Tech Talks Daily

This week has reminded me why I love what I do. I have spoken with people from the US, China, Dubai, Bulgaria, and South Africa, and even discovered that one of this show’s regular listeners had made the journey from the Netherlands to be here at GITEX Global. Over five sessions on the AI Stage, I have covered everything from autonomous cars to how AI could help the UN World Food Programme tackle hunger. We have explored how Serbia achieved tenfold growth through AI and how new tools can now verify misinformation simply by checking a video link. But beneath all the tech talk, what has stood out most to me is how technology connects people. In a world that often feels divided, it is refreshing to see how collaboration and shared curiosity can still bridge cultures and spark ideas. It is those moments, when technology brings people together, that remind me why this podcast exists. That spirit of connection is exactly what inspired my quick chat with Iancho Dimitrov from Living Homes, a company based in Dubai but shaped by global perspectives. Originally from Bulgaria, Iancho and his team are building what they call an “AI-native intelligent home,” a home that does not just automate switches but truly understands its inhabitants. From monitoring wellbeing and improving sleep to creating safe, supportive spaces for families across generations, Living Homes is redefining what it means to live smart. So while this conversation might be short, it captures something powerful. It is proof that innovation is not only about hardware or software, it is about empathy, understanding, and the shared drive to build a better way of living. In a week where the world gathered in one city to imagine the future, Iancho’s vision is a reminder that technology works best when it feels human.

What if the next generation of computing was not something you held or wore, but something you looked through? In this special episode recorded live from GITEX Global in Dubai, I speak with Roman Axelrod, founder of EXPANCEO, a deep tech company creating AI-powered smart contact lenses designed to merge augmented reality, biosensing, and what he calls digital superpowers. Roman explains how his company moved from an ambitious idea to becoming the first deep tech unicorn in the Gulf region, now valued at more than 1.3 billion dollars. Over the past five years, his team of physicists and engineers in Dubai has built more than fifteen prototypes and secured a wide range of patents, all aimed at developing what they see as the ultimate interface for AI-driven computing. These lenses can display digital images, measure biological signals such as glucose and intraocular pressure, and may one day eliminate the need for screens altogether. He reflects on the early days of disbelief, when even friends told him to give up, and how perseverance became the deciding factor. For Roman, success meant proving that deep tech innovation is possible outside Silicon Valley. He shares how Dubai’s ecosystem, low taxation, and access to world-class talent helped make that vision real. His story offers practical insight for founders who are told their ideas are impossible until they can show a working prototype. We also explore what this means for the future of human-computer interaction. Roman believes these lenses will help us communicate directly with intelligent systems, turning science fiction into everyday life. His message to entrepreneurs is simple: be stubborn, stay curious, and keep building. Could AI contact lenses redefine computing itself? Listen to the conversation and share your thoughts.

What if the real breakthrough in AI isn’t the model itself, but the data that gives it knowledge? In this episode of Tech Talks Daily, I sit down with Edo Liberty, founder and Chief Scientist of Pinecone, to unpack how vector databases have quietly become the backbone of modern AI infrastructure. We explore why retrieval-augmented generation (RAG) works so effectively out of the box, and why fine-tuning large models often adds complexity without real-world value. Edo shares how Pinecone’s research revealed that different models—from OpenAI to Anthropic—require differently structured context to perform well, a discovery that’s reshaping how enterprises think about AI implementation. As the former Director of Research at Yahoo and AWS, Edo offers a grounded perspective on where the real innovation is happening. He explains how the shift from traditional data structures to vector representations is redefining how machines “know” and retrieve information, creating smarter, context-aware systems. We also touch on his recent transition to Chief Scientist, his excitement for returning to hands-on research, and why he believes the convergence of AI and data represents the defining technological shift of our lifetime. So, what does it mean for developers, business leaders, and anyone building with AI when knowledge becomes an accessible layer of infrastructure? Can we build systems that truly “know” as humans do? Join the conversation, and after listening, I’d love to hear your thoughts—do you think the future of AI lies in the models or in the data that feeds them?

