entre Chaves é uma iniciativa da dti digital, uma empresa WPP #inteligenciaartificial

In this episode of Industry Matters, Respiratory Edition, host Boone Lockard, VP of HME, Respiratory, & Wellness with VGM & Associates, sits down with Steve Palmer, General Manager of Kinney Home Care Equipment and Supplies, to discuss their journey transitioning from a traditional oxygen tank model to portable oxygen concentrators (POCs). Steve shares the operational challenges that drove the decision — from emergency tank deliveries to rising costs — and how Kinney used VGM's ROI calculator to build confidence in the financial case. He also talks about the critical role their respiratory clinicians played in gaining referral source buy-in, the efficiency gains they've seen in delivery routes, and what led them to choose Rhythm and the Rhythm Resolve as their POC partner. If you're a DMEPOS provider weighing the tank-to-POC transition, Steve's real-world experience and practical advice make this a must-listen.Watch on YouTube: https://youtu.be/YfGe1M0Ql6s

Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode, he breaks down why most enterprise AI programs stall — and the playbook the winners are using instead.Huge thanks to PwC for supporting this episode!

Landing subcontracts with prime contractors like Boeing, Lockheed Martin, and L3Harris starts long before you ever pick up the phone, and most small businesses skip the steps that actually get them noticed. In this clip, a govcon entrepreneur breaks down the exact sequence he used to move from cold outreach to a signed, ongoing contract with a major defense prime. If you've been stuck trying to get a capability briefing to turn into real work, this is the roadmap. How to register on a prime contractor's vendor or partner portal before ever reaching out to their small business office Why leading with technical jargon in a capability briefing gets you ignored, and what small business POCs actually need to hear instead How to use a specific contract ID and government POC name to open doors that generic capability statements never will Why treating subcontracting as a "side door" instead of a back door builds long-term pipeline, even outside government work The exact process for escalating from a small business contact to the technical lead who controls the actual project   EPISODE CHAPTERS: 0:00 - Introduction and Mindy sponsor message 0:48 - Why calling a prime contractor cold fails 1:17 - Do your research before contacting Boeing or Lockheed 1:29 - Register as a vendor on the prime's portal 2:12 - Attend a capability briefing with the small business office 2:51 - Avoid technical jargon small business contacts cannot use 3:39 - Give small business POCs clear direction, not confusion 4:25 - Use a specific contract ID to open the conversation 5:20 - How referencing a real contract leads to the right department 6:26 - Applying this side door strategy to construction subcontracts 7:02 - Why subcontracting is a side door, not a back door 7:55 - Comparing pricing between GovIQ and DeltekGovWin Mindy gives you the federal opportunities, agency signals, recompete intel, and pursuit briefs that tell you not just what contracts exist, but which ones to chase and how to win them. Sign up for free Daily Alerts and get opportunities delivered to your inbox before the day starts.

Enterprise AI is easy to demonstrate. The real test begins when a promising POC meets production costs, security requirements, data movement, latency, and internal adoption.Shimon Ben-David, CTO at WEKA, joins Amir to discuss the gap between experimenting with generative AI and operating it at scale. They explore how classical AI differs from generative AI, why production exposes problems that demos hide, and how companies with limited AI maturity can start building useful internal capability.Practical Takeaways• A successful POC proves that an outcome is possible. It does not prove that the system will be affordable, secure, reliable, or fast at scale.• Enterprise AI adoption reaches across infrastructure, engineering, data, security, and business teams. It cannot be owned by one group in isolation.• Adding more GPUs will not fix slow data access, poor utilization, weak pipelines, or an experience users do not want to use.• External support can help, but the person or firm involved needs to stay through implementation and production, not stop at recommendations.• Companies that are behind should begin with proven use cases, build internal experience, and quickly stop experiments that fail to show value.Key Moments00:00 Why moving enterprise AI into production remains difficult01:55 The difference between classical AI and generative AI adoption07:05 How companies can use AI without having a formal AI strategy11:35 Why successful POCs often struggle when they reach production17:35 Competitive pressure, AI FOMO, and the need to calculate real ROI22:00 Why AI adoption requires cross organizational change33:10 Where a company with limited AI maturity should beginOne Line That Stuck“The promise is there. It is possible. You just need to do it properly.”Subscribe to The Tech Trek for more conversations about how technical teams are building, operating, and adapting around AI, data, product, platform, and engineering execution.

In a week where:IOC lifts suspension on Russia, paving way for them to compete at LA 2028 Olympics.Bonnie Tyler, 80s pop legend, dies aged 75.The US-Iran ceasefire collapses.Democratic Congressman Ro Khanna is kidnapped by Israeli settlers during West Bank visit.Actor Sam Neill dies aged 78.In Health: (10:05) We know that Whiteness is a constant in everyone's lives, but from cradle to grave?! Literally affecting the health of POCs from beginning to end?! (Article By Tobi Thomas) In Society: (25:50) South Africa has gone through a lot as a country and its native Black population haven't been given what they were promised when Apartheid ended. But how have they resorted to xenophobia towards fellow Africans? (Article By Nesrine Malik)In Tech: (37:35) We know very well that smartphones have done a lot of damage to our lives. But one less-touted element of that is our increasing lack of tactility, something that we humans love on a foundational level. (Article By Sara Herschander)In Culture: (56:25) As Gaza continues to get bombed daily, in the West Bank, Palestinian archivists are hurriedly accounting for their rapidly deteriorating history with a digital archive that's built to last. (Article By Tamara Davison)Thank you for listening! If you want to contribute to the show, whether it be sending me questions or voicing your opinion in any way, peep the contact links below and I'll respond accordingly. Let me know "What's Good?"Rate & ReviewE-Mail: the5thelelmentpub@gmail.comTwitter & IG: @The5thElementUKWebsite: https://the5thelement.co.ukPhotography: https://www.crt.photographyIntro Music - "Too Much" By VanillaInterlude - "Charismatic" By NappyHighChillHop MusicOther Podcasts Under The 5EPN:Diggin' In The Digits5EPN RadioBlack Women Watch...In Search of SauceThe Beauty Of Independence

