Tech Talks Daily

Neil C. Hughes

If every company is now a tech company and digital transformation is a journey rather than a destination, how do you keep up with the relentless pace of technological change?

Every day, Tech Talks Daily brings you insights from the brightest minds in tech, business, and innovation, breaking down complex ideas into clear, actionable takeaways.

Hosted by Neil C. Hughes, Tech Talks Daily explores how emerging technologies such as AI, cybersecurity, cloud computing, fintech, quantum computing, Web3, and more are shaping industries and solving real-world challenges in modern businesses.

Through candid conversations with industry leaders, CEOs, Fortune 500 executives, startup founders, and even the occasional celebrity, Tech Talks Daily uncovers the trends driving digital transformation and the strategies behind successful tech adoption. But this isn't just about buzzwords.

We go beyond the hype to demystify the biggest tech trends and determine their real-world impact. From cybersecurity and blockchain to AI sovereignty, robotics, and post-quantum cryptography, we explore the measurable difference these innovations can make.

Whether improving security, enhancing customer experiences, or driving business growth, we also investigate the ROI of cutting-edge tech projects, asking the tough questions about what works, what doesn't, and how businesses can maximize their investments.

Whether you're a business leader, IT professional, or simply curious about technology's role in our lives, you'll find engaging discussions that challenge perspectives, share diverse viewpoints, and spark new ideas.

New episodes are released daily, 365 days a year, breaking down complex ideas into clear, actionable takeaways around technology and the future of business.

Subscribe Listen in podnod RSS feed Show website

2,000 episodes
Updated Today

Episodes2000

Newest first

April 27 · 32 min
Tenable On Agentic AI, Exposure Gaps, And The Next Big Security Risk
What happens when AI starts moving faster than the people meant to control it? In this episode, I'm joined by Bernard Montel, Field CTO EMEA at Tenable, for a timely conversation about the AI risks many organizations may be underestimating. Bernard believes we are heading toward a defining AI accident and that the first major incident may come through speed, scale, and unintended consequences rather than a malicious attack. We talk about why so many companies feel pressure to adopt AI at pace, while visibility, governance, and control struggle to keep up. Bernard describes this moment as "driving faster than we can steer," and explains why shadow AI, overprivileged identities, cloud misconfigurations, and exposed AI projects are already creating real business risk. The conversation also looks at agentic AI and why giving systems the ability to take action changes the security equation. A chatbot giving a wrong answer is one problem. An AI agent making flawed decisions, leaking data, or interacting with industrial systems is something very different. Bernard also shares why AI can become a distraction from the security basics that still matter, including cloud security, identity, exposure management, and vulnerability remediation. Attackers may be using AI to move faster, but many of the weaknesses they exploit remain painfully familiar. We also discuss Tenable's new agentic AI framework, announced during RSA, and how the company is using AI to help security teams respond at machine speed while reducing exposure across IT, cloud, OT, identity, and AI environments. For business and security leaders, this episode offers a clear warning and a practical takeaway. AI adoption is no longer a future conversation, but control, governance, and exposure management need to move with it. How prepared is your organization for an AI incident caused by accident rather than attack? Share your thoughts. Useful Links Connect with Bernard Montel, Field CTO EMEA at Tenable Learn More About Tenable Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 26 · 28 min
The Role Of Technology In Creating Healthier, Smarter Buildings
What if the smartest climate technology strategy isn't about inventing something new, but rethinking the buildings we already spend 90% of our lives in? In this episode of Tech Talks Daily, I sit down with Ben Stapleton, Chief Executive Officer of US Green Building Council California, or USGBC California, to discuss why buildings sit at the center of sustainability, resilience, and community well-being. From energy use and air quality to wildfire resilience and climate justice, Ben makes a compelling case that the built environment may be one of the most practical places to create real change. Ben and his team launched the California Building Performance Hub, a platform designed to help building owners, operators, and policymakers understand how to improve building performance through policy guidance, technical resources, rebates, and even an AI-powered assistant trained on building codes and compliance pathways. We discuss how this platform is helping accelerate California's move toward healthier, lower-energy, high-performance buildings and why AI is becoming a useful sidekick rather than a replacement for human expertise. Our conversation also moves beyond technology and into something far more human: community. Ben shares how sustainability only works when people feel they have both awareness and agency. From helping low-income communities understand electrification and indoor air quality, to taking a "BuildSMART Trailer" filled with real building materials into neighborhoods so people can touch and understand the future of their homes, this episode is a reminder that climate progress starts with education and trust. We also talk about wildfire resilience in California, where simple low-cost building decisions can dramatically reduce fire risk while also improving energy efficiency and health outcomes. Ben explains why many of the solutions already exist, and why the challenge is often less about invention and more about implementation, policy, and long-term thinking. For business leaders, public sector teams, and anyone thinking about the future of cities, this episode offers a fresh perspective on sustainability as both a financial and human opportunity. Healthier buildings create healthier people, and healthier people create stronger businesses. Is the future of climate action already built around us, and are we finally ready to look up and see it? I'd love to hear your thoughts. Useful Links Connect with Ben on LinkedIn CA Building Hub USGBC California Follow USGBC California on LinkedIn Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 25 · 28 min