What happens when an AI strategy meets the real-world complexity of healthcare, law, and finance? That’s the challenge at the heart of my conversation with Mark Sherwood, CIO of Wolters Kluwer, a global leader in professional information services. With over three decades in technology leadership across Microsoft, Symantec, and Nuance, Mark brings a rare combination of enterprise depth and hands-on pragmatism to the AI discussion. Mark explains why cloud-native architecture and data governance are the twin foundations of trustworthy AI. He shares how Wolters Kluwer is embedding AI across highly regulated industries—from helping doctors access life-saving insights through natural language queries to giving tax and legal professionals faster, more accurate guidance on complex regulations. Behind the innovation lies a disciplined approach: governing data, managing risk, and building confidence in AI systems that must meet the highest standards of accuracy and compliance. We also explore how to build high-trust, low-friction partnerships between IT and business teams to prevent shadow IT while accelerating digital transformation. Mark offers candid insights into the rise of AI agents, the emerging risks of quantum security, and why he believes that high-quality data is the most valuable currency in digital transformation. His philosophy is simple: speed means nothing without trust, and trust starts with clean, well-governed data. From cloud transformation to the future of AI regulation, this episode offers a grounded look at how global enterprises can scale responsibly in an era where innovation often outruns policy. So as AI becomes inseparable from how professionals think and work, how do we balance speed with stewardship? And are we truly ready for the ethical, technical, and quantum frontiers ahead? Share your thoughts after the episode.

What does it take to build AI that enterprises can actually trust? That’s the question I explored with Nirankush “Kush” Panchbhai, Senior Vice President of Platform Fundamentals at ServiceNow, in a conversation about AI governance, human-centered design, and how the company’s AI Control Tower is reshaping enterprise adoption. Kush describes the AI Control Tower as an “air traffic controller” for AI agents, a central command center that provides visibility, accountability, and governance across every part of an organization’s AI ecosystem. It embeds compliance, legal, and risk workflows directly into the development process, replacing endless approval cycles with automated guardrails that accelerate innovation rather than slow it down. The result is a system where humans remain firmly in control, supported by transparent, explainable AI that acts as a teammate rather than a tool. We also discuss how ServiceNow is helping enterprises move beyond the “POC palooza” of pilot projects that never scale. By treating AI agents as members of a digital workforce—with performance tracking, retraining, and measurable ROI—companies can finally connect investment to real outcomes. Governance, in this context, isn’t a constraint; it’s a catalyst for confidence and adoption. At its core, ServiceNow’s philosophy is about taking the work out of work, not the human out of work. From password resets to process automation, AI is freeing employees to focus on creative, high-value problem-solving while building trust through transparency and accountability. As organizations begin managing both human and digital workforces, one question lingers: can AI governance truly become the accelerator that turns trust into enterprise-scale transformation? And what does it take to ensure AI always serves people, not the other way around? Share your thoughts after the episode.

What if the biggest sustainability challenge in tech isn’t hardware or cloud emissions, but the invisible mountain of unused data sitting in storage? That’s the question driving my conversation with Piero Gallucci, Vice President and General Manager for NetApp UK and Ireland, as we discuss how single-use data is quietly shaping the environmental and financial footprint of enterprise IT. Piero explains that 38 percent of stored data is never used again, yet it continues to consume energy and resources indefinitely. He describes how this digital hoarding—often driven by regulatory caution and the overvaluation of data—has become one of the most overlooked contributors to emissions in modern infrastructure. With the rise of AI accelerating data growth by an estimated 50 percent, the challenge is no longer simply about capacity but responsibility. Through examples such as Aston Martin Formula One and the NFL, Piero outlines how NetApp is helping organizations identify unused data, automate lifecycle policies, and design intelligent, energy-efficient infrastructure that supports both innovation and sustainability. We also explore the tension between AI adoption and environmental impact. As enterprises rush to train new models, Piero argues that smarter data governance, not bigger datasets, is the key to sustainable AI. He highlights the importance of educating teams on the true cost of data—both financial and environmental—and why leaders must build intentional strategies that align performance with purpose. NetApp’s vision is clear: make data management as sustainable as it is powerful. But as AI reshapes how we store and use information, can the tech industry finally balance digital growth with environmental stewardship? And what would your company look like if every byte of data had to justify its existence? Share your thoughts after the episode.