Curtis Dery, executive vice president at Xerox IT Solutions Canada Curtis Dery, executive vice president at Xerox IT Solutions Canada (doing business as Powerland), has been living the HPE GreenLake story since before most Canadian partners knew what as-a-service infrastructure meant. At HPE Discover 2026, he joined In The Channel to talk about what this week’s announcements look like from the practitioner’s desk. Dery’s team won HPE’s Canada GreenLake Partner of the Year in 2022 and has kept the streak going, but he’s clear that the barrier to adoption was never the technology. “Customers are facing constraints financially,” he says, citing tariffs and geopolitical pressure. That’s why he sees the 90/9 financing offer and 150% credit line expansion as genuine deal-closing tools. “It helps open more doors and close deals even sooner.” He also sees the channel-only expansion of Private Cloud and Zerto as a deliberate strategy his team was ready for, thanks to deep ties with HPE’s advisory councils. The real differentiator, he says, is operationalizing customer processes so they can move from 20-30 projects a year to 50-70. Where Dery gets animated is AI. He calls the current moment “the most exciting time in any of our careers” and describes AI as a “digital goldmine.” His team runs internal hackathons to build reps with large language models, work that has already helped Powerland close four of the largest infrastructure deals in the world – all out of Winnipeg. But he’s also blunt about tokenomics: “The burn is real.” On sovereignty, Dery points to the Anthropic government oversight incident as validation for private AI. “If I’m a customer and I’m all in on that model, what would happen?” He sees HPE’s network optimization and Private Cloud AI stack as the hedge. Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last 16 years. I’m Robert Dutt, editor at ChannelBuzz.ca and your host for the show. We’ve been on a bit of an unscheduled hiatus, but we’re back. We’re going to get back into the swing of things right now, and we’re going to start that off by finishing our coverage of this year’s HPE Discover 2026. Today’s guest is Curtis Dery, executive vice president at Xerox IT Solutions Canada, which most of the channel still knows as Powerland. Curtis is based in Winnipeg. His team covers the country. He’s been living the HPE GreenLake story since before most Canadian partners knew what as-a-service infrastructure meant. His team won HPE Canada’s GreenLake Partner of the Year back in 2022 and has kept that streak alive. They were the first partner to sell a GreenLake deal in Canada, the first to sell VM Essentials, and the first to sell a cyber vault. But Curtis isn’t just a sales exec. He’s genuinely hands-on with emerging technology, running internal AI hackathons with his team, and has a perspective on the announcements from Discover that come from actually closing the deals, not just reading the press releases. He joined me on site at Discover to talk about what the new financing tools, the channel-only expansion, and the AI story mean for partners on the ground. Let’s get right into it. My chat with Curtis Dery. Robert Dutt: Curtis, thanks for taking the time. I appreciate it. Curtis Dery: Absolutely. Thanks for having me. Robert Dutt: Before we get into this week, I have to acknowledge – just having been in this industry a while, you’ve got Xerox and what is formerly HP at a conference here. Slightly unexpected combination on the surface. Most people’s mental model of Xerox is still copiers, but you were running Powerland as one of the leading HPE infrastructure providers in Canada long before that. What does the Xerox relationship mean in practice for the IT business? Has it changed how you go to market with HPE, or does Powerland essentially operate in its own lane? Curtis Dery: You know what, that’s a great question. The way the market’s changing, the industry is changing, businesses are needing to change. That was the reason why Xerox looked at acquiring us – to help go through the realignment and the changes that they’re making as a business. Obviously, from a print perspective, looking at the industry challenges it was going through through COVID and post-COVID and just the market shift around that, having a focus around infrastructure and technology and driving those outcomes with our customers helped them amplify the customer base that they have across North America. Robert Dutt: You were doing GreenLake before a lot of Canadian partners knew what it was. You won the Canadian GreenLake Partner of the Year back in ’22, closing deals in the as-a-service model when it was still a pretty hard sell to customers used to buying it outright. Now HPE’s on stage talking about 90/9 financing and offering 150% expansion of credit lines. For someone who’s been engineering these deals since the beginning, what do these tools mean specifically? Do they change what’s possible for you, or are you already doing – you already have your system set up and ready to go? Curtis Dery: Yeah, I mean, being fortunate to be a little bit on the front edge of GreenLake, we’re fortunate to be Partner of the Year four years in a row, and from a North American perspective, Partner of the Year to make it five. What that created was just validation that how we’re going to market and how we’re executing it is a little bit more uniquely than others, and how we’re prepared to understand the customer, the outcomes that they want, and wrap that around operationalizing it through a GreenLake model. Having flexibility in some of these announcements helps with the challenges that we’re all entering – some of it unknown, some of it shortages, all these changes, and then you wrap that around obviously the disruption of AI. So having these flexibilities of what they want to do around credit is definitely needed because customers are facing constraints financially – just with the cost of, from a geopolitical impact perspective, tariffs, all these things are real. So HP coming to the table with new offerings helps open more doors and close deals even sooner than expected sometimes. So we’re always looking at making sure, yes, we have a foundational core that we can execute on with a rinse and repeat with a proven track record, but always making sure we’re aligned with the changes that they’re making and making sure we’re enhancing our offering with them, a walk and step together. Robert Dutt: It feels like they’re acknowledging that one of the barriers to GreenLake adoption isn’t the technology, the concept, or anything like that. It’s the customer’s budget cycles. And it sounds like you’re saying that’s the right diagnosis from what you’re seeing in the Western Canada market. Curtis Dery: Yeah, and I think also people sometimes think it has to be OpEx. There’s a balance that you can still capitalize GreenLake as well too, and then do a term top-up depending on the utilization they have, but also operationalizing it from a financial [perspective]. So that flexibility is still there. I just think sometimes that message isn’t out there at the street level. So that’s where our value comes in as a partner, right? To understand where that noise and friction is and remove the friction. Robert Dutt: Three products went to channel-only at Partner Growth Summit this week: Private Cloud, PC 3000, PC 1000, and Zerto. Last year it was VM Essentials. It seems like it’s clearly a deliberate expansion of that strategy. From where you sit with that infrastructure-heavy book of business, is channel-only a meaningful strategic signal for you, or is it about filling the gaps for customers who need DR and private cloud but haven’t had a clean vehicle to buy it through you? Curtis Dery: That’s a great question. I think, fortunate to have a strategic partnership with HP, we sold the first cyber vault in the country. And so same with VME, we sold the first in the country, and same with GreenLake. So there’s a theme there, right? Being so strategically aligned with them from executive level down to technical level, I’m on their advisory council from a GreenLake perspective. My pre-sales engineer is the ambassador on the GreenLake program. Because of that exposure, we get line of sight a little bit earlier. So then we’re already preparing on how we’re going to market to augment some of these announcements that they’re doing, and then wrapping around our own little secret sauce to that to be able to expedite the sales and making sure that we’re taking down the logos together. Robert Dutt: What is that secret sauce, in whatever depth you wish to share in this forum? Curtis Dery: Well, I think sometimes getting down to the nitty-gritty of what GreenLake really does, and that’s operationalizing the customer’s process to be able to allow them to be more agile within their own business. So instead of going to a traditional market and doing a traditional way of getting quotes, going to an RFP process, all those things take time, money, and energy. And when you do that, you then don’t have time to focus on the business to drive the outcomes you can do. Now, with our customers from a GreenLake perspective, that agility of being able to streamline that process – we have our customers that are able to go from 20 to 30 projects a year to now 50 to 70 projects a year. So then they get to see the benefits of how fast we can make their business move, get the outcomes that they want so they can start to accelerate further. Robert Dutt: You heard the partner branded services announcement this week. Curious what you thought of that and how it kind of maps with what you do in terms of, are you already running that services-led model, or are you looking for opportunities to have HP back you up but still go under your brand? Just curious how it hit. Curtis Dery: For us, it definitely hit. But yes, we also do it as well that way. But again, it’s the right tool at the right place at the right time. And sometimes we may need them, they may need us, or it’s an augmentation of both. And that’s the beauty that I love about HP is the investment to the channel, always staying aligned at the street level and making sure it’s very predictable on how you can make bets jointly with them. So it’s a flexibility thing. Robert Dutt: A hundred percent. Curious what’s driving the HP business for you right now. What’s kind of hitting, what are customers talking to you about, what’s driving it forward? I have to imagine AI is part of that. Curtis Dery: Yeah. And I think AI was a little bit of paralysis in the market, right? People were frozen of like, “What do I do? Where do I start? What type of technology? Do I go to a public LLM?” Or you hear this word, “sovereign,” and what does that mean? And I think the perfect storm is brewing. But the beauty is that HP has made the right investments, right? Acquisitions to now truly be ready for what the market is going to be hitting with right now. Which is, you look at the announcement of what happened with Anthropic last week. Government oversight, they said shut down that model. Well, if I’m a customer and I’m all in on that model, what would happen? And so this created that validation of why private sovereign AI with HP wrapping around customers’ data and giving them real hardened AI outcomes within their environment, and then choosing if they need to go into the public LLM. And so getting ready for that market condition is what’s going to drive success and velocity with HP in the market. Robert Dutt: Along with the idea of tokenomics, that idea of AI projects getting stuck in, and maybe hand-in-hand with tokenomics in fact, that idea of AI projects getting stalled out in the implementation or the proof-of-concept phase and not getting to full implementation is a theme that we’re hearing from HP and from just about any vendor who’s playing in the space this year. It seems to be one of the big catches. I’m curious how you’re seeing that reflected in your customer base, if they’re kind of getting to a point of doing POCs and then starting to discover, “Well, wait a second, this could get real expensive, real fast.” Curtis Dery: I’m 1000% [there]. Right? The burn is real. We have customers that knew that they have to start getting their battle scars and learning from the AI and understanding how does it work, how do we integrate it, how do we make sure there’s no hallucinating, how do we trust it, how do we do all these different things? My analogy I like to use is, at the end of the day, we all need refrigerators. But a lot of people probably don’t remember who invented the refrigerator. They just know they needed one, right? That’s the same with the public LLMs, right? You need the refrigerator when you need to go into it. What we do with our customers is show them how to take a Coke can and put it in the fridge and bring it out of the fridge. And so we help them navigate it so then tokenomics is not exposed as much, right? They can manage their cost, manage their environment, and choose where they want to put their data. I think the tokenomics is definitely a real thing and I think we’re going to find out significantly what that means in the next 120 days. Why? Because these companies are going IPO and you now know where the math is mathing, right? And so that’s going to show us a lot on what the true tokenomics looks like. Robert Dutt: You touched a little bit on the importance of sovereignty in customer discussions, but can you tell me a little bit more about how that’s showing up in terms of what customers are asking about and how you see that trending and evolving as an interest and a care about, both on the geopolitical front and, as you say, on an issue like Anthropic suddenly having to pull access to the latest model? Curtis Dery: Yeah, a thousand percent. I think when you hear the word sovereign, I always ask people, “What does that mean to you?” Because when you look at the World Economic Forum, for example, in February, what did they announce as the next pandemic, the cyber pandemic? Why? You’ve got scale of agents running everywhere. People don’t know what is a good agent or a bad agent, right? And if you look at the internet bandwidth since December till now, it’s increased over 12x of volume. Do you know how much traffic is now coming down? So now people are going to need to get prepared about how do you control your network, your data, your access, and sovereign that so that you’re secure so that if a bad day occurs, you don’t have the public exposure. And that’s why you hear from Antonio Neri and the focus around the network. How do you optimize that network? How do you secure that traffic and have the access to where you need to go and ensure that it can handle the scale? So that’s why this storm is brewing in front of all of us right now. Robert Dutt: Looking at your background, it’s clear that you’re not just selling infrastructure. You’re genuinely interested in AI and emerging technology. And it seems like you like to get pretty hands-on. As you’re at an event like this and you’re hearing the announcements and seeing what’s coming and what they’re talking about, what are you really excited to get your hands on and play with, and beyond that to actually get in front of your customers either now or down the road as it becomes more concrete? Curtis Dery: You know, I tell people this is probably the most exciting time in any of our careers because it’s the first time in any of our careers that it’s a level playing field, where it’s up to you to grab the baton of AI and understand how do you use it, apply it, and get the outcomes and innovation that you want to do with people. The tagline I like to use with my team internally is, we’re not underpinned by anyone anymore. We have the opportunity to dream, to build, and execute, and we can use AI technology to do that. And so I call it the digital goldmine. We get to go inside these LLMs and mine what we want out of that and be able to take advantage of what we can do with our customers. And that’s the one thing I enjoy the most is understanding, okay, what tools can I use, whether it’s from PCAI and apply our own private AI strategy around that. I’ve worked with a lot of advisory around a lot of the latest LLMs that are out there, but also some of these private ones like [Mistral AI] and understanding how it’s a puppet master to the public AI and how to optimize white space within a customer’s environment to show them where they have inefficiencies, profitability, when they can take the market in a different way. And that’s what AI does – allow customers to be agile, at edge, on time, and be able to really disrupt if they choose to. And I think it sounds a lot daunting for a lot of people, right, to understand how do I get proactive now with AI and not get disrupted by it, because you don’t know if you can wake up and all of a sudden your competitor is something that you didn’t expect. And so I think being able to just dive in and learn. A lot of people say, “Well, I don’t know much about AI.” None of us do. This is all the latest technology. So I tell people to speak to it, learn from it, and just start understanding how it works. So then at the end of the day, you can now augment it because it ain’t going away. If you think about from a generational perspective, we have kids that are going to be born in AI. They don’t even understand what that means. So it’s exciting times. And I tell people embrace it, because like I said, it’s the first time in history that nobody’s really walking in a room saying, “I got 10 years in AI.” Everyone’s like, “Hey, I’ve been working with it for six months. Cool.” Just like all of us. It’s how many people are putting in the reps with it. Robert Dutt: What are you pulling out of that goldmine so far at Powerland? What are you doing in terms of both – how’s AI changing both what you’re doing customer-facing, and internally your own operations and how you think about AI within the org? Curtis Dery: Yeah, absolutely. So I mean, we were fortunate being ahead of it from an AI perspective and understanding our domain strengths, using AI to be better prepared for our customers and think through strategies with them. And with that, we were able to build out blueprints where we were fortunate to close out four of the largest deals in the world with four different vendors out of a city called Winnipeg. And a lot of people came to me and said, “Curt, I don’t get it. We’re not doing this in New York, Toronto. You were doing this in Winnipeg. How are you doing this?” And I’m like, using AI to get better prepared to understand how do we simulate an environment to say, “This is the customer. What can we do to drive out these types of outcomes? And what does this look like from a strategy?” We get the blueprint and now we go and see the customer and go, “Does this make sense?” And they go, “Yes. Well, let’s go execute that with AI.” And so that’s the advantage that we get to do. And then from an internal perspective, I love having our own internal roundtable hackathons. What’s something we want to do? Throw it on the whiteboard. Everybody has their AI account and go, “Okay, how would you approach that?” So then our team is learning how to put those reps in to say, “Well, I would approach it this way.” And it’s a cool exercise to see how everyone thinks differently. And that’s the beauty about AI. We’re all going to prompt it differently. We’re all going to work with it differently and then take those unified approach of everyone’s pieces, put it together and go, “Okay, now we solve the puzzle together.” So I really enjoy the ability to be able to scale so rapidly with it. It’s an exciting time. I feel like we’re built for this era. Robert Dutt: And I’d imagine a lot of those ideas that are coming out in the internal hackathons are eventually going to find their way into what you’re doing with customers as well. So that’s a nice plus. Curtis Dery: Yeah, absolutely. Robert Dutt: As you point out, you’re in Winnipeg, presence across Western Canada. I’ve talked to a couple of other Canadian partners this week, and I’m getting this consistent theme that Canadian customers right now are in their moment – between sovereignty, between AI infrastructure refresh, between really starting to get AI in play rather than playing with AI. Does that map with what you’re hearing from your customers in the prairies and the West? And where do you see HPE fitting into that story for the balance of the year and beyond? Curtis Dery: Yeah, I think we touched on it lightly, right? The changes that happened with the government oversight last week, I think opened people’s eyes on what their approach is to public LLMs. And then also understanding costs, constraints, all these things that have been hitting our markets and hitting customers’ budgets and challenges. It’s a difficult time to be a CIO right now. When you’re sitting there and you have to protect them from a cybersecurity perspective, you have to have a future of understanding where AI fits into this, and never mind constraints around cost and all that stuff. It’s a tough time to be an executive for a business right now and understand how you can be profitable, scale all these things while you’re facing all these challenges in the market. So being prepared in Canada of how we’re going to our customers is understanding how to package what HP has done effectively well on the overall strategy around GreenLake and saying, “How do we now enter the customer and say, ‘You can now do on-demand AI in your environment predictably, cost-effectively, compliance and govern, and now you can choose how you want to scale that rapidly?'” I think finally, we’re starting to see that curve get around the corner where customers are jumping into wanting to do it this way. It’s just such a learning dynamic exercise right now, right? Because at first it was ChatGPT and then it was Grok and then it was Claude and it just kept going and going. People are not talking about the disruption that happened out of China too with their LLMs. So if you look at DeepSeek, Kimi and all these models, they’re doing exactly what Claude and these others can do at 75% cheaper. So when people start to realize, “Well, I can run that SDK natively inside my environment way cheaper than going to a public API Claude license,” people are going to look at that and go, “Oh, what makes sense now? Because the math ain’t math.” Robert Dutt: That theme is coming up in a lot of different places, isn’t it? Last one for me, whether it’s something we’ve already covered off or something else, what’s the one thing that’s really caught your attention here at Discover this week, the thing that you’re going to take back to the team and on Friday or Monday or whenever you’re first in there saying, “By the way, this is what I heard. This is what we got to get ready for.” Curtis Dery: Well, a few things. One is truly being prepared on the foundation of the network and understanding what does that mean to have an optimized AI network both internally and externally for the customer. I think there’s a high, high value in that. I learned that on the journey with cloud. Everybody wanted to go to cloud. Love the destination. Nobody talked about the highway to the cloud. Nobody talked about the cloud tax of egress coming out of there. So there’s a lot of lessons and best practices that came from the cloud journey that we can now reapply to the AI journey. So focusing on that is huge. And then understanding the intelligence layer and understanding [NVIDIA] Morpheus is an extremely powerful tool and understanding how does that fit into the entire reference architectural stack with PCAI and understanding how do we build on top of that. And that’s some of our secret sauce of what we’re doing, being able to do our own private SDK on top of PCAI so customers can truly control their own AI platform. And so that’s the focus that we’re going to do. And we’re super excited to get velocity going into Q4 with HP so that in 2027, I expect a big year. Robert Dutt: All right. Well, good luck on bringing that back to the team next week and good luck on that big year. And thanks again for taking the time on what I’m sure has been a very busy week. Curtis Dery: Absolutely. And I welcome the time and being able to share this conversation with you. So we look forward to doing it again. Robert Dutt: There you have it. Curtis Dery from Xerox IT Solutions Canada. I’d like to thank Curtis for his time. If you’re finding value in these interviews, I’d appreciate if you’d follow or subscribe to the show. You can find the podcast on Apple Podcasts, Spotify, YouTube, and most major podcast directories. Ratings and reviews are always welcome. A few things that stood out for me from this conversation. One is Curtis’s framing that the real barrier to GreenLake adoption has never been the technology, it’s the customer’s budget cycle. The 90/9 financing and expanded credit lines aren’t abstract partner program benefits. They’re deal-closing tools for partners who are already in the room with constrained CIOs. Another is his digital goldmine metaphor for AI. The idea that for the first time in our careers, the playing field is level and what matters is who’s putting in the reps. But he’s also refreshingly blunt about the burn on tokenomics and the need for partners to help customers manage costs as AI moves from proof-of-concept to production. I appreciated his point about sovereignty not being theoretical anymore. The Anthropic incident gave customers a concrete reason to ask hard questions about public LLM dependence. Finally, it’s worth noting that the company is closing some of the largest infrastructure deals in the world out of Winnipeg. The Canadian channel is not a Toronto-only story, and this is a reminder of that. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