Certinia And Spaulding Ridge On AI, ROI, And Services Teams
How is AI really changing professional services work today, beyond the demos, predictions, and LinkedIn hype? In today's episode, I'm joined by DJ Paoni, CEO of Certinia, and Jay Laabs, CEO of Spaulding Ridge, to discuss how AI is already being used inside services organizations to improve project delivery, resource planning, workforce optimization, and client outcomes. DJ shares what a hybrid workforce of people and AI agents looks like in practice. Rather than thinking of AI as a search bar, he explains why services firms should think of agents as specialized colleagues that can handle repeatable tasks, draft project blueprints, support configuration work, and help teams deliver faster without losing the human judgment clients still rely on. Jay brings the adoption reality from the consulting front line. He explains why the biggest barrier is rarely the technology itself, but the processes, incentives, data models, and cultural habits wrapped around it. The most successful firms are moving away from broad experimentation and focusing on specific business problems where AI can deliver clear ROI. We also discuss the risks of rushing in without a plan. From disconnected AI agents creating a "spaghetti web" across the enterprise to teams automating broken workflows, DJ and Jay share practical warnings for leaders who want AI to create value without adding another layer of complexity. This episode offers a clear look at what is working, what is failing, and what needs to change as professional services firms rethink billable hours, project economics, and the role of human expertise in an AI-enabled workplace. Are services firms ready to measure success by outcomes rather than hours, and what will that mean for the future of consulting?
April 24 · 34 min
How Nue Is Bringing Agentic AI To Revenue Operations
How much revenue is lost because the systems behind pricing, quoting, billing, and finance still do not talk to each other properly? In today's episode, I'm joined by Tina Kung, CTO and Co-Founder of Nue, the quote-to-revenue platform helping AI and SaaS companies rethink how they sell, bill, and grow. Tina brings more than two decades of experience across enterprise software, CPQ, billing, and revenue operations, with previous roles at Oracle, Zuora, SteelBrick, and Salesforce. Tina shares the story behind Nue and why she saw a growing gap between the systems that handle selling and the systems that manage revenue. As SaaS companies move from traditional subscriptions into usage-based pricing, credit burn-down models, product-led growth, partner channels, and enterprise sales, the old way of stitching together tools with manual work and spreadsheets starts to break down. We discuss how AI is changing go-to-market operations and why transaction-level intelligence matters. Tina explains how Nue connects quoting, billing, usage, and revenue data into a single system, then applies AI so teams can understand what is happening, spot opportunities, and take action faster. One of the standout stories is OpenAI, which rolled out Nue in just eight weeks to support the rapid growth of its ChatGPT Enterprise business. Tina shares what that process revealed about the speed of modern AI companies and why flexible revenue infrastructure is now a serious advantage. We also talk about the rise of agentic AI in revenue operations, from creating quotes and orders to handling subscription changes and surfacing upsell opportunities. As the SaaS model comes under pressure from AI, Tina offers a practical view of what needs to change behind the scenes for companies to stay competitive. If SaaS is entering a new chapter, are your revenue systems ready for how customers now buy, use, and pay for software?
April 23 · 25 min
Jack Fu Of Draco Evolution On The Future Of AI-Driven ETFs
Can AI really remove emotion from investing, or does human judgment still matter most when money is on the line? In today's episode, I'm joined by Jack Fu, Founder and CEO of Draco Evolution, a company using AI, quantitative models, and decades of market experience to help investors make smarter and more disciplined decisions. Jack's journey began during the 2008 financial crisis while working as a financial advisor at Union Bank of California, where watching investors lose life-changing amounts of money completely reshaped how he thought about risk, discipline, and long-term wealth creation. That experience led him to focus on one simple principle: avoiding big losses matters just as much as chasing returns. From managing assets for family offices and institutional clients to leading major investment operations across the Asia-Pacific region, Jack built his career around protecting capital first and helping investors stay in the market long enough to benefit from long-term growth. We explore how Draco Evolution is bringing institutional-level investment tools to everyday investors through AI-powered ETFs and a more dynamic approach to portfolio management. Jack explains how ETFs actually work, why they have become such a popular choice for investors, and the important difference between investing in AI companies and using AI itself to manage investment decisions. We also discuss the future of robo-advisors and why the next generation will move far beyond static questionnaires and occasional portfolio rebalancing. Jack shares why he believes the future lies in systems that adapt continuously to market conditions and investor behavior, creating something far more personal and responsive. From algorithmic trading and AlphaGo to today's world of agentic AI, Jack offers a practical perspective on how technology is changing finance without replacing human oversight. He also shares why investors should treat AI as an enhancement tool rather than blindly trusting every recommendation. If you've ever wondered how AI is changing investing, what makes AI-driven ETFs different, or how to stay disciplined in unpredictable markets, this conversation offers plenty of insight. How much would you trust AI to help manage your financial future, and where would you still want a human in the loop?