What if the next big leap in business AI isn’t generative at all, but predictive? That’s the question at the heart of my conversation with Zohar Bronfman, CEO and co-founder of Pecan AI, a company helping business teams forecast outcomes with precision and turn historical data into future insights. Zohar explains why he believes predictive AI will deliver far greater enterprise value than the generative models dominating headlines. He points to research showing that most generative AI projects fail to produce ROI, while predictive systems built on a company’s own data can directly improve revenue, reduce churn, and guide smarter decisions. With Pecan’s no-code platform, marketing and operations teams can now create predictive models without needing data scientists—bridging the gap between technical expertise and business execution. Through stories like Little Spoon’s, a direct-to-consumer baby food brand that used Pecan AI to identify and retain at-risk customers, Zohar illustrates how predictive analytics turns data into real business impact. He also shares common mistakes companies make when implementing AI—starting with unclear objectives and misaligned resources—and why success depends on defining the problem before choosing the tool. Looking ahead, Zohar envisions predictive AI as the backbone of every organization, shifting business intelligence from reactive analysis to proactive action. As companies move beyond dashboards and toward dynamic decision-making, predictive insights may soon become as fundamental as spreadsheets. So, if your company could anticipate every challenge before it happened, how different would your strategy look? And are business leaders finally ready to treat predictive AI as core infrastructure rather than a passing trend? Share your thoughts after the episode.

What happens when artificial intelligence meets the everyday heroes of local government? That’s the question driving my conversation with Justin Dennis, co-founder and COO of Urban SDK, a geospatial AI company helping more than 250 North American cities make faster, safer, and smarter decisions. Justin shares how a Smart Cities Challenge from the U.S. Department of Transportation inspired him to co-found Urban SDK in 2018, and why he believes the future of public safety depends on replacing manual data collection with real-time intelligence. From traffic fatalities to hurricane recovery, he explains how the company’s HALO platform gives local leaders and emergency responders the insights they need to act before crises escalate. In a single platform, they can identify dangerous road zones, predict high-risk intersections, coordinate clean-up operations, and rebuild infrastructure based on data rather than guesswork. We also explore how AI is quietly reshaping government operations, from disaster management to traffic enforcement. Justin discusses the challenges of introducing cutting-edge technology into systems that still rely on spreadsheets and siloed workflows. Yet his optimism is clear. He believes governments are beginning to embrace AI not as a buzzword but as a practical tool to save time, resources, and lives. As one Florida community recently reported a 40 percent drop in traffic fatalities, the impact is already measurable. Urban SDK’s story is about technology meeting public service with purpose. So as we enter another year of rapid AI progress, how can data-driven insights continue to empower local leaders to protect citizens and improve quality of life? And what could your city achieve if every decision were powered by real-time intelligence? Share your thoughts after the episode.

Two years after our last conversation, Raj Koneru, CEO and Founder of Kore.ai, returns to discuss how the world of AI has changed and how much of it still needs to. When we first spoke, conversational AI was promising. Now it is powering over a billion interactions every day for companies like LG, Coca-Cola, and Blue Cross Blue Shield. Yet Raj argues that the next real breakthrough will not come from novelty, but from accessibility. In this episode, Raj explains why the future of AI depends on open collaboration rather than vendor lock-in. Kore.ai’s partnerships with Microsoft, AWS, and G42’s Inception in the UAE reflect a commitment to interoperability and shared innovation. He offers a rare look into what happens “above the line,” where enterprises actually design and deploy AI agents, compared to the massive “below the line” investments driving the hardware, cloud, and model layers of AI. For Raj, platforms like Kore.ai act as the bridge, translating technical potential into business outcomes. We also explore what true democratization of AI looks like in practice. Raj believes no-code platforms are key to giving both large and small businesses the power to build their own agents without deep technical skills. He discusses the challenges of scaling responsibly, managing latency, ensuring governance, and keeping AI secure and transparent. From the shift toward on-device AI in smartphones to the lessons learned from running one of the world’s largest enterprise AI platforms, his perspective blends realism with optimism. This conversation is a reminder that progress in AI will not be defined by who owns the biggest model but by who makes the technology usable, ethical, and open to everyone. Raj’s message is simple but powerful: read widely, question everything, and collaborate boldly.