Across Southeast Asia, generative AI pilots are stalling—not from a lack of model power, but from broken retrieval. Agentic RAG bridges this gap: autonomous agents that verify facts, enforce governance, and execute end-to-end workflows. For CIOs in 2026, this turns fragile experiments into auditable, scalable profit centres. With Gartner warning that 60% of AI projects will be abandoned due to poor data and weak controls, agentic RAG is no longer optional—it is the only practical path from pilot to production. In markets like Singapore, where data residency and compliance are non-negotiable, retrieval intelligence is now the bedrock of ROI.In this PodChats for FutureCIO, Ed Keisling, Chief AI Officer, Progress Software, discusses how CIOs and heads of AI across Southeast Asia, can turn AI pilots and POCs into profit-generating initiatives for enterprises in 2026.What is RAG?Given that most regional AI pilots never scale, what specific architectural weaknesses does agentic RAG fix that traditional RAG or fine-tuning cannot?In markets with fragmented data landscapes—legacy systems, multilingual content, and disparate cloud storage—how does agentic RAG ensure consistent, high-quality retrieval at enterprise scale?What out-of-the-box governance and audit trails does agentic RAG provide to satisfy both local data residency laws (e.g., Singapore's PDPA) and board-level risk controls?For CIOs managing lean teams, how does agentic RAG reduce the operational burden of maintaining retrieval pipelines, monitoring hallucinations, and orchestrating multi-step agent workflows? How can agentic RAG help move beyond isolated use cases (e.g., customer support) toward fully autonomous, end-to-end processes spanning finance, supply chain, and compliance?As agents become more autonomous by 2027, what retrieval strategies will prevent cascading errors or unauthorised actions, and what should CIOs implement today to stay safe?(original 3) How should CIOs in Singapore and across Southeast Asia measure the ROI of retrieval intelligence compared to simply upgrading large language models?For regional enterprises without custom AI stacks, what vendor or open-source scaffolding for agentic RAG offers the fastest path from pilot to profit while preserving data sovereignty?What organisational, data, and leadership shifts must CIOs prioritise over the next 12–18 months to ensure agentic RAG transitions from a technical capability into a sustained source of competitive advantage?

Today, I'm talking to Rana Gujral, CEO of Behavioral Signals, which provides AI that interprets human behavioral cues in speech to help route call center conversations more effectively, improve customer service performance, and detect voice-based fraud. Their moat is a decade of voice data tied to real business outcomes, not the model itself, as Rana explains.   During our conversation, Rana shares his practical framework for making the value of their AI obvious to the various humans in the loop that the product needs to “touch,” and he argues that a one size [UI] doesn't fit all. In Rana's product, they discovered that customer service reps need ambient assistance, supervisors need aggregate patterns, compliance teams need audit trails, and executives need outcome metrics tied to business results.   He also explains why having measurable ROI isn't enough. Early renewals for Behavioral Signals suffered because the people signing the checks couldn't actually see the product's impact. Rana's solution? “Ship the meter” alongside the intelligence. If your AI works quietly in the background, you still need reporting UIs that clearly communicate the product's value.   For founders struggling with stalled POCs, Rana breaks down the three-stage evaluation journey his team developed after repeatedly seeing deals fail at predictable moments. By designing the customer experience around those milestones, his team transformed how buyers gained confidence throughout the evaluation process.   Finally, we explored why great B2B AI products don't succeed by becoming another dashboard. Rather, they succeed by closing the loop between decisions, outcomes, and learning. Rana also fills me in on his upcoming book, The AI Instinct, which focuses on how AI changes human judgment rather than simply advancing model capabilities. And his parting advice? Listen to find out!   Highlights / Skip to: Making “invisible AI” value clear (3:57) The four surfaces of visibility the product team dials into to ensure Behavioral Signals is indispensable to customers(6:26) Behavioral Signals' intentionality behind their three-phase model to address deals not closing (15:35) How Rana's team deals with AI moving downstream problems further upstream (19:56) Determining their product's boundaries: when do you stop building? (22:55) Why proprietary data makes for such a good moat (24:57) What Rana would do the same and differently if he were starting over (28:45) Rana's book: The AI Instinct: The Future of AI and Human Decision-Making (39:29) Rana Gujral's closing advice (44:35) Links Behavioral Signals  The AI Instinct: The Future of AI and Human Decision-Making  Rana Gujral's website   Rana Gujral's LinkedIn

¿Tu empresa hace IA o solo lo aparenta? Tito Neira retoma la conversación con Iván Herrero Bartolomé, Chief Data Officer de Grupo Intercorp (+30 empresas) y cofundador de CDO LATAM, dos años después de su primera visita. Hablan de la trampa de los pilotos eternos, el "teatro de la innovación", por qué la prueba de concepto siempre funciona (y producción es otra historia), la receta de construir carreteras — arquitectura, observabilidad, casos de bajo riesgo con resultados reales (+15-20% ventas, +10-15 NPS) — y las 3 decisiones que un CEO debería tomar en los próximos 90 días.00:00 Intro y regreso de Iván01:59 De Bilbao a Intercorp04:38 ¿Burbuja de pilotos? No: una trampa07:26 La tecnología está madura, nosotros no09:04 Licencias no son ventaja competitiva11:49 El teatro de la innovación15:24 POCs que no llegan a producción17:48 El gap del 85% al 98%18:46 El "AI Engineer" de dos meses21:04 Construye carreteras primero22:33 Arquitectura de IA y observabilidad25:55 Casos de bajo riesgo y resultados29:33 Del caso puntual al proceso completo32:14 Las 3 decisiones del CEO en 90 días38:42 Cierre✩ CURSOS DISPONIBLES

Referências do EpisódioWEBINAR: A CONFIANÇA COMO PORTA DE ENTRADA DO ATACANTEDon't Eat The ChocoPoCs! How Vulnerability Researchers Were Repeatedly Targeted By Trojanised ExploitsARToken: Inside an EvilTokens affiliate panel targeting Microsoft 365Browser-Only Ransomware: From LLM Hallucinations to a Practical Attack TechniqueCisco Catalyst Center Arbitrary File Read VulnerabilityClamAV Vulnerabilities Affecting Cisco Products: July 2026Progress Kemp LoadMaster Pre-Auth RCE Flaw Faces Active Exploitation AttemptsRoteiro e apresentação: Carlos CabralEdição de áudio: Paulo Arruzzo Narração de encerramento: Bianca Garcia

In Episode 12 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Justin Borgman, Co-Founder and CEO of Starburst, where they discuss why the biggest barrier to AI success is no longer about models.The conversation explores why traditional approaches to data architecture are struggling in the AI era, how enterprises can overcome fragmented data estates, the importance of context and semantics, why many organisations remain stuck in pilot mode, rising AI costs, build versus buy decisions, agentic AI, and what the next three to five years of enterprise AI adoption are likely to look like, which includes;Why the vision of centralising all enterprise data into a single platform has never truly reflected reality.Why the AI industry's obsession with model selection is increasingly distracting organisations from the real challenges.How advances in foundation models are rapidly commoditising model performance and shifting attention elsewhere.What the true bottlenecks to AI adoption actually are.Where the clearest examples of AI delivering measurable value are today.Why many organisations remain trapped in POCs despite significant investment and executive attention.How the lack of context and semantic understanding continues to limit the effectiveness of AI in enterprise environments.Why trust, meaning and business context matter as much as access to data itself.Why AI success depends on; data foundations, analytics performance, enterprise context and trusted agentic interfaces.Why rising AI costs are becoming one of the biggest concerns for enterprise leaders and CFOs.Why data products are emerging as a practical solution for creating AI-ready context across the enterprise.Why separating context from physical data location creates more flexible and scalable architectures.Why executives are increasingly expecting answers rather than reports and dashboards.Why organisations should be building differentiated business capabilities rather than core platform infrastructure.How businesses that feel behind are often closer to the market than they realise.What the next three to five years could look like as AI becomes embedded into every major business function.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

La Flama del Canigó ha baixat aquesta matinada del cim de la muntanya i ha començat el seu recorregut per encendre les fogueres d'arreu dels països catalans. Pocs minuts després de les 6 del matí, una primera flama ha arribat al Coll d'Ares, un dels principals punts de pas de la flama, que enguany celebra 60 anys. Des d'aquest punt, centenars de persones han recollit la flama amb quinqués i fanalets per portar-la cap a punts d'arreu de Catalunya, ja sigui corrents, en bicicleta o cotxe.

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Today's guest is Chandra Sekhar Chappa, Global Head, Co-Innovation - ServiceNow at Google Cloud. Founded in 2016, Google Cloud is Google's enterprise cloud computing platform, providing organizations with scalable infrastructure, data analytics, AI, machine learning, security and application development services. Google Cloud helps businesses modernize operations, accelerate innovation, and securely build, deploy and manage applications and data at scale across hybrid and multi-cloud environments.Chandra is a technology leader with over 16 years of experience across product management, cloud operations, IT service management, infrastructure, and governance, risk and compliance. He specializes in ServiceNow, hyperscaler partnerships, cloud marketplace integrations, and enterprise service management. Chandra has led large-scale technology initiatives that have generated significant revenue growth and cost savings, while helping more than 1,100 enterprise customers improve cloud governance and operational efficiency.In the episode, Chandra talks about:0:00 His journey from IT tutor to leader in ServiceNow innovation at Google2:08 The importance of mentors in his career and giving back4:31 How the Google Cloud - ServiceNow integration/partnership combines AI with workflow transformation5:26 Enabling enterprise-scale workflows, execution and governance8:01 How their AI control tower provides full agent visibility and governance10:26 How the Google Cloud - ServiceNow integration/partnership enables real-time data and AI-driven CRM gains13:29 His advice to leverage AI ecosystems, avoid POCs and build faster15:51 The need to use trusted partners, align leadership and balance decision-making17:58 How mentorship, self-belief and persistence through uncertainty leads growthTo find out more about all the great work happening at Google Cloud, check out the website cloud.google.com