April 22 · 25 min
Adobe Summit: Virgin Atlantic's AI Concierge and the Future of Travel
What does it actually take to move from AI experiments and pilot projects to real business outcomes that customers can feel? At Adobe Summit in Las Vegas, I sat down with Neil Letchford, Vice President of Digital Engineering at Virgin Atlantic, to talk about how the airline is doing exactly that. While many organizations are still debating ROI, governance, and where agentic AI fits into the customer journey, Virgin Atlantic has already launched an AI concierge that is actively helping customers book holidays, find answers faster, and create a smoother travel experience from the first search to stepping onboard the aircraft. Neil shared how a proof of concept built in just two months evolved into a live multi-agent system that now helps customers plan trips, book holidays, and move seamlessly between digital channels and human support when needed. We talked about the importance of "knowledge over data," why observability and model evaluation matter when deploying AI at scale, and how the team built trust internally by focusing on real customer pain points rather than chasing shiny technology trends. What stood out most was how Virgin Atlantic has kept its famously human customer experience at the center of every decision. This is not automation for the sake of efficiency. It is about using AI to strengthen relationships, preserve brand personality, and create better outcomes for both customers and the business. From personalized holiday planning to agent-to-agent interactions that may soon redefine travel booking, this conversation offers a practical look at what happens when AI moves beyond theory and starts delivering value today. If you want to understand what agentic AI looks like in the real world, and why the companies moving early may gain a serious advantage, this is an episode you do not want to miss. What would AI need to do in your business before you would trust it to take the lead? Useful LInks Connect with Neil Letchford Learn more about Adobe Brand Concierge Check out Virgin AI Concierge Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 21 · 34 min
Inside Brightcove: Filippo de Salazar On AI, Automation, And The New Streaming Economy
How has streaming changed from simply delivering video to becoming one of the most important business engines behind sports, media, and customer engagement? In this episode of Tech Talks Daily, I sit down with Filippo de Salazar, who leads the Brightcove team following its acquisition by Bending Spoons, to talk about how the company is evolving and where the future of streaming is heading next. With more than 20 years in the industry and powering over a billion streams every week, Brightcove has become the invisible backbone behind many of the broadcasters, publishers, sports networks, and enterprise video experiences we all rely on without ever thinking about the technology behind them. Filippo shares how the past year has accelerated Brightcove's product velocity, with major releases including AI capabilities, live 4K, live DRM, and automation tools that help customers move faster without compromising reliability. While the business has gained speed, he explains that Brightcove's focus on stability and customer obsession remains unchanged, especially when customers depend on mission-critical video workflows that leave no room for failure. We also unpack how AI is moving beyond hype and creating measurable value for broadcasters today. From automatically detecting live sports highlights and clipping them for instant social sharing, to improving ad placement relevance, generating live captions, and translating content into more than 70 languages, AI is reshaping both operational efficiency and revenue generation. Filippo explains how tools like Brightcove's Universal Translator and Metadata Optimizer are helping broadcasters unlock ROI that simply was not possible before. Our conversation also covers personalized streaming, fan engagement, cloud-native automation, and the rise of FAST channels. We discuss why sports audiences now expect low latency, instant highlights, and highly personalized viewing experiences, and how broadcasters must balance those expectations with the realities of infrastructure costs and monetization pressure. Filippo also shares why discoverability has become one of the biggest battlegrounds in streaming, with some viewers spending more time searching for content than actually watching it. Looking ahead, Filippo outlines the three trends he believes will define the next phase of streaming: intelligent automation, stronger monetization discipline, and managing fragmented viewing behaviors across live, subscription, ad-supported, and FAST environments. As media companies try to unify these experiences without adding complexity, platforms like Brightcove are becoming increasingly central to how modern video businesses operate. What does the future of streaming really look like when AI, automation, and personalization all collide, and are broadcasters ready for what comes next? Useful Links Connect with Filippo de Salazar Learn more about Brightcove following its acquisition by Bending Spoons Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 20 · 24 min
How HelloFresh Replaced 450 Spreadsheets With Real-Time Decisions