In this episode, Mike Baker, Vice President and Global CISO at DXC Technology, says the cyber industry has been focusing on the wrong side of AI. He believes too many companies use it only to block threats instead of studying how criminals use it to scale phishing, bypass defenses, and deploy adaptive malware. Attackers are learning faster than ever, and security teams must catch up. Mike argues that defenders need to think differently and use AI as both protection and opportunity. He shares how DXC is already doing this. The company has brought autonomous AI agents into its security operations through a partnership with 7AI. These agents process alerts that used to require hours of human effort. The result is faster detection, less burnout, and more time for analysts to investigate real threats. By cutting manual work by more than eighty percent, DXC has shown how AI can make cybersecurity teams stronger, not smaller. Zero Trust remains a core part of DXC’s strategy. Mike calls it a journey that never ends. It needs cultural change, constant learning, and leadership that keeps security invisible to end users. AI now plays a role here too, improving identity checks and spotting access issues in real time. Yet, he reminds us, AI still needs people in the loop for oversight and judgment. We also talk about supply chain risks. Too many companies still treat risk assessments as one-time tasks. Mike pushes for continuous monitoring and close collaboration with suppliers. He closes the conversation on a hopeful note. AI will not replace people in cybersecurity, he says. It will make their work more meaningful and more effective if used with care and common sense.

What happens when the future of teamwork collides with the power of AI? That’s the question at the heart of this episode as Tiffany from Atlassian joins me from Barcelona during Team 25, where Atlassian is showcasing how AI-powered collaboration is redefining how work gets done. We talk about how Atlassian’s mission to unleash the potential of every team is coming to life through its bold decisions, from sunsetting data center products to expanding its multi-cloud partnerships with Google. Tiffany offers a front-row view of how Atlassian’s evolving cloud platform is designed to help customers work smarter while enabling secure, scalable innovation across some of the world’s most complex enterprises. The conversation also uncovers the thinking behind the teamwork graph, Atlassian’s powerful data intelligence layer that connects billions of work objects to create truly personalized AI experiences. Tiffany shares how companies like Royal Caribbean and Mercedes-Benz are already seeing measurable performance gains and how AI is becoming a real teammate that unifies knowledge, connects tools, and drives better outcomes. We discuss what it means to build a “system of work,” why flexibility and context matter, and how Atlassian’s open approach allows teams to build custom systems tailored to their own culture and workflows. Beyond the technology, this is a story about continuous learning, adaptability, and human-centered progress. Tiffany’s reflections on learning from the toughest customers, embracing change, and reimagining the browser as an active workspace reveal how Atlassian is blending AI with empathy and purpose. As AI becomes inseparable from teamwork, what steps will you take to unleash what your team can do next? I’d love to hear your thoughts.