Introduction Most insurers say they want to be innovative. Fewer have a systematic way to know what's worth pursuing, who's building it, and whether they should partner, invest, or simply wait. Matt Connolly has spent ten years building the answer to that problem. Connolly is the founder of Sønr, a global market intelligence platform that tracks over five million companies and helps insurers, reinsurers, and brokers make better decisions about innovation and technology. Working with fifty-plus tier-one carriers—from Travelers and Liberty Mutual to Munich Re, Allianz, and Tokio Marine—as well as brokers like Guy Carpenter and WTW, Sønr sits at the intersection of the startups changing the industry and the incumbents that need to understand them. In this conversation, Josh Hollander and Connolly dig into where innovation intent breaks down inside large carriers, the four points where value leaks out of a corporate innovation process, why POC purgatory is a symptom not the disease, and how Sønr 2.0 is bringing market intelligence to operators who've been tasked to innovate but not given the tools to do it. Guest Bio Matt Connolly is the Founder and CEO of Sønr, a global insurtech market intelligence platform used by fifty-plus tier-one insurers, reinsurers, and brokers worldwide. Founded ten years ago, Sønr tracks over five million companies and has built a proprietary data set on insurance innovation unavailable to general AI platforms. He also hosts his own podcast interviewing innovation leaders from major global carriers. Sønr now generates half its revenue from North America and recently made its first US hire. Key Topics • Where innovation intent breaks down — At the CEO level. Without clear sponsorship and direction from leadership, innovation functions become disconnected from real business priorities. Ten years of data backs this up. • The four value leaks — Not understanding trends, poor scouting discipline, year-long POCs that should be three weeks, and failing to move from POC to pilot to scale. Each is a distinct failure mode with a distinct fix. • POC purgatory — Mature innovation programs are running more POCs than ever but scaling fewer. The root cause is almost always people: wrong sponsors, wrong internal champions, or wrong startup for the actual need. Sønr's fix: a one-day workshop to build a mini business case before a three-week POC begins, with KPIs and go/no-go criteria agreed upfront. • The decentralization of innovation — Carriers that once had centralized innovation functions have spread that mandate across underwriting, claims, and distribution—but capability hasn't followed. Operators have been tasked to innovate with no networks, no tooling, and no experience. This is the gap Sønr 2.0 addresses. • Sønr 2.0 and the Emerging Trends Academy — A simple front-end into ten years of proprietary insurance innovation data, priced for operators not just innovation teams. The Emerging Trends Academy goes deeper: cross-industry groups going deep on specific trends with startups, carriers, consultants, and academics in the same room. • The data moat — Ten years of tracking every company, trend signal, and client engagement within insurance innovation. Data that Connolly notes even Anthropic or OpenAI simply can't access. That compounded intelligence sits behind both the platform and the research offering. Notable Quotes "Don't go with the startup that is the best salesperson. Do the scouting properly—where are they based, what's their culture, who are their people, does the technology align to your needs?" "POC purgatory. We're seeing mature innovation businesses doing more POCs than ever but not moving beyond them. The answer is often the people." "The data we sit on is not available to anybody else. It's compounded intelligence from ten years. Anthropic or OpenAI simply can't get to it." "If you don't get your direction right from the top, the value leak is going to be huge later on. Just start in the right place." Resources Guest: • Sønr: https://www.sonr.io • Matt Connolly on LinkedIn: https://www.linkedin.com/in/wearematt/ Host & Organization: • Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ • Horton International (USA): https://www.horton-usa.com/ • Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

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GitOps ist ein DevOps-Ansatz, bei dem der Betrieb von Services als Code in Git abgelegt und versioniert wird, statt Deployments manuell über Oberflächen zusammenzuklicken. In dieser Episode erklären Mira und Andreas, was GitOps ausmacht, wie sich der deklarative Ansatz vom klassischen imperativen Vorgehen unterscheidet und wo die Abgrenzung zu Infrastructure as Code verläuft. Sie sprechen über die Vorteile – etwa Nachvollziehbarkeit, Versionskontrolle, Automatisierung und geringere Fehleranfälligkeit – ebenso wie über Herausforderungen rund um Secrets-Management und das nötige Umdenken. Außerdem ordnen sie ein, wann sich der Einsatz lohnt und wann manuelles Vorgehen sinnvoller bleibt. Den Abschluss bildet ein Hands-on-Teil mit konkreten Einstiegsschritten und Werkzeugen wie ArgoCD.   **Zusammenfassung** Was GitOps ist: Betrieb von Services als versionierter Code in Git, inklusive Konfiguration und laufender Versionen Beispiel API-Deployment: früher alles in der Pipeline, heute ein separates Repo, das den gewünschten Zustand beschreibt und von Tools wie ArgoCD mit dem Cluster abgeglichen wird Abgrenzung zu Infrastructure as Code: GitOps fokussiert die laufenden Services statt der Infrastruktur und gleicht Änderungen aktiv und kontinuierlich an Vorteile: Dokumentation, Rollback per Versionskontrolle, Automatisierung, weniger Fehler, Review-Möglichkeit und gemeinsame Verwaltung mehrerer Service-Versionen Herausforderungen: Umstieg von imperativ auf deklarativ, schwierigeres Debugging, alles muss in Git liegen, Secrets brauchen ein zusätzliches Tool Wann sinnvoll: ab MVP fast immer; bei kurzlebigen PoCs ruhig manuell oder per Pipeline Einstieg: mit neueren, einfacheren Projekten starten, ArgoCD installieren und schrittweise komplexer werden (dev/prod, mehrere Services) Fazit: kurze Einarbeitung, dann lohnt es sich – inzwischen etablierter Standard und "Deployments mit Ruhepuls" **Links** ArgoCD: https://argo-cd.readthedocs.io FluxCD: https://fluxcd.io ArgoCD Image Updater: https://argocd-image-updater.readthedocs.io Sealed Secrets: https://github.com/bitnami-labs/sealed-secrets External Secrets Operator: https://external-secrets.io Helm: https://helm.sh Kustomize: https://kustomize.io Kubernetes: https://kubernetes.io

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How to find prime contractors and pitch them for real subcontracting work is one of the most overlooked skills in federal contracting, and most small businesses get it completely wrong. In this episode of the Federal Help Center podcast, Eric Coffie sits down with Zach Golden to break down a step-by-step research method for stalking primes, profiling tribal 8(a) firms, and writing outreach that actually gets a response. If you've been chasing contracts you can't win solo, this is the workflow that opens doors. Here's what you'll learn inside this episode: Why large 8(a) primes like ANCs, tribal entities, and Native Hawaiian Organizations operate more like Amazon than Walmart and how to position yourself inside their partner network The exact research workflow Zach uses inside OpenCube IQ to pull a prime's financials, NAICS codes, contract history, and government POCs before sending a single email How to write a short capability statement email that doesn't over-explain and triggers a real reply from busy CEOs and business development leads How to use NAICS code spending data and state filters to surface the right primes when you don't yet know who's holding the contracts in your space The talking points strategy that lets you sound like an insider in conversations with primes even when you're early in your govcon journey EPISODE CHAPTERS: 0:00 - Why large 8a primes work like Amazon partners 1:25 - How to approach tribal entities with capability statements 2:50 - Sending capability statements into the network 3:30 - Researching tribal primes inside OpenCube IQ 5:00 - Reading contract history and finding government POCs 6:30 - Using NAICS code spending to find the right primes 7:45 - Filtering by state to narrow down vendor lists 8:45 - Building talking points that prove you know the game Market Intelligence gives you the federal opportunities, agency signals, recompete intel, and pursuit briefs that tell you not just what contracts exist, but which ones to chase and how to win them. Sign up for free Daily Alerts and get opportunities delivered to your inbox before the day starts.

In this episode, Raghu Nandakumara sits down with Andrew Rubin, Founder & CEO of Illumio, for a candid conversation about the next phase of AI-driven cybersecurity risk. Just weeks after a major AI breakthrough sparked shockwaves across the security industry, Andrew shares his immediate reaction — from the sobering implications of machine-speed vulnerability discovery to a frank assessment of why the cybersecurity industry's fundamental model may already be broken. The conversation explores what actually changes in an era where vulnerabilities could be discovered and exploited faster than any human-driven operation could manage. Andrew argues that while segmentation as a concept is decades old, its role as a critical backstop has never been more urgent. If attackers begin operating at machine speed, defenders must rethink not just their tools, but their entire operating model — from how they assess risk to how quickly they can respond. Raghu and Andrew discuss: Why the cybersecurity industry has spent more every year while outcomes have gotten worse How AI creates an asymmetric threat unlike anything defenders have faced before Why patching alone won't solve the problem — and the COVID vaccine analogy that explains why The shift from prevention to resilience as the new security north star What the SolarWinds story reveals about how organizations miscalculate tail risk Why segmentation becomes one of the few reliable backstops in a model-driven world How the era of 12-month RFPs and POCs may be coming to a swift and necessary end Stay Connected with our host, Raghu on LinkedIn: https://www.linkedin.com/in/raghunandakumara/ For more information about Illumio, check out our website at illumio.com    Resources Mentioned: Hard Truths in Cybersecurity: Fear, Liability, and the Industry's Biggest Lies | RSAC 2026 Panel:  https://www.youtube.com/watch?v=88XjfZBYIw0