What happens when the biggest breakthrough in AI isn't a flashy new tool, but finally getting rid of 450 spreadsheets? Recording live from Qlik Connect, I sat down with Ed Dunger from HelloFresh to talk about what operational transformation actually looks like inside one of the world's most complex supply chain environments. Because when your business depends on forecasting demand, managing perishable food, coordinating deliveries, and making sure customers receive the right box at the right time, small inefficiencies quickly become expensive problems. Ed leads operational technology and analytics enablement across global teams at HelloFresh, covering everything from forecasting through to final-mile logistics. In this conversation, he shares how the company moved away from hundreds of disconnected Google Sheets and manual processes toward a near real-time, data-driven operating model that gives teams faster, clearer, and more reliable decision-making. We talk about the practical reality of replacing over 450 spreadsheets, building trust in the data, and creating systems that operational teams actually want to use. Ed explains why this was a two to three year journey rather than an overnight transformation, and how early wins, like predicting waste before it happened, helped build confidence across the business. We also explore how HelloFresh is using predictive AI to improve exception management when deliveries fail. From triggering recovery boxes faster to improving customer communication when something goes wrong, the focus is not on AI for the sake of AI, but on solving real problems that directly affect customer experience. There is also a valuable lesson here for any business trying to move from experimentation to operational reality. Start small, build trust gradually, and focus on solving one problem well before trying to transform everything at once. So as more organizations race to adopt AI, are we sometimes overlooking the simple operational fixes that create the biggest impact? And is real transformation less about the technology itself, and more about how people learn to trust it? Join me for a practical and honest conversation from Qlik Connect, and let me know your thoughts. Are you still managing around old processes, or are you building systems people can truly rely on? Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 19 · 24 min
How the Reconomy Group and Valpak Went From Spreadsheets to Scalable AI-Powered Data Platforms
How do you turn complex regulatory data into something customers can actually use, trust, and act on? Recording live from Qlik Connect, I sat down with Robin Astle, Head of Qlik Analytics at Reconomy Group, to explore how data is becoming far more than an internal reporting tool. In Robin's world, it has become a product in its own right, helping some of the world's largest retailers manage compliance, reduce costs, and make smarter sustainability decisions. Robin works across Valpak, a business at the center of environmental compliance and packaging regulation, supporting over 100 enterprise customers across the UK, Europe, and the US. From packaging taxes and recycling targets to government submissions and sustainability reporting, the amount of data involved is enormous, and the stakes are high. In our conversation, Robin shares how the Valpak Insight Platform evolved from manual SQL extracts and spreadsheets into a fully scaled cloud-based analytics platform ingesting millions of rows of data every day. We discuss how that transformation helped reduce onboarding from weeks to days, created up to 90% time savings on CSR and analytics requests, and helped customers reduce compliance costs by up to 15%. We also explore the launch of PackChat, which uses natural language queries to help customers interact with compliance and packaging data without needing deep technical knowledge. Robin explains why context is everything when dealing with environmental regulations, and why building trust in the data model is essential before AI can deliver real value. There is also a bigger conversation here around how businesses can use data to serve customers directly, not just support internal teams. From OEM partnerships and cloud automation to scaling AI-powered services across global markets, Robin shares what it takes to turn data into a revenue-generating service. So as more organizations look to unlock value from the information they already hold, are we still thinking too narrowly about what data can do? And could your greatest untapped product actually be the data sitting inside your business today? Join me for a fascinating conversation from Qlik Connect, and let me know your thoughts. Are you still using data for reporting, or are you starting to think about it as a product? Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 18 · 27 min
Qlik Connect: Mary Kern On Building AI People Will Actually Use
How do you turn powerful AI technology into something customers actually trust, adopt, and use? Recording live from Qlik Connect, I sat down with Mary Kern, Vice President of Analytics Product Go-To-Market at Qlik, to explore one of the most overlooked challenges in enterprise AI today. Not building the technology, but making it real for the people expected to use it every day. Because while AI innovation is moving at incredible speed, many organizations are still struggling with a much more practical question. How do you move from exciting product announcements and pilot projects to real adoption, measurable outcomes, and business value? In our conversation, Mary shares how Qlik is approaching that challenge by shifting the focus away from shiny features and toward outcomes that matter. We discuss why agentic AI is creating so much excitement, why customers are often much closer to operationalizing it than they realize, and how years of investment in data quality, governance, and analytics are now becoming the foundation for what comes next. We also talk about the growing importance of trusted data and context, especially as AI moves from generating insights to influencing decisions and actions. Mary explains why simply adding a large language model on top of existing systems rarely works, and why organizations need to think more carefully about how AI is trained, governed, and integrated into the environments where people already work. There is also a refreshingly honest conversation around cost, experimentation, and imperfection. Mary makes the case that organizations should start now, even if the data is not perfect, because using AI often reveals where the real gaps are and what needs to improve next. So as businesses look ahead to the next 12 months, what will separate those who successfully scale AI from those still stuck in pilot mode? And are we spending too much time talking about the technology, and not enough time understanding how people will actually use it? Join me for a candid conversation from the heart of Qlik Connect, and let me know your thoughts. Is your organization closing the gap between AI capability and real adoption, or is that still the biggest challenge?