As AI tools race into every corner of software development, a simple question keeps coming back to me. Will AI replace human testers, or will it force us to rethink what great testing looks like in the first place. In today’s conversation, I talk with Santiago Komadina Geffroy, a Software Engineer at Jalasoft and an educator with Jala University, about what changes, what stays, and what teams should do next. Santiago shares how his day job and teaching intersect. He points to a gap he sees often. Engineers are experimenting with large language models without fully understanding how they work, which leads to overconfidence and avoidable rework. He argues for clearer interaction patterns between tools and people. Think less about magic prompts and more about protocols, context sharing, and agent to agent collaboration. That shift frees testers to do the thinking work that AI still struggles with, from exploratory testing and usability judgment to spotting the weird edge cases that only show up when real humans use real products. We also get into bias and ethics. AI is only as fair as the data it learns from, and that matters in healthcare, finance, and hiring where a mistake can carry life changing consequences. Santiago calls for stronger education around data quality, authorship, privacy, and environmental impact, not as a side note but as part of how engineers are trained. He believes governance helps teams move faster with fewer regrets when they take AI into production. Security sits in the mix too. Many AI tools need deep system access. If compromised, they can distort results or leak sensitive information. Santiago is candid about the limits of any single safeguard. He recommends a culture of shared responsibility where engineers understand when to call in security specialists and how to design workflows that keep humans in the loop for consequential decisions. We close with what Jalasoft has learned from building with AI inside a nearshore model in South America. More thinking time. Smaller, controllable scopes. Clear lines between routine automation and human judgment. The headline is simple. AI will change testing. Human testers will remain at the heart of quality.

Here’s the thing. Most of us still picture a hotel lobby with a counter, a queue, and someone typing furiously while we wait after a long flight. In this episode, I sit with Richard Valtr, founder of Mews, to ask whether that scene is quietly fading. Backed by Tiger Global, Goldman Sachs, and Battery Ventures, Mews recently raised 75 million dollars to scale an AI-powered platform that already processes more than 10 billion dollars in payments each year. Richard argues the real bottleneck in hospitality isn’t software. It’s mindset. If hotels rethought workflows around guests rather than systems, the front desk would feel less like a checkpoint and more like a welcome. Richard shares the origin story of building for hoteliers as well as guests, and why the property management system should function like a central nervous system. He explains how automation handles the repetitive pieces of check-in so staff can actually look people in the eye and start a conversation. That’s the promise of AI here. Not gimmicks, but orchestration across bookings, payments, inventory, and service so the boring parts disappear into the background and the human parts come forward. We also talk about underused tech. Richard uses a memorable comparison for many hotel platforms that have Ferrari-level capability but get driven like Volvos. The data is there. The intent to serve is there. What’s missing is the leadership confidence to rewire the stack, measure outcomes, and keep pushing. When that happens, hotels stop thinking only in terms of rooms and start monetizing the full journey. Daybeds, coworking passes, last-mile upgrades, spa time after back-to-back meetings. AI can surface the right offer at the right moment without turning the experience into a sales pitch. By the end, Richard paints a picture of hospitality where screens fade, transactions happen on the guest’s time, and every interaction feels more personal precisely because the admin has been taken out of the way. If you want a grounded view of how AI will change hotels without stripping away the reason we love staying in them, this conversation is a helpful place to start.

When a company quietly builds world-class storage and virtualization software for twenty years, it usually means they have been too busy solving real problems to shout about it. That is what makes euroNAS and its founder, Tvrtko Fritz, such an interesting story. In this episode, I reconnect with Tvrtko after meeting him on the IT Press Tour in Amsterdam to learn how his company evolved from “NAS for the masses” into a trusted enterprise alternative in a market filled with bigger names. Tvrtko shares how euroNAS began with a simple idea that administrators should not have to battle complex infrastructure to keep systems running. Over time, that belief shaped a complete platform covering hyper-converged virtualization, Ceph-based storage, and instant backup and recovery. He recalls the story of a dentist who lost a full day of work waiting for a slow restore, which inspired euroNAS to create instant recovery that restores in seconds rather than hours. We also discuss how their intuitive graphical interface has turned Ceph from a daunting project that once took a week to set up into something that can be configured in twenty minutes. That change has opened advanced storage to universities, managed service providers, and enterprises handling petabyte-scale workloads. We also tackle a topic that many in IT are thinking about right now: VMware. With licensing changes frustrating customers, Tvrtko explains how euroNAS has become the quiet plan B for many organizations seeking stability and control. Its perpetual per-node licensing model removes the pressure of forced subscriptions, while tools such as the VM import wizard make migration faster and less painful. What stands out most is that Tvrtko still takes part in customer support himself, using real conversations to guide product development and keep the company close to the people who depend on it. Looking ahead, Tvrtko outlines how euroNAS is growing through partnerships with major hardware vendors and through its expanding role in AI infrastructure, where demand for scalable storage continues to rise. The conversation highlights the value of engineering-led companies that build with care, focus on reliability, and give customers genuine ownership of their systems. If you want to understand what practical innovation looks like in enterprise storage, this episode will remind you why simplicity still wins.