Patrick Moorhead and Daniel Newman dig into the week's biggest moves in enterprise AI: Anthropic and OpenAI launching PE-backed enterprise JVs on the same day, Anthropic filling its compute gap with SpaceX's Colossus, Cerebris filing for a $3.5 billion IPO, NVIDIA going deep on co-packaged optics with Corning, and a full IBM Think and ServiceNow recap. Plus, for The Flip, hosts debate whether Anthropic, at $1.2 trillion, is the most important company in enterprise tech. The handpicked topics for this week are: 1. Anthropic and OpenAI Launch PE-Backed Enterprise JVs on the Same Day — Both companies announced private equity joint ventures, with OpenAI backed by Bain, Brookfield, and Advent, and Anthropic partnering with Blackstone, Goldman Sachs, Apollo, and General Atlantic. Daniel's read is that this is fundamentally a distribution play, using private equity portfolio companies as a deployment channel for AI at scale. Pat sees it as the clearest admission yet that enterprise AI cannot be self-implemented at scale without specialized consulting support, and flags that mid-tier systems integrators (SIs) could get cut out of the middle. (The Decode) 2. Anthropic Signs Massive Compute Deal with SpaceX Colossus — Anthropic urgently needed compute and SpaceX had 300 megawatts and 220,000 GPUs sitting at Colossus One in Memphis without enough business to fill them. Pat's take is blunt: this move is pragmatic. Anthropic needs it, xAI has it. Daniel adds that Dario himself said they planned for 10x growth and got 80x, and this deal is the fast backfill that reality demanded. The side note both hosts flag: Anthropic is running on H100s, H200s, and B200s, which puts the whole "Anthropic only runs on Trainium and TPUs" narrative to rest. (The Decode) 3. Cerebris Files for a $3.5 Billion IPO at $26.6 Billion Valuation — This marks their second attempt at an IPO after pulling the first filing. The architecture is genuinely unique, a complete wafer with massive on-chip SRAM and interconnects built directly onto the wafer rather than copper or photonics. Pat calls it the first credible Western alternative for AI inference. Daniel's framing cuts through: you do not have to beat NVIDIA to sell right now. You just need to have availability. The more interesting headline, both hosts agree, is that Sam Altman and Greg Brockman are angel investors, which adds fuel to the ongoing OpenAI lawsuit. (The Decode) 4. NVIDIA and Corning Announce $500 Million Optical Partnership — Three new US factories, co-packaged optics for Vera Rubin, and a supply chain strategy that mirrors what NVIDIA did with Coherent. Pat's context: this is vertical integration through investment rather than acquisition. Daniel's observation is that the pace of movement toward co-packaged optics is accelerating faster than anyone expected, and his "rule of and" applies here too. Copper is not going away. Optics are being added on top because the data volumes moving across these racks are outrunning what copper alone can handle. US manufacturing in North Carolina and Texas is a strategic bonus. (The Decode) 5. IBM Think 2026: Day Zero, Sovereign Core, and the Quantum Plus AI Bet — Pat moderated on stage with CEO Arvind Krishna and calls this IBM's best showing in five years. Arvind opened with the AI divide, the gap between companies still running POCs and companies already in production, and framed where IBM sits as day zero, not because nothing has happened, but because enterprise AI deployment at scale is still so early. Daniel's biggest takeaways: watsonX Orchestrate updates, Sovereign Core going GA with policy at runtime, and the Confluent acquisition potentially being IBM's most important asset since Red Hat, given that 40% of Fortune 500 companies run on it and real-time streaming data is foundational to agentic systems. Both hosts land on quantum plus AI as IBM's next inflection moment. (The Decode) 6. ServiceNow Knowledge 2026: Enterprise SaaS 2.0 is Emerging — Daniel got there on day three of the event and noted the conference was densely packed. His observation: enterprises have not gotten the memo from Wall Street that SaaS is supposedly dead. His emerging thesis is that middleware could make a comeback for AI, with companies needing a layer that lets agents work across any infrastructure, any app, and within the rules of their specific business. Pat agrees and adds that the growth question is about mix, not survival. (The Decode) 7. The Flip: Is Anthropic at $1.2 Trillion the Most Important Company in Enterprise Tech? — Daniel took the affirmative citing that Claude Code is deeply entrenched in developer workflows. Anthropic went from $9 billion to $45 billion ARR in months. Every major hyperscaler is both a customer and an investor. The PE JVs are turning verticals into Anthropic engines. Dario said they planned for 10x and got 80x. Pat's counter: the enterprise trust gap is real after what Anthropic pulled on pricing and performance. Microsoft has 2 billion users across 365, Azure, and Copilot. NVIDIA is the infrastructure Anthropic runs on. And workforce replacement, which is how Anthropic extracts its terminal value, is not arriving as fast as the valuation suggests. In reality, both hosts admit their notes looked almost identical. (The Flip) 8. AMD — Lisa Su guided AI data center growth up from 60% to 80%. With OpEx growing 83%, net income up 95%, free cash flow ripping, and CPUs growing at nearly 40% without price increases, Pat reads this as unit market share gains coming soon. Daniel's framing: AMD is now a two-headed juggernaut with CPUs and GPUs for the data center. And Helios has not even started shipping yet. Both hosts take a victory lap for previously calling this one. (Bulls and Bears) 9. Palantir — Triple beat on revenue, EPS, and forward guidance. Rule of 40 at 145%. Government revenue up 84%, 47 deals over $10 million, and the largest guidance raise in the company's history. Daniel's take: Palantir is redefining the category entirely. It's not a software company in the Salesforce or ServiceNow sense. It's technology, plus ontology, plus people, deployed at the deepest layers inside governments and enterprises. Pat adds that the four deployed FTE model lets them stand up AIP POCs within a week, which is why they are winning business at this pace. (Bulls and Bears) 10. ARM — AGI processor demand doubled from $1 billion to $2 billion within 45 days. Record revenue, strong pipeline, royalty growth at 21% for the full year. The stock ripped after hours, then sold the next day when management confirmed only enough supply for $1 billion of that $2 billion demand. Pat's read: 50% CPU market share with hyperscalers at the core level is the most underdiscussed signal on the call. Daniel adds that the worry about ARM competing with its own customer base in custom silicon has been quietly swept away by the sheer volume of compute demand. (Bulls and Bears) 11. Supermicro — A board member allegedly used a hairdryer to remove labels from GPU boxes being shipped to China. Approximately 20% of their revenue has reportedly been illegally shipped to China. They beat on EPS and Q4 guide but missed Q3 revenue versus consensus. Stock still ripped 18%. Daniel's take: if you are selling picks and shovels during a gold rush and you are this messed up, he cannot imagine owning it with the overhang that is building. (Bulls and Bears) 12. Lattice Semi and Coherent — Lattice revenue up 42%, back into growth, guiding to 50% year-on-year at midpoint. The AMI acquisition at $1.65 billion doubles their serviceable market from $6 billion to $12 billion and puts them inside every AI server on the planet at the BIOS and platform firmware layer. Pat calls the timing right: core financials crushing it, time to make a move. Coherent printed 21% year-on-year growth, 55% EPS growth, margins expanding, debt coming down, entered the S&P 500, and sits at the center of the co-packaged optics trend that is accelerating. Pat's choke point note: Indium phosphide capacity is the constraint. Six-inch fabs are doubling capacity in 2026, a quarter ahead of plan, and competitors are still ramping their transitions. (Bulls and Bears) Want the full breakdown from IBM Think and ServiceNow Knowledge, and check out our on-the-ground coverage linked in the show notes. Be part of our community. Hit that subscribe button and let us know what you want us to cover next week in the comments. Intro Pat on Stage at IBM Think https://x.com/PatrickMoorhead/status/2051381046537601101?s=20 The Decode OpenAI and Anthropic Both Launch PE-Backed Enterprise Services JVs on the Same Day — The Palantir FDE Model Goes Mainstream https://www.bloomberg.com/news/articles/2026-05-04/openai-finalizes-10-billion-joint-venture-with-pe-firms-to-deploy-ai https://techcrunch.com/2026/05/04/anthropic-and-openai-are-both-launching-joint-ventures-for-enterprise-ai-services/ https://www.semafor.com/article/05/04/2026/openai-anthropic-ramp-up-enterprise-push Anthropic and SpaceX Sign Massive Compute Deal — Full 300MW / 220,000 GPU Colossus 1 Memphis Data Center Plus Exploration of Multi-Gigawatt Orbital AI Compute https://www.cnbc.com/2026/05/06/anthropic-spacex-data-center-capacity.html https://www.bloomberg.com/news/articles/2026-05-06/anthropic-inks-computing-deal-with-spacex-to-meet-ai-demand https://www.tomshardware.com/tech-industry/artificial-intelligence/musks-spacex-has-rented-out-access-to-its-supercomputers-220-000-nvidia-gpus-and-300-megawatts-of-ai-compute-power-to-rival-anthropic Cerebras Files for $3.5B IPO at $26.6B Valuation — The First Major AI Chip IPO of 2026 https://www.cnbc.com/2026/05/04/cerebras-ipo-ai-chipmaker.html https://theaiinsider.tech/2026/05/06/cerebras-systems-eyes-3-5b-in-largest-tech-ipo-of-2026-on-strength-of-ai-chip-demand/ https://www.briefs.co/news/ai-chipmaker-cerebras-just-filed-for-a-3-5-billion-ipo/ NVIDIA and Corning Announce Game-Changing Optical Partnership — $500M Investment, 3 New U.S. Factories, and Co-Packaged Optics for Vera Rubin and Beyond https://www.corning.com/worldwide/en/about-us/news-events/news-releases/2026/05/nvidia-and-corning-announce-long-term-partnership-to-strengthen-us-manufacturing-for-ai-infrastructure.html https://www.cnbc.com/2026/05/06/nvidia-corning-optical-factories-nc-texas-ai.html https://www.wsj.com/tech/nvidia-corning-form-partnership-to-expand-fiber-optic-manufacturing-17f525de https://kfgo.com/2026/05/06/corning-partners-with-nvidia-to-expand-us-fiber-optic-output-for-ai-growth/ IBM Think 2026 Boston — Watsonx Orchestrate Next-Gen, Confluent Real-Time Data, IBM Concert, and Sovereign Core Define IBM's Agentic Operating Model https://newsroom.ibm.com/2026-05-05-think-2026-ibm-delivers-the-blueprint-for-the-ai-operating-model-as-the-ai-divide-widens https://www.ibm.com/new/announcements/ibm-announcements-at-think-2026 https://www.instagram.com/reel/DX42DlrglOs/ ServiceNow Knowledge 2026 Las Vegas https://www.servicenow.com/events/knowledge.html https://newsroom.servicenow.com/press-releases/details/2026/Cohesity-and-ServiceNow-Deliver-Real-Time-Recovery-for-Enterprise-AI-Agents/default.aspx https://www.cnbc.com/2025/09/04/nvidia-backed-cohesity-eyes-2026-ipo-with-valuation-rivaling-17-billion-rubrik.html   The Flip: Anthropic at $1.2T Now the Most Important Company in Enterprise Tech — More Important Than NVIDIA, Microsoft, or OpenAI FOR: Dual-hyperscaler compute anchor (Amazon $33B + Google $40B = $73B) is structural — unmatched https://futurumgroup.com/insights/anthropics-gigawatt-scale-tpu-deal-with-broadcom-creates-a-structural-advantage/ Constitutional AI safety positioning wins regulated industries https://www.anthropic.com/news/anthropic-nec-japan-ai-engineering-workforce $900B valuation surpasses OpenAI ($852B) at faster revenue growth and lower burn rate https://techcrunch.com/2026/04/30/anthropic-potential-900b-valuation-round-could-happen-within-two-weeks/   AGAINST: NVIDIA still controls the substrate — every Anthropic dollar of revenue requires NVIDIA inference at some layer https://www.cnbc.com/2026/04/27/nvidia-just-hit-an-all-time-high-why-some-think-a-rally-is-just-getting-started.html Microsoft has the enterprise distribution — 365 + Azure + Copilot reach >2 billion users https://www.marketbeat.com/originals/microsofts-maia-200-the-profit-engine-ai-needs/ $900B valuation is venture marketing — the IPO will reset the number https://www.semafor.com/article/05/04/2026/openai-anthropic-ramp-up-enterprise-push   Bulls & Bears: AMD Q1 2026 — Revenue $10.3B (+38% YoY), MI300X Data Center GPU Demand Drives Stock +20% on the Print https://ir.amd.com/news-events/press-releases/detail/1284/amd-reports-first-quarter-2026-financial-results https://www.cnbc.com/2026/05/05/amd-q1-2026-earnings-report.html https://finance.yahoo.com/markets/stocks/articles/amd-q1-2026-earnings-revenue-203331768.html Palantir Q1 2026 — Revenue +85% YoY, US Commercial +133%, Rule of 40 Score Hits 145%; Largest Guidance Raise in Company History https://investors.palantir.com/files/Palantir%20-%20Q1%202026%20Business%20Update.pdf https://www.reddit.com/r/PLTR/comments/1t3t0me/palantir_reports_q1_2026_us_revenue_growth_of_104/ https://finance.yahoo.com/markets/stocks/articles/palantir-technologies-inc-q1-2026-002218719.html https://semiconalpha.substack.com/p/palantir-q1-2026-rewriting-the-rule Arm Holdings Q4 FY2026 — Record $1.49B Quarter, Full-Year Revenue Crosses $4.92B, $2B AGI CPU Pipeline; Stock +16% After Hours https://finance.yahoo.com/markets/stocks/articles/arm-q4-earnings-call-highlights-225942093.html https://www.stocktitan.net/sec-filings/ARM/6-k-arm-holdings-plc-uk-current-report-foreign-issuer-7e9ca9ac7dda.html https://semiconalpha.substack.com/p/arm-q4-fy2026-record-quarter-2-billion Super Micro Computer Q3 FY2026 — Revenue $10.2B (+123% YoY), Strong Q4 Guide; Stock +18% AH on First Earnings Call Since Co-Founder Indictment Drama https://www.cnbc.com/2026/05/05/super-micro-smci-q3-earnings-report-2026.html https://www.stocktitan.net/sec-filings/SMCI/8-k-super-micro-computer-inc-reports-material-event-e70b2f8b3cb7.html https://www.instagram.com/reel/DX42DlrglOs/ Lattice Semiconductor Q1 2026 — Beat-and-Raise Quarter ($170.9M, +42% YoY) Paired With $1.65B AMI Acquisition That Doubles Lattice's SAM to $12B https://www.stocktitan.net/sec-filings/LSCC/8-k-lattice-semiconductor-corp-reports-material-event-642a862b2bf9.html https://www.ami.com/resources/ami-announces-agreement-to-be-acquired-by-lattice-semiconductor/ https://www.linkedin.com/posts/patmoorhead_lattice-semiconductor-posts-beat-and-raise-activity-7457411226944425984-xA8T Coherent Q3 2026 Earnings https://www.msn.com/en-us/money/companies/coherent-cohr-tops-revenue-expectations-in-q3-as-ai-demand-accelerates-shares-decline/ar-AA22Bz24?ocid=finance-verthp-feeds  

L'Olga Angaril va ser la primera dona taxista de Sitges, ara ja jubilada repassa els seus inicis en un món d'homes. Pocs anys després es va incorporar la Sònia Bages que ja suma quinze anys portant el taxi a Sitges, i aquesta darrera setmana s'ha incorporat la Carla Delgado, propietària de llicència que ha decidit fer un canvi de rumb laboral i deixar la banca per incorporar-se al taxi. Amb elles conversem sobre com és treballar en un món altament masculinitzat on només el 10% dels treballadors són dones. L'entrada Olga, Sònia i Carla, històries de tres dones taxistes de Sitges ha aparegut primer a Radio Maricel.