April 18 · 21 min
Qlik Connect: Nick Magnuson On Trusted Data and Agentic AI
What if the reason most AI projects fail has less to do with the technology and more to do with how the work itself is designed? Recording live from Qlik Connect, I sat down with Nick Magnuson, Head of AI at Qlik, for a conversation about the gap between AI ambition and operational reality. Because while many organizations are still focused on models, tools, and the race to deploy new capabilities, the real challenge often sits somewhere much less glamorous. Workflow design, trusted data, and making sure AI fits the way a business actually runs. Nick brings more than two decades of experience in machine learning and predictive analytics, and in this conversation, he shares why so many AI initiatives fail before they ever create value. His view is refreshingly direct. Most failures are not technology failures at all. They are workflow failures, where teams try to force AI into the business without first understanding the outcomes they are trying to achieve. We also explore the rise of agentic AI and what it means when systems move from generating insights to taking action. Nick explains why governance becomes even more important in that world, how organizations can balance speed with control, and why trusted data has to move beyond being "good enough for reporting" to becoming reliable enough for decisions and automated execution. There is also a strong discussion around openness, portability, and the growing risk of vendor lock-in. As enterprises build more complex AI ecosystems, flexibility is becoming a strategic advantage, especially for organizations trying to scale without creating expensive dependencies they will regret later. For mid-market businesses with limited resources, Nick also shares a practical path to production. A reminder that operationalizing AI does not require massive teams or unlimited budgets, but it does require clarity, discipline, and a focus on the right problems first. So as the next wave of enterprise AI moves from experimentation to execution, what will separate the organizations that scale successfully from those still stuck in pilot mode? And are we asking the wrong questions by focusing on more AI, instead of better AI? Join me for a thoughtful conversation from the heart of Qlik Connect, and let me know your view. Is workflow design the missing piece in your AI strategy?
April 17 · 26 min
How American University's Kogod School Of Business Is Redefining AI Education And Business Strategy
What does it really take to turn AI from a flashy experiment into something that creates measurable business value? In this episode of Tech Talks Daily, I sat down with Angela Virtu from American University's Kogod School of Business to talk about what business leaders should actually be paying attention to as AI moves into a new phase in 2026. This conversation goes far beyond the usual headlines about bigger models and faster tools. Angela brings a rare mix of academic leadership and hands-on startup experience, which means she understands both the technical side of AI and the hard business questions around adoption, trust, and ROI. One of the most interesting parts of our discussion centered on how American University's Kogod School of Business became one of the first AI-first business schools. Angela shared how that shift was never really about chasing hype. It was about recognizing a real change in the workplace and preparing students for jobs, workflows, and expectations that are already being shaped by AI. From faculty training to culture change, she explained how transformation only works when leadership is willing to support experimentation and accept that some ideas will fail before the right ones take hold. We also spent time unpacking where businesses stand right now in the AI adoption cycle. After years of pilots and proof-of-concept projects, many companies are under pressure to show results. Angela offered a refreshingly honest take on why so many AI projects stall and why adoption alone is a weak metric. Instead, she argued that companies need to tie AI initiatives to clear business problems and existing KPIs. Whether that means customer support resolution times, employee productivity, or operational efficiency, the point is simple. AI needs to earn its place. Another thread running through this episode is governance. As AI becomes more deeply embedded inside organizations, the conversation is shifting toward oversight, accountability, and trust. Angela explains why the strongest governance models are often shared across the company rather than locked inside one team. She also discusses the need for closed systems, stronger communication, and honest disclosure when businesses use AI in customer-facing environments. That part of the conversation feels especially timely as more brands try to balance innovation with customer expectations. We also looked ahead at what is coming next, from model orchestration and vertical AI to the rise of physical world models and even the possibility of AI agents becoming a customer audience in their own right. It is one of those episodes that will give business leaders, technologists, educators, and curious listeners plenty to think about. If you are trying to understand where AI strategy is headed in 2026, and how to separate real value from noise, this episode is for you. What did you make of Angela's views on governance, ROI, and the next phase of AI adoption, and where do you think businesses are still getting it wrong? Share your thoughts with me. Useful Links: Connect with Angela Virtu Kogod School of Business Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 16 · 26 min
Qlik Connect: Ryan Welsh On Turning AI Into Business Outcomes
What actually separates AI that delivers real value from AI that never makes it past the demo stage? Recording live from Qlik Connect, I sat down with Ryan Welsh, Field CTO of Generative AI at Qlik, to get a grounded, practitioner-led view of what it really takes to make AI work inside a business. While the industry has spent the past few years racing to experiment, build, and deploy new capabilities, many organizations are still struggling to turn that progress into capabilities people use every day. In our conversation, Ryan cuts through the noise and explains why so many AI initiatives fail. Not because the models aren't powerful enough, but because they're not designed to fit into real workflows. He shares why context is far more than just a buzzword and how getting the right data, in the right place, at the right time, enables AI to deliver meaningful outcomes. We also explore the growing shift toward agentic AI and the responsibilities that come with it. From designing systems that can act autonomously while remaining under control to understanding where humans need to stay involved, Ryan offers a practical view of how organizations can move forward without introducing unnecessary risk. There's also a refreshing honesty around where we are right now. After a wave of investment and expectation, many companies struggled to see immediate value from AI. But as Ryan explains, that period is changing, with more organizations finding ways to scale what works and move beyond isolated use cases. So, as businesses look ahead, what does it really take to move from experimentation to execution? And are we focusing too much on building more AI rather than the right AI for how our organizations actually operate? Join me for a candid conversation from the heart of Qlik Connect, and let me know your thoughts. Are you seeing AI deliver real outcomes in your business, or is it still stuck in the demo phase? Useful Links Connect with Ryan Walsh on LinkedIn Learn more about Qlik. Follow on Twitter, Facebook, and LinkedIn Visit the May Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 16 · 23 min