AI hype has been loud for three years, but most leaders still tell me the real work begins after the demo. That was the starting point for my conversation with Christina Ellwood, co-founder of AI Realized, a community built to help enterprises move from pilots to production with less noise and more results. Christina has a calm, practical way of explaining why progress has accelerated from a tiny fraction of companies in production to roughly one in five this year, and why many of the remaining blockers have little to do with model choice and everything to do with people, policy, and permission to ship. We talk about the messy middle between a proof of concept and a live service that customers can rely on. According to Christina, the most complex problems are organizational. Teams need upskilling, guardrails, and clear deployment guidelines to ensure effective execution. Legal and brand risk create hesitation. Boards want more substantial evidence and better controls. That is where leadership shows up in a very human way. The skill she hears most often from successful program leads is humility. No one knows everything here, and the leaders who admit that, invite challenge, and keep learning are the ones getting to value without creating chaos. I loved her point that cross-organisational leadership is fast becoming the hidden superpower as AI connects systems and workflows that used to sit in separate silos. We also look forward to the 2025 AI Realized Summit, scheduled for November 5 in San Francisco. Attendance is intentionally capped at 500 to maintain high-quality conversation and genuine networking. Expect Fortune 2000 use cases across multiple industries, a healthy mix of predictive and generative work, and practical talk on small language models, multi-model strategies, and running models inside your security perimeter. Eric Siegel will keynote on combining predictive analytics with generative techniques, and you will hear from executives at companies including Amazon, Audible, Red Hat, and Zscaler. Christina highlights one example from Fandom that combines predictive ad targeting with generative tools to enhance brand safety and suitability, a trend I expect to see repeated throughout the day. If you are leading AI programs and need fewer slogans and more proof, this episode will feel like a deep breath. We explore how to move faster while staying responsible, why smaller and multi-model setups are gaining traction, and how to build confidence with your board without overpromising.

Finance leaders know the struggle of managing endless spreadsheets, juggling data from every corner of the business, and trying to plan for a world that changes by the hour. In this episode, I talk with Julio Martínez, Co-Founder and CEO of Abacum, about how his team is helping finance professionals move from reactive reporting to confident, real-time decision making. Abacum was recently named the fastest growing tech company in Spain by Deloitte after increasing revenue by 6,733 percent in just four years. Julio shares the story behind that growth and explains how finance teams are transforming from back-office operators into true strategic partners. He describes how Abacum’s platform helps CFOs and FP&A teams create accurate forecasts, automate manual work, and build scenario models that answer “what if” questions in minutes instead of days. We also talk about the role of AI in finance and why current large language models are not yet reliable enough for quantitative use cases. Julio discusses the need for precision, the importance of a human in the loop, and how new hybrid approaches are shaping the future of financial planning. From Barcelona to New York, his journey reflects the global rise of data-driven finance and the growing strength of Spain’s startup ecosystem. Julio also leaves listeners with a thoughtful recommendation, Meditations by Marcus Aurelius, a book that continues to inspire him to stay grounded amid rapid change. If you want to understand how technology is redefining financial planning and how strong foundations can fuel extraordinary growth, this conversation with Julio offers a rare look inside the engine of one of Europe’s fastest-rising tech companies.