Hoje, fraudes não são mais eventos isolados — elas viraram processos automáticos, digitais e orquestrados em escala, impulsionados por IA e pela perda do controle do perímetro tradicional de segurança. Como as empresas estão reagindo a essa nova realidade para proteger dados, reduzir riscos e, ao mesmo tempo, garantir uma boa experiência para o usuário?No episódio, converso com dois especialistas que estão na linha de frente desse desafio: Marcelo Queiroz, Head Product Innovation ID&F no Serasa Experian, que lidera soluções de identidade digital, prevenção à fraude e cibersegurança Pedro Ivo Lima, CEO & Co-Founder da PhishX, startup focada em transformar o comportamento humano em uma linha de defesa contra ataques digitais.Falamos sobre a importância da orquestração de múltiplos fatores de autenticação, os dilemas entre segurança versus experiência do usuário, e os riscos trazidos pela IA generativa que pode tanto proteger como facilitar ataques sofisticados.Também exploramos o conceito de identidade digital reutilizável, a mudança radical no modo como negócios vão se estruturar com agentes digitais autorizados e um olhar prático para startups — que precisam abraçar a segurança desde a concepção do produto, mesmo com recursos limitados.E por falar em oportunidade para startups: as inscrições para o Programa de Inovação Aberta da Serasa Experian estão abertas! Sua startup pode conquistar investimento de até R$ 50 milhões, mentoria com especialistas, POCs com investimento e acesso a dados e infraestrutura da maior datatech do Brasil — com escala real junto à Serasa Experian. Não deixe para depois: essa é a chance de construir o próximo padrão de confiança digital. Link nas nossas redes sociais.Se você quer entender como fraude virou software, por que o futuro da segurança digital passa por inovação constante e quais atitudes aplicar hoje no seu negócio para não ser mais uma vítima — dá o play e vem com a gente!Para conferir mais conteúdos,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠acesse nosso site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!Instagram:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠@aceventuresbr⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠ACE Ventures⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠E-mail: ⁠⁠⁠⁠⁠⁠contato@goace.vc⁠⁠⁠

In Episode 3, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Daragh Kelly, Chief Data Officer at The Economist, where they discuss why most AI initiatives are still failing, why there's not much measurable progress and how insight functions have become toolmakers and decision intelligence partners, which includes;Why most AI initiatives fail because they as a solution looking for problems.The importance of aligning AI use cases to strategic goals, KPIs and measurable outcomes.Why speed rather than velocity leads to very little measurable progress.Why compelling POCs create false confidence before the real production challenges begin.The deployment gap: why robust, scalable and commercially viable AI is still hard.Why disconnected tools and poor workflow integration stall AI value realisation.The simple test for prioritisation: is this problem big enough to matter?Why the best AI use cases act as building blocks for future capability.How AI and UX together are driving true self-service insight generation.Why insight teams are evolving from answer providers to toolmakers.The growing importance of data governance, quality and observability in an AI-first world.How distributed insight creation can weaken corporate memory and knowledge curation.The skills shift toward UX, enablement, storytelling and decision intelligence.Practical build vs buy criteria in fast-moving and rapidly commoditising AI markets.Why operating models matters less than discipline, purpose and capability building.

80% of enterprise AI projects never reach production. After two decades helping enterprises adopt new technology, Kashif Manzoor breaks down the five failure modes killing enterprise AI initiatives, introduces the GenAI Maturity Framework, and shares three questions every CTO should ask before approving their next AI project. Episode #: 185 In this episode, you'll learn: The 5 failure modes killing enterprise AI initiatives The GenAI Maturity Framework (6 dimensions, 6 levels) 3 questions every CTO should ask before their next AI initiative Why the gap between perceived and actual AI maturity is where POCs go to die Practical actions you can take this week   TIMESTAMPS: 0:00 - The POC graveyard (a real conversation) 1:30 - Welcome + Why this episode exists 3:30 - My journey: Oracle → Cloud → GenAI 7:00 - The 80% problem: Why enterprise AI fails 10:00 - Failure Mode 1: The Strategy Gap 12:30 - Failure Mode 2: The Architecture Gap 15:00 - Failure Mode 3: The Governance Gap 17:00 - Failure Mode 4: The Talent Gap 19:00 - Failure Mode 5: The Measurement Gap 21:00 - The GenAI Maturity Framework (6 levels explained) 24:00 - 3 Questions Every CTO Should Ask 26:30 - What's coming next 28:00 - Subscribe + Connect

DescriptionBhaskar was employee #1 at AppDynamics, which was sold to Cisco for $3.7B. He and co-founder Jyoti found a way to change how enterprise monitoring tools worked. From tracking low-level code metrics that ops teams didn't understand to monitoring what the business actually cares about.In this episode, Bhaskar breaks down how that one insight won them Netflix and Priceline as early customers, why they ran production POCs that no competitor would dare try, and how a free download called AppDynamics Lite generated over 60% of their leads—in an industry where getting started normally took weeks of professional services and six-figure contracts.Why You Should ListenWhy selling to developers is operating on hard mode.How one-day POCs became the killer enterprise sales weapon.Why freemium disrupted an industry that required weeks of professional services to get started.How they grew from $2M to $12M in revenue in just one year post launch.Keywords startup podcast, startup podcast for founders, product market fit, AppDynamics, application monitoring, enterprise SaaS, B2B sales, finding pmf, freemium strategy, Cisco acquisition, production POCChapters00:00:00 Intro00:11:33 Choosing the ICP00:20:37 Landing Netflix with Freemium00:28:44 Growing from $2M to $12M in Year Two00:30:10 The Free Download Strategy That Generated 60% of Leads00:32:04 Days from the NASDAQ Bell—Then Cisco Offered $3.7B00:41:28 The Moment of True Product Market FitSend me a message to let me know what you think!

Idén februárban hirtelen tíz éve nem látott szintre emelkedett a munkanélküliségi ráta Magyarországon. A témát Hornyák József, a Portfolio munkaerőpiaccal foglalkozó elemzője segített értelmezni. A második részben a szőlő aranyszínű sárgaság betegségével foglalkoztunk: Kovács Nóra, az Agrárszektor vezető szerkesztője arról beszélt, hogy a tavaszi munkák indulásával a veszély ismét aktuálissá vált. Főbb részek: Intro – (00:00) Munkanélküliség – (01:32) Aranyszínű sárgaság – (13:09) Tőkepiaci kitekintő – (22:14) Kép forrása: Getty ImagesSee omnystudio.com/listener for privacy information.

Nauta is building the data infrastructure layer for global supply chain, starting with mid-market shippers who manage 600+ suppliers across 40+ countries but lack a single source of truth. Co-founded by Valentina Jordan, who spent six and a half years at Rappi, Nauta targets the $200M-$2B revenue segment where companies face enterprise-level complexity without enterprise resources. In this episode of BUILDERS, Valentina shares how Nauta moved from Excel automation to building data pipes that connect 12-13 stakeholders touching a single product—and why they refuse to run POCs.Topics Discussed:Why shippers with ERP, TMS, and WMS systems still run operations in ExcelThe tribal knowledge crisis: 20-30 year operators retiring with undocumented institutional knowledgeNauta's no-POC policy and why it requires contract exit clauses insteadThe cost reduction vs. revenue generation framework that escapes pilot purgatoryBuilding familiar interfaces (Excel-like tables) over novel UX for conservative industriesThe shift from hiding AI capabilities (January 2025) to leading with them (eight months later)GTM Lessons For B2B Founders:Distinguish symptoms from root cause pain in discovery: Most enterprise buyers surface symptoms, not problems. A client reporting penalty costs isn't revealing the root issue—just downstream impact. Valentina uses the five whys methodology to drill into actual pain: "A client can tell me, hey, I'm paying X amount of dollars in penalties. That's not necessarily the root cause, it's just a symptom of the actual pain." This prevents building features that address surface-level complaints while missing the structural problem. The real issue might be data fragmentation across systems, lack of visibility into supplier performance, or decision-making bottlenecks—each requiring different solutions.Structure POC alternatives that demand mutual commitment: Nauta kills traditional POCs entirely because "it implies that they are testing us and that it's not a collaborative process." Instead, they offer contract exit clauses if expectations aren't met while requiring upfront commitment. This only works when you have proven results and can confidently deliver value. The insight: POCs create evaluator-vendor dynamics where the burden of proof sits entirely on you. Paid engagements with performance-based exits create partner dynamics where both parties invest in success. For early-stage companies without case studies, this won't work—but once you have repeatable results, test this approach.Layer revenue generation on top of cost reduction: Nauta starts every engagement with 3-4 cost reduction KPIs—penalties, reconciliation time, manual labor automation—then transitions to revenue generation through fill rate optimization and cash-on-cash improvements. "You need to go beyond just cutting costs. That way you transition from a nice to have to a must have." Supply chain has historically been viewed as a cost center; proving top-line impact changes budget conversations entirely. This matters because cost reduction has a ceiling (you can only cut so much), while revenue generation creates expanding budget headroom. Map your product capabilities to both from day one.//Sponsors:Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co//Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM  This episode reframes how leaders should approach generative AI, with insights from Justin Trombold. Instead of chasing use cases or tools, the focus is on fixing processes, incentives, and operating models. The conversation explores why many AI pilots fail, how ROI thinking can mislead, and why AI should be treated as a new way of working rather than a software upgrade. Practical examples show how small, disciplined changes can unlock productivity, innovation, and meaningful business impact without overinvesting or freezing in fear. 

Qualytics is redefining enterprise data quality by positioning it as a collaborative business function rather than an isolated data engineering problem. Founded at the start of the pandemic by Gorkem Sevinc - a former CTO and CDO who spent years managing reactive data quality firefights - Qualytics emerged from a clear practitioner pain point: writing endless custom rules to catch data issues after they'd already broken dashboards and KPIs. The company raised pre-seed and seed rounds while building with beta customers, then closed a Series A as repeatability patterns emerged in their POC process. Now, as enterprises scramble to operationalize AI initiatives, Qualytics is experiencing explosive inbound demand from organizations realizing their data foundations aren't ready for democratized data access. Topics Discussed The practitioner insight that sparked Qualytics: reactive rule-writing doesn't scale Leveraging existing CTO/CDO networks and PE portfolio connections for beta customers The evolution from free POCs to paid POCs as a mutual commitment mechanism Identifying repeatability through week-by-week POC conversion patterns Building practitioner credibility into the sales motion while hiring for enterprise sales grit The decision to hire sales and marketing leadership simultaneously post-Series A Tracking in-product engagement metrics (DQ operations frequency, anomaly detection, rule editing) as churn prevention Positioning data quality as vertical-specific business problems (premium leakage, regulatory compliance) The timing advantage: AI adoption forcing enterprises to treat data governance as mandatory infrastructure GTM Lessons For B2B Founders Talk to 100 prospects before writing code—even with deep domain expertise: After burning 18 months building a radiology second opinion product that patients didn't want (they didn't even know radiologists were doctors), Gorkem adopted a hard rule: validate with 100 conversations before building. His advantage as a former CTO who lived the data quality problem created false confidence. Practitioners often assume their pain is universal, but buyer awareness and willingness to pay are separate questions. Start with NSF I-Corps-style problem validation: show rough sketches, probe what happened when they hit the pain point, understand how it hurt them financially or operationally. Repeatability appears in micro-conversions during trials, not just closed-won rates: Gorkem didn't declare product-market fit when deals closed—he declared it when he could predict POC behavior by week. "Week two, I'm expecting this. Week three, I'm expecting this." That predictability enabled ROI calculators and internal champion enablement materials. For technical founders, this means instrumenting your trial or POC to track leading indicators: specific features activated, data volumes processed, number of team members engaged, frequency of logins. When those patterns stabilize across prospects, you have a repeatable motion. Use paid POCs as a procurement front-loading mechanism, not a revenue play: Qualytics charges nominal amounts for some POCs—not for the revenue, but to get the MSA signed and force both parties through legal/security review upfront. This eliminates the pattern where free POCs succeed technically but die in procurement. Large enterprises often refuse to pay for POCs, which Gorkem accepts—but only if they commit equivalent effort (executive time, cross-functional teams). The paid POC is a qualification tool: if they won't commit anything, they're not a real opportunity. Hire sales and marketing leadership in parallel and hold them to unified GTM metrics: Gorkem regrets hiring early sales reps before leadership and delaying marketing investment. Post-Series A, he hired both leaders simultaneously and holds them jointly accountable to pipeline generation and velocity—not siloed MQL counts or quota attainment. This structural decision forces collaboration on messaging, ICP definition, and campaign strategy from day one. For technical founders who "figured out" founder-led sales, resist the urge to replicate your motion with more SDRs. Bring in strategic leadership that can build a scalable system. Instrument product engagement as your earliest churn signal—then intervene immediately: Beyond quarterly NPS and executive QBRs, Gorkem tracks granular product usage: how many data quality operations users run, how many anomalies they discover, how actively they're editing rules. When engagement drops, he doesn't wait—he jumps into the customer's existing weekly meetings to diagnose and course-correct. For B2B founders building complex products with long time-to-value, passive health scores aren't enough. You need active usage telemetry and a low-latency intervention process. Translate technical capabilities into vertical-specific business outcomes: Gorkem doesn't pitch "data quality for data engineers." He talks about premium leakage with insurance companies and OCC/SEC data controls with banks. This reframing works because buyers recognize their problem, not a vendor category. The shift requires research: understand each vertical's regulatory environment, operational pain points, and the business metrics executives care about. When you walk in speaking their language about their P&L impact, you're not another vendor—you're someone who gets it. Time your market entry to when "nice-to-have" becomes "must-have": When Qualytics launched, some enterprises called data quality a "nice-to-have." AI adoption changed that calculus overnight. Organizations planning to let 20,000 employees interrogate data through AI interfaces suddenly realized they need robust data governance, quality controls, and cataloging first. Gorkem's timing wasn't luck—he built during the "nice-to-have" phase so he'd be ready when AI budgets made it mandatory. Technical founders should identify the external forcing function (regulation, technology shift, economic change) that will transform their solution from vitamin to painkiller. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