Qlik Connect: James Fisher On Turning AI Into a Business Strategy
What does it really take to move beyond AI experimentation and build something a business can rely on? Recording live from Qlik Connect, I sat down with James Fisher, Chief Strategy Officer at Qlik, to unpack what's actually changing as AI moves from hype into real-world execution. Because while many organizations have spent the past few years exploring use cases and running pilots, the harder challenge is now in front of them. Turning that early momentum into something scalable, governed, and aligned with business outcomes. In our conversation, James offers a candid view of where companies are getting this wrong. He describes a period of what he calls "AI madness," where everything became a potential use case, but very little translated into measurable value. Now, he sees a shift toward more focused, outcome-driven thinking, where success depends on understanding the user, the data, and the specific problem being solved. One of the most thought-provoking moments comes when James challenges the idea of having an AI strategy at all. Instead, he argues that AI should be embedded directly into the broader business strategy, shaping how decisions are made, how processes operate, and how organizations compete. We also explore the realities that many businesses are only just beginning to face. The complexity of data access and governance, the growing pressure around cost and sustainability, and the risks of vendor lock-in in a rapidly evolving AI ecosystem. James shares why openness and flexibility are becoming critical, and why some of the same patterns seen in previous technology waves are starting to repeat themselves. So as organizations look ahead to the next 12 to 24 months, what will separate those that successfully operationalize AI from those that remain stuck in cycles of experimentation? And are we focusing too much on the technology, and not enough on the business problems it's meant to solve? Join me for a grounded and strategic conversation from the heart of Qlik Connect, and let me know your thoughts. Are you still experimenting with AI, or are you starting to embed it into the core of how your business operates? Useful Links Learn more about Qlik. Follow on Twitter, Facebook, and LinkedIn Visit the May Sponsors of Tech Talks Network and learn more about the NordLayer Browser.
April 15 · 32 min
3483: How Glean Is Securing The Next Wave Of AI Agents In The Enterprise
What happens when your AI agents start making decisions faster than your security team can even see them? In this episode, I sit down with Sunil Agrawal, Chief Information Security Officer at Glean, to unpack a shift already underway in enterprises. With predictions that 40 percent of enterprise applications will include autonomous AI agents by the end of 2026, we are moving from human-led workflows to machine-to-machine interactions at a scale most organizations are not fully prepared for. Sunil brings a rare perspective, blending more than 25 years of cybersecurity experience with an inventor's mindset shaped by over 40 patents. What stood out to me in our conversation is how quickly the traditional security model is becoming outdated. As he explained, "autonomous agents break those assumptions because they operate across tools, varying permissions and data sources with alarming speed and autonomy." This creates what he calls the "autonomy gap," in which the CIO's drive for speed collides with the CISO's need for visibility and control. We explore how that tension is playing out in real organizations today, and why so many are already falling behind. Nearly half of businesses still lack the AI-specific controls needed to prevent untraceable incidents, and the risks are not always what you might expect. Sunil argues that the first major rogue-agent incident is unlikely to be a malicious attack. Instead, it will come from confusion: a well-intentioned system taking the wrong action in the wrong context, with consequences that ripple across the business. The conversation then turns practical. Sunil breaks down his AWARE framework, a structured way to introduce real-time guardrails that evaluate intent, context, and risk before an agent takes action. Rather than relying on static policies, this approach focuses on continuous runtime enforcement, where systems are constantly assessed based on behavior rather than assumptions. What I found particularly valuable is how this moves beyond theory into something leaders can act on today. From starting with tightly scoped use cases to investing in full observability, this episode offers a clear roadmap for balancing innovation with accountability. As Sunil put it, organizations that succeed will not be the ones that move fastest, but the ones that prove trust at scale. So how do you embrace the productivity gains of autonomous AI without opening the door to invisible risk, and are your current security models ready for a world where the "user" is no longer human? Useful Links Connect with Sunil Agrawal on LinkedIn Learn more about Glean Follow Glean on LinkedIn Visit the Tech Talks Network Sponsor NordLayer Browser
April 14 · 18 min
Qlik Connect: Mike Capone On Agentic AI and Turning Insight Into Action
What does it actually take to move AI from experimentation into something a business can depend on every single day? Recording live from the show floor at Qlik Connect in Florida, I sat down with Qlik CEO Mike Capone to cut through the noise and get to the reality behind enterprise AI in 2026. Because while the headlines are still dominated by rapid innovation and new capabilities, many organizations are quietly facing a different challenge. They are struggling to turn AI ambition into measurable outcomes. In our conversation, Mike shares what he is hearing from customers around the world and why so many companies remain stuck in cycles of pilots and proof of concepts. We talk about the growing pressure from boards and leadership teams to move faster, and why that urgency is often leading to what he calls a "ready, fire, aim" approach that fails to deliver real business value. We also explore one of the biggest themes emerging at Qlik Connect this year. The shift toward agentic AI. But rather than focusing on the hype, Mike breaks down what this actually means inside a real enterprise workflow, where insights are not just generated but turned into decisions and actions. He also explains why getting the data foundation right is no longer optional, and how poor data quality can quickly turn AI from an opportunity into a risk. From data trust and governance to the challenges of operating across increasingly complex regulatory environments, this episode offers a clear view of what it takes to build AI systems that are reliable, scalable, and grounded in real business context. So as organizations look ahead to the next 12 to 24 months, what will separate those that successfully operationalize AI from those that remain stuck in pilot mode? And are we focusing too much on building more AI, rather than building better AI? Join me for a candid conversation from the heart of Qlik Connect, and let me know where you stand on this shift. Are you seeing real progress, or are the same challenges holding things back?