What happens when a CTO and a CIO of a global tech company sit down together to talk about AI? That’s the starting point of today’s episode, where I’m joined by Jeremy Ung, CTO at Blackline, and Sumit Johar, the company’s CIO. Rather than chasing the hype, we focus on what AI really means for executive decision making, governance, and business outcomes. Both leaders open up about how their partnership is blurring the traditional lines between product and IT, and why the board is demanding answers on topics that once sat deep in the technology stack. Jeremy and Sumit explain why AI is not just another SaaS subscription and why expectations have changed so dramatically. For decades, technology was seen as predictable, a rules-based engine that followed instructions without error. AI feels different because it speaks, reasons, and sometimes makes mistakes. That human-like experience is what excites employees, but it is also what unsettles them. This is where education and governance come in, helping teams learn how to question, verify, and trace AI outputs before they make critical decisions. We also explore how AI agents are beginning to work across tools like SharePoint and email, raising new compliance and security questions that CIOs and CTOs must answer together. The conversation turns to AI sprawl, a problem that mirrors the SaaS explosion of a decade ago. With new AI tools emerging every week, enterprises risk overlapping investments and fragmented initiatives. Sumit shares how Blackline uses two governance councils to keep projects aligned. One is dedicated to risk, pulling in voices from legal, security, and privacy. The other is focused on transformation, evaluating whether requests for new AI capabilities make sense, or whether they duplicate what already exists. The signal that sprawl is taking root, he says, is when requests for tools suddenly jump from a few each month to a dozen. We also tackle the build versus buy dilemma. Budgets haven’t magically increased just because AI is hot. Jeremy argues that building only makes sense when it reinforces a company’s core advantage. Everything else should be bought, integrated, and kept flexible so that organizations can pivot as the AI landscape changes. Both leaders stress that trust, auditability, and value delivery must sit at the center of every investment decision.

Zeta Global’s CTO, Chris Monberg talks about building AI that helps brands grow with repeatable, scalable programs without losing the spark that makes a brand feel human. Zeta’s promise is simple to say and hard to do. Help marketers deliver better results with less waste by pairing strong data, clear identity, and practical AI inside the Zeta Marketing Platform. What stood out first was Chris’s view of design as a contact sport. He hires builders who live in the work, and he still enjoys rolling up his sleeves himself. That mindset shows up in how Zeta approaches AI for marketing. Rather than shouting for the next click, he wants systems that perceive intent and context. He described an early lesson from retail floors in Seattle. The best experience came from people who noticed a customer’s posture and pace before speaking. Empathetic design translates that awareness into algorithms that understand latent signals and respond with care, not noise. We also dug into a tension many leaders feel. Automation is exciting, but nobody wants generic content. Chris answered with a practical frame. Give marketers a way to create a personal “super agent” that learns from their choices, their brand voice, and the paths they take through the platform. Offload the repetitive chores, keep creative control, and grow pride of ownership. That pride matters because it breeds adoption. When teams feel the system reflects them, they keep using it and keep improving it. Another thread was trust. In Chris’s words, the market still underestimates what these tools can do, partly because users are unsure where the value comes from. Zeta is leaning into transparency so teams can see how decisions are made and how results tie back to their inputs. Data and identity are the moat, but privacy and compliance are the foundation. He was candid about the weekly grind of meeting new regulatory needs region by region. That operational discipline shapes how Zeta decides to build, buy, or partner. Acquisitions must make sense on day one and integrate fast, with people as the primary asset. Chris also spoke directly to younger builders who feel stuck. There are no shortcuts. The only way through is work, curiosity, and a willingness to learn in public. He sees small teams pushing new protocols and patterns forward, and he wants more marketers and technologists to join that frontier with clear eyes and a bias for doing. We closed on culture. Zeta Live in New York brings sports and tech onto the same stage, and there is a reason. When the wider world pays attention, ideas travel further. If you care about marketing that respects customers and still moves the needle, this episode will give you a practical blueprint. It is about AI that makes room for people, systems that earn trust, and a product leader who still enjoys getting a little grease under his nails.