No episódio do Hipsters.Talks, PAULO SILVEIRA, CVO do Grupo Alura, conversa com KUNTUALA ZELI, diretora de vendas na Oracle, sobre como funciona toda a jornada de vendas em empresas de software, cloud e SaaS. Vamos explorar o papel do pré-vendas/arquitetos, que fazem a ponte entre as demandas do cliente e a tecnologia, e dos engenheiros, que mergulham na profundidade técnica e conduzem as POCs. Uma conversa que mostra como vendas em tecnologia deixaram de ser apenas comerciais e passaram a exigir profundo entendimento técnico, visão de negócio e colaboração entre times. além de revelar como carreiras híbridas entre tech e vendas estão se tornando cada vez mais comuns.

I'm digging into a frustrating reality many teams face: even technically superior analytics and AI products routinely lose deals—not because the KPIs or models aren't good enough, but because buyers and users can't clearly see how the product fits into their day-to-day work. Your demos and POCs may prove what's possible, but long time-to-understanding, heavy thinking burden on the user, and required behavior or process changes introduce risk—and risk kills momentum. When value feels complicated, sales don't move forward.     Adding to the challenge is that many sales efforts focus almost entirely on the fiscal buyer while overlooking the end users who actually have to adopt the product to create outcomes. This buyer–user mismatch, combined with status quo bias, often leads to indecision rather than change. To address this, I explore the idea of thinking about the sales challenge as a product problem—and I introduce the idea of achieving Flow of Work Alignment (FOWA). The goal isn't better persuasion—it's clearer value. Strong FOWA means transitioning from demonstrating capabilities to helping customers see themselves—and their workflows—represented in your demos and POCs. The result? Prospects understand your value quickly, ask deeper, contextual questions, and deals move forward.      Highlights/ Skip to: Data products must work harder to expose value clearly to avoid the dreaded “closed-lost” deal stage in your CRM (1:38) Making your data product's value instantly obvious (5:18) How the “old model” of selling based on capabilities and feature demos can lead to lost sales (7:22) What  Flow-of-Work Alignment is and how it can help you unlock deals (13:02) How to know if you have achieved FOWA or not in your product and sales process (13:58)

aiOla is pioneering speech-to-data technology that transforms unstructured speech into actionable data for enterprise operations. As a serial entrepreneur on his sixth startup, Co-Founder Amir Haramaty built aiOla after witnessing firsthand how traditional AI implementations fail to deliver ROI in enterprise settings. The company has developed proprietary technology that achieves near-100% accuracy in challenging environments with heavy jargon, multiple languages, and difficult acoustics. With strategic investors including a major airline and partnerships with Nvidia, Accenture, and USG, aiOla is addressing the fundamental challenge that 95% of enterprise AI pilots fail to show value by focusing on immediate, measurable ROI through speech-based data capture. Topics Discussed: The genesis of aiOla from consulting work revealing AI's implementation gaps in traditional enterprises Solving the triple challenge of speech recognition: accuracy in jargon-heavy environments, separating signal from noise, and converting speech to structured workflow data aiOla's "jargonic" approach: creating hyper-personalized language models for specific processes without retraining Early customer acquisition through serendipitous encounters and demonstrating immediate ROI Vertical expansion strategy from food manufacturing to aviation, travel, hospitality, and retail Channel partnership strategy refined from previous startups to achieve scale The shift from convincing customers about speech technology to being pulled into diverse use cases Building the aiOla Intelligate orchestration layer to dynamically select optimal speech recognition models GTM Lessons For B2B Founders: Make CFOs your best friend, not IT departments: Amir explicitly targets CFOs rather than IT as primary buyers because "it doesn't matter how small or big you are, you still have to do more with less." While IT serves as facilitators, CFOs control budgets focused on operational efficiency and ROI. B2B founders should identify which executive truly owns the pain point and budget authority, even if IT will implement the solution. Deploy capital strategically to remove obstacles before they emerge: aiOla convinced their airline investor to provide working capital specifically to fund POCs for prospects without existing budgets. This eliminated the "we don't have pilot budget" objection before it arose. B2B founders should proactively identify and neutralize common barriers in their sales process, whether through creative deal structures, proof-of-concept funding, or implementation support. Prioritize instant ROI over long-term transformation promises: Amir explicitly avoids "digital transformation" conversations, instead selecting use cases delivering "biggest impact within shortest period of time with minimum obstacle possible." The airline baggage tracking example saved 110,000 hours immediately, creating momentum for expansion. B2B founders should resist selling comprehensive transformation and instead identify narrow use cases with quantifiable, rapid returns that create internal champions. Replicate proven use cases across customers rather than customizing: Once aiOla achieved success with specific applications like CRM data entry or pre-op inspections, they "stop, print, replicate" rather than reinventing for each customer. This approach reduced a two-hour inspection process to 34 minutes in food manufacturing, then replicated across industries. B2B founders should document successful implementations as repeatable playbooks and resist the urge to over-customize for each prospect. Channel success requires speaking the partner's economic language: When working with telcos, Amir demonstrated that his solution increased ARPU by 34% and reduced churn by 17%—the only two metrics telcos prioritize. He built predictable models showing exactly how many units each channel rep would sell by geography. B2B founders pursuing channel strategies must translate their value proposition into the specific KPIs that drive partner economics and compensation. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Neo4j's Ajay Singh discusses future shifts in AI and why knowledge graphs may be the missing layer in your Gen AI strategy.Topics Include:Ajay Singh from Neo4j discusses graph intelligence platform serving 80+ Fortune 100 companies.Financial services firms use Neo4j knowledge graphs to detect fraud rings and accounts.IT companies build digital twins of infrastructure to analyze attack surfaces and vulnerabilities.Knowledge graphs provide richer context for Gen AI agents beyond what vector search offers.Gaming company achieved 10x faster insights and 92% reduction in analyst data gathering.Transportation company improved tariff code workflow from 50% abandonment to 95% completion rate.Neo4j has partnered with AWS since 2013, running on AWS infrastructure and Marketplace.Customers combine Neo4j with AWS Bedrock and SageMaker to build agentic AI applications.Neo4j evolved from late-stage AWS collaboration to early-stage joint customer solution development approach.Success requires business-first mindset over technology-first to avoid POCs that never reach production.Effective Gen AI needs semantic layers and knowledge graphs, not just throwing documents at LLMs.Future agents will tackle outcome-based objectives requiring explainability, security, and proper LLM operations.Participants:Ajay Singh – Global Vice President, Neo4jSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Corey Quinn sits down with Avery Pennarun, co-founder and CEO of Tailscale, for a deep dive into how the company is reinventing networking for the modern era. From finally making VPNs behave the way they should to tackling AI security with zero-click authentication, Avery shares candid insights on building infrastructure people actually love using, and love talking about.They get into everything: surviving 100% year-over-year growth, why running on two tailnets at once is pure chaos, and how Tailscale makes “secure by default” feel effortless. Plus, they dig into why FreeBSD firewalls needed some tough love, the uncomfortable truth behind POCs, and even the surprisingly useful trick of turning your Apple TV into an exit node.About Avery: Avery Pennarun is the co-founder and CEO of Tailscale, where he's redefining secure networking with a simple, Zero Trust approach. A veteran software engineer with experience ranging from startups to Google, he's known for turning complex systems into approachable, user-friendly tools. His contributions to projects like wvdial, bup, and sshuttle reflect his belief that great technology should be both powerful and easy to use. With a mix of technical depth and dry humor, Avery shares insights on modern networking, internet evolution, and the realities of scaling a startup.Highlights:(0:00) Introduction to Tailscale and Security(00:52) Sponsorship and Personal Experiences(02:07) Technical Deep Dive into Tail Scale(06:10) Challenges and Future of Tail Scale(22:45) Building the Tail Net's API(23:54) Connecting Cloud Providers with Tailscale(25:22) Tailscale as a Security Solution(26:44) Innovations and Future of TailscaleSponsored by: duckbillhq.com

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Catching yourself rereading last year's VC emails while you're back in Silicon Valley is a pretty good way to realize how wild the last 12 months have been. Colin, Chuck, Canisius, and Todd break down how Collide AI is turning fast POCs into real production workflows, why change management is the actual moat, and how a stacked forward deployed team plus community driven distribution is setting up 2026 to be the year everything scales.Click here to watch a video of this episode.Join the conversation shaping the future of energy.Collide is the community where oil & gas professionals connect, share insights, and solve real-world problems together. No noise. No fluff. Just the discussions that move our industry forward.Apply today at collide.ioClick here to view the episode transcript. 00:00 Product market fit jokes and kickoff00:28 VC email flashback and velocity01:29 Forward deployed model and AI first mindset02:18 Sam Texas and AI coding shift04:04 What AI first actually means06:18 Not just podcast bros anymore07:00 AI breaks silos across the business08:21 Doglegs example and incentives09:57 Change management is the advantage10:18 Client story and regulatory filings win12:42 Selling outcomes not hype13:36 Building the FTE team and faster delivery16:24 AI strategy as workflow ROI first18:26 Grok as a thought partner and GPU cluster20:15 Shale revolution mindset parallel22:29 Recruiting, software DNA, and stacked team26:16 Content and community as a recruiting engine29:11 Distribution flywheel in the real world30:22 Team distribution vs product debate32:32 2026 is the scaling year34:02 Community platform finally clicking36:09 Building the community platform the hard way39:20 Scaling clients, POCs, and production41:09 Why mom and pops matter41:55 Energy demand tailwinds and macro impact44:44 One word answer for next year: scale45:20 POC to production cycle time focus47:12 Scaling tech, sales, and financing49:45 Moving at AI speed story50:14 Raising capital and building serious software52:56 Collide as the operator layer vision54:02 Gratitude and community over everythinghttps://twitter.com/collide_iohttps://www.tiktok.com/@collide.iohttps://www.facebook.com/collide.iohttps://www.instagram.com/collide.iohttps://www.youtube.com/@collide_iohttps://bsky.app/profile/digitalwildcatters.bsky.socialhttps://www.linkedin.com/company/collide-digital-wildcatters