April 14 · 34 min
Twilio: Demystifying Model Context Protocol (MCP) And Real-World AI Deployment
How are brands supposed to deliver AI-powered customer experiences when their data is scattered across systems that were never designed to work together? In this episode, I sit down with Peter Bell, VP EMEA Marketing at Twilio, to unpack one of the most important AI topics that still does not get enough attention outside technical circles, Model Context Protocol, or MCP. While many conversations about AI remain stuck on model hype, chatbots, and the latest product launch, Peter brings the discussion back to something far more practical. If businesses want AI to deliver real outcomes in customer service, marketing, and brand engagement, they first need a reliable way to connect large language models to the right data, in the right systems, with the right controls in place. That is why this conversation matters. Peter explains how MCP could become one of the biggest unlocks for enterprise AI by creating a standard way for LLMs to access information across fragmented tools like CRM platforms, marketing systems, and other business applications. Instead of forcing every company to build custom integrations from scratch, MCP creates a more consistent path for connecting models to the context they need. For me, that is where this episode really earns its place, because it moves the AI conversation away from vague ambition and toward the plumbing that actually makes useful AI possible. We also talk about why first-party data remains so important, especially as businesses try to create customer experiences that feel seamless, personal, and trustworthy. Peter makes the point that public models may be useful for general knowledge, but brands cannot rely on generic internet-trained systems to solve precise business problems. If you want AI to support travel bookings, customer service, or commerce journeys, you need specific data, strong governance, and a much clearer understanding of the problem you are trying to solve. That sounds obvious, but it is still where many AI projects fall apart. Another part of our conversation focuses on trust, which feels especially relevant right now. From scams and impersonation to consumer fatigue and poor automation, brands are under pressure to move faster without losing credibility. Peter shares how Twilio is thinking about branded calling, RCS, conversational AI, and voice experiences that feel modern without becoming intrusive or robotic. We also discuss why too many companies still automate too broadly, too quickly, without defining the actual use case first. What I enjoyed most here was Peter's balanced view. He is optimistic about where AI is heading, but he is also realistic about the work still required to get there. This is not a conversation about AI magic. It is about data access, governance, trust, brand experience, and the standards that may quietly shape the next phase of AI adoption far more than the flashy headlines. So if you have been hearing more people mention MCP and wondering why it matters, or if you are trying to understand what needs to happen before enterprise AI can move from promise to practical value, this episode will give you plenty to think about. Is Model Context Protocol the missing layer that finally helps AI connect with the real world of business data?
April 13 · 29 min
Invisible Technologies CEO On Building AI Around Real Workflows, Not Hype
What does it actually take to make AI work inside a real business, where messy data, human judgment, and operational risk all collide? In this episode, I sit down with Matt Fitzpatrick, CEO of Invisible Technologies, to talk about why the biggest barrier to enterprise AI is not model quality, it is everything that comes before the model ever gets to work. Since stepping into the CEO role in January 2025, Matt has moved quickly, raising $100 million and expanding Invisible's footprint across major cities including New York, San Francisco, DC, Austin, London, and Poland. But this conversation is far less about headlines and far more about what happens in the trenches of AI adoption, where companies are trying to move from pilots and PowerPoint promises to systems that actually deliver results. A huge theme throughout our discussion is data readiness. Matt makes a compelling case that most businesses are still dealing with fragmented systems, inconsistent records, and information spread across disconnected tools. That reality makes it incredibly hard to deploy AI in a way that creates trust and value. We talk about SwissGear, where Invisible used its Neuron platform to clean and structure 750 scattered tables in just one week, a task that could have taken a large engineering team months or longer. We also discuss why that kind of work matters so much, because once the data foundation is fixed, companies can start making better decisions on forecasting, operations, and planning with a level of confidence that simply was not there before. We also spend time on Invisible's human-in-the-loop approach, which I think will resonate with a lot of listeners trying to cut through the noise around job displacement and agentic AI. Matt argues that the real opportunity is not replacing people, but giving them better tools to handle repetitive work while preserving room for human expertise, judgment, and oversight. He shares examples from commercial credit workflows, healthcare, and sports analytics, including a fascinating story about the Charlotte Hornets using AI to turn broadcast footage into detailed tracking data. What stood out to me was how practical his perspective felt. This was not theory. It was about building systems around how organizations actually work, rather than expecting businesses to reshape themselves around a generic AI product. Another part of the conversation that deserves attention is governance. As boards rush to understand agentic AI, Matt explains why trust, standards, and responsible deployment are now driving buying decisions just as much as raw capability. We talk about privacy in healthcare, the risks of scaling autonomous systems without mature governance, and why enterprise adoption still trails consumer AI by a wide margin. That gap between excitement and execution may be one of the most important stories in AI right now. If you are wondering why so many AI projects never make it into production, or what it will take for enterprise AI to finally deliver on its promise, this episode is packed with insight. It is a conversation about data, deployment, governance, and the role humans will continue to play as AI becomes part of everyday business operations. After listening, I would love to know where you stand, is the future of AI really about bigger models, or is it about making AI fit the messy reality of how work gets done?