I invited Michael Reitblat, CEO and founder of Forter, to unpack a reality many retailers are living with every day. Fraud is no longer a side issue. It shapes conversion rates, customer loyalty, and the bottom line. Michael argues that if you remove the fear of fraud, you unlock growth. That sounds bold, but his lens is practical. Replace guesswork with instant, consistent decisions and you improve both security and the checkout experience. Here’s the thing. False declines feel like fraud in disguise. When good customers get blocked, they do not return. Michael explains how Forter uses real-time signals to say yes or no within the transaction, without adding friction. The promise is simple. If a buyer is genuine, let them through. If it is fraud, stop it and cover the chargeback. It is a clean model that puts accountability on the platform, not the merchant. We also talk about what happens when AI agents start buying on our behalf. If software is placing orders, refunding items, or filing disputes, identity and intent become fluid. Michael walks through how trust platforms need to reason about behavior across accounts, devices, and sessions. The goal is confidence at the moment of purchase without slowing anyone down. Michael shares how Forter’s scope has expanded from blocking bad actors to enabling smart, business-wide decisions about customers. That means recognizing loyal buyers even if they shop across regions and brands, and spotting synthetic identities that mimic human patterns. It also means measuring success by approvals and lifetime value, not only by stopped attacks. Let me explain why this matters. Retailers are caught between two pains. Ease up and you invite chargebacks. Tighten controls and you lose revenue from good customers. Michael’s point is that trust should be a growth lever. If the system is confident, the checkout stays smooth on web and mobile. If the system is unsure, it can ask for the least painful extra step rather than send a blanket decline. We close with practical guidance for leaders. Treat trust as a product. Give teams shared visibility into decisions. Align incentives so fraud, payments, product, and marketing are working from the same truth. Michael’s vision is a world where anyone can transact with ease because fraud has been priced out of the experience. That is a conversation worth having, and one retailers can act on today.

Here’s the thing. “Smart” has been the buzzword for years, but Richard Leurig argues we’re on the cusp of something bolder. In our conversation, the Accruent president drew a clear line between buildings filled with connected systems and buildings that can sense, decide, and act without a person staring at a dashboard all day. Richard shared a retail story that sticks. By wiring refrigeration units with sensors and training models on billions of telemetry points, his team can spot failures 48 to 72 hours before lettuce wilts or milk spoils. That time window turns panic calls at 3 a.m. into planned daytime fixes. It cuts waste, protects revenue, and keeps customers from walking into empty shelves. The bigger idea is a shift from many panes of glass to no pane of glass. Instead of asking people to wrangle alerts, AI agents coordinate HVAC, security, and maintenance, then dispatch the right technician with the right part only when one is truly needed. That is the road to self-healing facilities. Practicalities that matter now Let me explain why this resonates across industries. Whether you run a hospital, a university, a factory, or a grocery chain, you’re wrestling with aging infrastructure and short supply of skilled workers. Richard sees the same pattern everywhere. Teams need guidance at the point of work, not another report. Natural language agents that answer plain questions and walk users through a task are winning hearts because they remove friction. Return-to-office adds another layer. Hybrid work has made space usage lumpy. Richard outlined how linking lease data, occupancy, and booking behavior helps leaders decide what to close, reshape, or scale. It also changes floor plans. When people do come in, they want project rooms and collaboration zones, not endless rows of cubicles. Retrofit is the sleeper story. You don’t need a skyline of brand-new towers to get smarter. Low-cost sensors and targeted integrations are making older buildings more responsive than most people expect. That opens the door for progress without nine-figure capex. Energy, sustainability, and proof Boards want less energy spend and real emissions progress. The quickest wins are often hiding in plain sight. Richard walked through HVAC control that follows people, sunlight, and weather rather than fixed schedules. Lights that turn off when a room is empty are yesterday’s news. Cooling only where teams are actually working is today’s play. He also flagged a coming wave on factory floors. Many legacy motors and line components quietly draw more power than they should. Clip-on sensors can spot out-of-tolerance behavior so maintenance can fix the energy hog instead of replacing an entire line. That is the kind of operational change that lowers bills and supports sustainability targets with data, not slogans. Richard’s timeline is refreshingly near term. He believes a large slice of the built environment will show real autonomy in three to five years. Not theory. Not demos. Everyday operations that quietly handle themselves until a human is truly required. If this conversation sparks an idea for your sites, stores, labs, or campuses, I want to hear how you’re approaching it. What feels possible this quarter, and what still feels out of reach?