Datawizz is pioneering continuous reinforcement learning infrastructure for AI systems that need to evolve in production, not ossify after deployment. After building and exiting RapidAPI—which served 10 million developers and had at least one team at 75% of Fortune 500 companies using and paying for the platform—Founder and CEO Iddo Gino returned to building when he noticed a pattern: nearly every AI agent pitch he reviewed as an angel investor assumed models would simultaneously get orders of magnitude better and cheaper. In a recent episode of BUILDERS, we sat down with Iddo to explore why that dual assumption breaks most AI economics, how traditional ML training approaches fail in the LLM era, and why specialized models will capture 50-60% of AI inference by 2030. Topics Discussed Why running two distinct businesses under one roof—RapidAPI's developer marketplace and enterprise API hub—ultimately capped scale despite compelling synergy narratives The "Big Short moment" reviewing AI pitches: every business model assumed simultaneous 1-2 order of magnitude improvements in accuracy and cost Why companies spending 2-3 months on fine-tuning repeatedly saw frontier models (GPT-4, Claude 3) obsolete their custom work The continuous learning flywheel: online evaluation → suspect inference queuing → human validation → daily/weekly RL batches → deployment How human evaluation companies like Scale AI shift from offline batch labeling to real-time inference correction queues Early GTM through LinkedIn DMs to founders running serious agent production volume, working backward through less mature adopters ICP discovery: qualifying on whether 20% accuracy gains or 10x cost reductions would be transformational versus incremental The integration layer approach: orchestrating the continuous learning loop across observability, evaluation, training, and inference tools Why the first $10M is about selling to believers in continuous learning, not evangelizing the category GTM Lessons For B2B Founders Recognize when distribution narratives mask structural incompatibility: RapidAPI had 10 million developers and teams at 75% of Fortune 500 paying for the platform—massive distribution that theoretically fed enterprise sales. The problem: Iddo could always find anecdotes where POC teams had used RapidAPI, creating a compelling story about grassroots adoption. The critical question he should have asked earlier: "Is self-service really the driver for why we're winning deals, or is it a nice-to-have contributor?" When two businesses have fundamentally different product roadmaps, cultures, and buying journeys, distribution overlap doesn't create a sustainable single company. Stop asking if synergies exist—ask if they're causal. Qualify on whether improvements cross phase-transition thresholds: Datawizz disqualifies prospects who acknowledge value but lack acute pain. The diagnostic questions: "If we improved model accuracy by 20%, how impactful is that?" and "If we cut your costs 10x, what does that mean?" Companies already automating human labor often respond that inference costs are rounding errors compared to savings. The ideal customers hit differently: "We need accuracy at X% to fully automate this process and remove humans from the loop. Until then, it's just AI-assisted. Getting over that line is a step-function change in how we deploy this agent." Qualify on whether your improvement crosses a threshold that changes what's possible, not just what's better. Use discovery to map market structure, not just validate hypotheses: Iddo validated that the most mature companies run specialized, fine-tuned models in production. The surprise: "The chasm between them and everybody else was a lot wider than I thought." This insight reshaped their entire strategy—the tooling gap, approaches to model development, and timeline to maturity differed dramatically across segments. Most founders use discovery to confirm their assumptions. Better founders use it to understand where different cohorts sit on the maturity curve, what bridges or blocks their progression, and which segments can buy versus which need multi-year evangelism. Target spend thresholds that indicate real commitment: Datawizz focuses on companies spending "at a minimum five to six figures a month on AI and specifically on LLM inference, using the APIs directly"—meaning they're building on top of OpenAI/Anthropic/etc., not just using ChatGPT. This filters for companies with skin in the game. Below that threshold, AI is an experiment. Above it, unit economics and quality bars matter operationally. For infrastructure plays, find the spend level that indicates your problem is a daily operational reality, not a future consideration. Structure discovery to extract insight, not close deals: Iddo's framework: "If I could run [a call where] 29 of 30 minutes could be us just asking questions and learning, that would be the perfect call in my mind." He compared it to "the dentist with the probe trying to touch everything and see where it hurts." The most valuable calls weren't those that converted to POCs—they came from people who approached the problem differently or had conflicting considerations. In hot markets with abundant budgets, founders easily collect false positives by selling when they should be learning. The discipline: exhaust your question list before explaining what you build. If they don't eventually ask "What do you do?" you're not surfacing real pain. Avoid the false-positive trap in well-funded categories: Iddo identified a specific risk in AI: "You can very easily run these calls, you think you're doing discovery, really you're doing sales, you end up getting a bunch of POCs and maybe some paying customers. So you get really good initial signs but you've never done any actual discovery. You have all the wrong indications—you're getting a lot of false positive feedback while building the completely wrong thing." When capital is abundant and your space is hot, early revenue can mask product-market misalignment. Good initial signs aren't validation if you skipped the work to understand why people bought. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Welcome to a special end-of-the-year series on Making Risk Flow as we count down the weeks to the end of 2025. Each Tuesday, we will re-release one standout episode as we build up to releasing our top fan favourite on the last Tuesday. In this episode, Juan de Castro is joined by his colleagues, Rich Lewis, Cytora's Sales Director, and Zaheer Hooda, Head of North America, for a deep dive into what makes proof-of-concept (POC) initiatives in risk digitisation succeed or fail.Drawing on firsthand experience from working with leading carriers, they break down five essential capabilities insurers need to get right when implementing digitisation initiatives, from extraction accuracy and full-spectrum intake handling to scalable deployment and human-in-the-loop exception management.They also provide a practical, inside look at how insurers structure effective proof of concept processes, including live workshops, data preparation, success metrics, and how to align POC design with measurable business outcomes.Whether you're revisiting the episode or viewing it for the first time,  this episode offers tactical guidance to ensure your technology investments deliver meaningful impact.Fan Mail: Got a challenge digitizing your intake? Share it with us, and we'll unpack solutions from our experience at Cytora.To receive a custom demo from Cytora, click here and use the code 'Making Risk Flow'.Our previous guests include: Bronek Masojada of PPL, Craig Knightly of Inigo, Andrew Horton of QBE Insurance, Simon McGinn of Allianz, Stephane Flaquet of Hiscox, Matthew Grant of InsTech, Paul Brand of Convex, Paolo Cuomo of Gallagher Re, and Thierry Daucourt of AXA.Check out the three most downloaded episodes: The Five Pillars of Data Analytics Strategy in Insurance | Craig Knightly, Inigo 20 Years as CEO of Hiscox: Personal Reflections and the Evolution of PPL | Bronek Masojada Implementing ESG in the Insurance and Underwriting Space | Simon Tighe, Chaucer, and Paul McCarney, Moody's

“Tecnologia pela tecnologia tem que morrer. Área de tecnologia que não pensa em entregar valor pro negócio ou pro usuário final, ela tende a morrer. Você não deveria gastar dinheiro por gastar dinheiro” No décimo quinto episódio do Hipsters.Talks, PAULO SILVEIRA, CVO do Grupo Alun, conversa com ANATERRA OLIVEIRA, CTO da DASA, sobre inovação aberta, parcerias com startups e por que experiência do usuário é mais importante que tecnologia sofisticada. Uma conversa sobre o dia a dia de quem lidera tecnologia em uma das maiores empresas de saúde do Brasil. Prepare-se para um episódio cheio de conhecimento e inspiração!

Svetlana Zavelskaya, Head of Software Engineering for Data Platform and Infrastructure at Quanata, joins the show to unpack what it really takes to make the “impossible” possible in tech. From re-architecting a startup codebase to scaling innovation inside an insurance giant, she shares how her team turns complex R&D challenges into production-ready systems. This conversation dives deep into engineering discipline, AI tool adoption, and why the next wave of insurance innovation is powered by data and software.Key Takeaways• Real innovation often means balancing speed with long-term architecture decisions• AI coding tools are valuable for exploration but need governance and clear security guardrails• POCs fail when expectations aren't aligned, not because the tech doesn't work• Insurance tech is evolving fast through telematics and context-based data models• Well-structured, well-documented code is still the foundation for scalable innovationTimestamped Highlights00:33 How telematics is changing the economics of insurance and rewarding better drivers03:59 Cars as software platforms and what that means for data privacy and innovation06:02 The growing pains of re-architecting an organically built startup codebase08:38 Evaluating new AI tools and maintaining data security across teams11:08 Why most AI POCs never make it to production16:29 How Quanata's R&D work feeds into State Farm's larger technology initiatives20:40 Safe-driving challenges, behavioral change, and saving lives with dataA Thought That Stuck“If we can prevent just 1 percent of drivers in the world from using their phone behind the wheel, imagine how many lives we can save.”Pro Tips• Before starting a POC, define if it's an experiment or a potential product foundation• Let engineers explore new tools but build frameworks to govern how data and results are handledCall to ActionIf you enjoy exploring how data, AI, and engineering innovation come together to solve real-world problems, follow The Tech Trek on Apple Podcasts or Spotify and share this episode with a colleague who builds at the edge of what's possible.

Madhavan Ramanujam is the world's foremost expert on pricing and monetization strategy. As managing partner at Simon-Kucher, he helped over 250 companies, including 30 unicorns, architect their pricing strategies. He's the author of the definitive book on pricing, Monetizing Innovation. Now he's back with a sequel, Scaling Innovation, which reveals how to build enduring businesses by dominating both market share and wallet share. He recently left Simon-Kucher to launch his own fund, 49 Palms, focused on helping early-stage AI companies.In this conversation, we discuss:1. The 2x2 framework that identifies your optimal pricing model2. Why AI companies can capture 25% to 50% of value created, vs. 10% to 20% for traditional SaaS products3. Why popular AI coding tools may have already doomed themselves with underpricing4. The “give-and-get” framework top negotiators use to extract maximum value from every deal5. The negotiation strategy that helped one founder 4x their deal size overnight6. How to frame POCs as “business case creation” instead of technical demos (and why this changes everything)7. Why AI companies must get monetization right from day one—not “figure it out later”8. How companies like Intercom's Fin and Sierra pioneered outcome-based pricing (charging $0.99 per AI resolution)9. The single question that reveals if your pricing is too complex—Brought to you by:Enterpret—Transform customer feedback into product growth: https://enterpret.com/lennyDX—A platform for measuring and improving developer productivity: https://getdx.com/lennyPersona—A global leader in digital identity verification: https://withpersona.com/lenny—Transcript: https://www.lennysnewsletter.com/p/pricing-and-scaling-your-ai-product-madhavan-ramanujam— My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/168109183/my-biggest-takeaways-from-this-conversation—Where to find Madhavan Ramanujam:• X: https://x.com/madhavansf• LinkedIn: https://www.linkedin.com/in/madhavansf/• Promo email for Scaling Innovation: promo@49palmsvc.com — If you're purchasing more than five copies, send a screenshot of your receipt to enter Madhavan's exclusive bundle raffle.—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Madhavan and his work(04:30) The core thesis of Scaling Innovation(09:20) Common traps founders fall into(12:06) Beautifully simple pricing(15:00) Mastering negotiations(26:51) Other strategies for effective pricing and monetization(27:35) How AI pricing is different(31:33) Handling POCs(36:25) The importance of mastering monetization(38:58) Choosing the right AI pricing model(43:13) Current trends in AI pricing(44:48) Strategizing for outcome-based models(50:23) Packaging strategies for scaling(51:37) Adapting pricing strategies over time(53:40) Key axioms for pricing success(58:00) Takeaways for founders(01:01:33) Lightning round and final thoughts—Referenced:• The art and science of pricing | Madhavan Ramanujam (Monetizing Innovation, Simon-Kucher): https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan• Cursor: https://www.cursor.com/• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Sierra Finn: http://www.sierrafinn.com/• Chargeflow: https://www.chargeflow.io/• GitHub: https://github.com/• Intercom: https://www.intercom.com/• Warren Buffett's quote: https://www.goodreads.com/quotes/11478913-if-you-ve-got-the-power-to-raise-prices-without-losing• Sierra: https://sierra.ai/• Clay Bavor on LinkedIn: https://www.linkedin.com/in/claybavor/• Mission: Impossible—The Final Reckoning: https://www.imdb.com/title/tt9603208/• Delphi: https://www.delphi.ai/• Dara Ladjevardian on LinkedIn: https://www.linkedin.com/in/dara-ladjevardian/• Sam Spelsberg on LinkedIn: https://www.linkedin.com/in/samuel-spelsberg/• Lennybot: https://www.lennybot.com/• Granola: https://www.granola.ai/• Simon-Kucher: https://www.simon-kucher.com/• Josh Bloom on LinkedIn: https://www.linkedin.com/in/joshuabloompricingconsulting/—Recommended books:• Monetizing Innovation: How Smart Companies Design the Product Around the Price: https://www.amazon.com/Monetizing-Innovation-Companies-Design-Product/dp/1119240867• Scaling Innovation: How Smart Companies Architect Profitable Growth: https://www.amazon.com/dp/1119633060• Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers: https://www.amazon.com/Business-Model-Generation-Visionaries-Challengers/dp/0470876417• Thinking Fast and Slow: https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555/• Contagious: Why Things Catch On: https://www.amazon.com/Contagious-Things-Catch-Jonah-Berger/dp/1451686587/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com