April 12 · 48 min
Willow On How AI Is Changing The Way Buildings Operate
In this episode, I speak with Bert Van Hoof, CEO of Willow, about how AI is starting to reshape the built world in ways that go far beyond smart dashboards and efficiency reports. Bert brings decades of experience from the front lines of digital infrastructure, including his time at Microsoft, where he helped create Azure Digital Twins and Smart Places. Today at Willow, he is focused on a much bigger idea, using AI to help buildings, campuses, hospitals, airports, and other complex environments operate with greater intelligence, lower waste, and better outcomes for the people who rely on them every day. One of the most interesting parts of our conversation is how Bert explains the shift from passive building software to active management systems. For years, many digital twin and smart building tools were good at showing what had already happened. But operators do not need another screen full of charts. They need systems that can connect live data, static records, spatial context, and operational history to help them make better decisions in real time. That is where Willow comes in, creating a digital foundation where AI can reason across everything from HVAC and air quality to occupancy, refrigeration, maintenance history, and even energy usage patterns. We also unpack why this matters right now. Energy costs remain under pressure, sustainability goals are getting harder to ignore, and many organizations are still stuck with fragmented systems that do not talk to each other. Bert shares how AI can help move building teams from reactive maintenance to predictive performance, spotting issues earlier, cutting downtime, reducing waste, and extending the life of expensive assets. He also explains why the future of building operations will depend on a stronger data foundation, operational AI copilots, and systems that can support an aging workforce while making these roles more appealing to the next generation. What stood out for me was how practical this all became once we moved past the buzzwords. This was not a conversation about futuristic hype. It was about real examples, from occupancy-based HVAC control in offices and campuses to leak detection in schools, vaccine refrigeration monitoring, and hospital environments where downtime can carry enormous consequences. Bert makes a strong case that buildings are no longer just static structures. They are living operational environments filled with signals, systems, and opportunities that have been hiding in plain sight. We also touch on the wider picture, including what Bert learned from smart cities and energy grid modernization, and how those lessons now apply to commercial real estate, airports, research labs, and higher education campuses. There is a real sense that the physical world is entering a new chapter, one where AI starts to bridge the gap between digital intelligence and real-world action. If you have ever wondered what AI looks like when it leaves the screen and starts improving the places where people work, heal, travel, learn, and live, this episode will give you plenty to think about. As always, I would love to know what you think, are buildings finally ready to become truly responsive, and what opportunities or risks do you see ahead?
April 11 · 47 min
Blumberg Capital On What Investors Really Want From AI Founders Now
What does it really take to build the next generation of AI companies when the hype around scale begins to fade and real-world impact takes center stage? In this episode, I sit down with David Blumberg, founder and managing partner at Blumberg Capital, to unpack what he believes will define the next wave of AI startups. With a track record that includes being the first investor in companies like Nutanix, Braze, and DoubleVerify, David brings a perspective shaped by decades of identifying breakout innovation early. But what stood out most in our conversation was his belief that 2026 marks a turning point where intelligence moves beyond experimentation and becomes operational. We explore what that shift actually means in practice. David explains how AI is evolving from systems that generate insights into systems that take action, and why that distinction matters for founders, investors, and enterprise leaders alike. He shares how the most compelling startups today are not simply layering AI onto existing products, but embedding it deeply into workflows across industries like finance, security, and supply chain. These are companies built on proprietary data and real operational context, designed to make decisions with precision rather than simply process information. Our conversation also challenges some widely held assumptions about success in the AI space. David makes it clear that scale alone will not separate winners from the rest. Instead, the focus is shifting toward accuracy, reliability, and domain expertise. Founders who have lived the problems they are solving, rather than approaching them from the outside, are far more likely to build something defensible and lasting. It is a subtle shift, but one that could redefine how value is created in the years ahead. There is also a broader discussion about where investment is flowing and why. With the vast majority of companies Blumberg Capital now evaluates being rooted in AI, the bar for differentiation is rising fast. David offers insight into what his team is really looking for in founders entering this next cycle, and how startups can stand out in an increasingly crowded field. So as AI moves from promise to execution, and from experimentation to real-world outcomes, the question becomes harder to ignore. Are we ready to rethink how we measure success in the AI era, and what kind of companies will truly earn their place at the top?