The Tech Blog Writer Podcast

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?

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?

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?

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?

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.

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.

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 Connect with Mike Capone 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.

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

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?

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?

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?

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?

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?

How do we talk about artificial intelligence without ignoring the very human consequences it can have on our mental health? In this episode, I sit down with Dr. Ragy Girgis, Professor of Clinical Psychiatry at Columbia University, to unpack a topic that has quietly moved from the fringes of academic discussion into mainstream headlines. You have probably seen the term "AI psychosis" appearing more frequently, often surrounded by speculation, fear, or misunderstanding. But what does it actually mean, and how should we be thinking about it as these technologies become part of everyday life? Ragy brings a clinical and deeply considered perspective to the conversation. He explains that what we are seeing is not AI creating entirely new delusions out of thin air, but something more subtle and arguably more concerning. Large language models can reflect and reinforce ideas that already exist within a person's mind. For someone already vulnerable, that reinforcement can push a belief from uncertainty into absolute conviction. That shift, even if small, can have life-altering consequences. It raises uncomfortable questions about how persuasive technology interacts with fragile mental states. We also explore the comparison many people make with older internet rabbit holes, and why this new generation of AI tools feels different. There is something about conversational systems that mimic human interaction so convincingly that they can blur the line between reflection and validation. Ragy introduces a powerful analogy rooted in the story of Narcissus, which reframes the issue in a way that feels both timeless and unsettling. It is not about an external voice planting ideas, but about a mirror that becomes impossible to look away from. But this conversation is not about fear. It is about responsibility and awareness. We discuss practical steps that could help reduce risk, from how AI systems communicate their limitations, to the role of families and clinicians, and even the responsibility of tech companies to invest in research around early warning signs. There is a sense that we are only at the beginning of understanding this phenomenon, and that the decisions made now will shape how safely these tools evolve. So as AI continues to move closer to us, speaking in our language and responding in real time, how do we make sure it supports human wellbeing rather than quietly amplifying our most vulnerable moments? Useful Links Connect with Dr. Ragy Girgis, Professor of Clinical Psychiatry at Columbia University Time Magazine Article Visit the May Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

Why are so many AI projects failing to deliver real business value, despite the hype and investment? In this episode, I sit down with Jay Litkey, SVP of Cloud & FinOps at Flexera, to explore the growing gap between AI ambition and measurable results. We discuss why findings from PwC reveal that only a small percentage of CEOs are seeing both revenue growth and cost savings from AI, and why the issue often comes down to a lack of clear outcomes, financial discipline, and governance rather than the technology itself. Jay shares what organizations are getting wrong, why many are stuck in experimentation mode, and what it really means to go back to basics in 2026. The conversation also reframes FinOps for the AI era, moving beyond cost control to a model that connects AI usage directly to business value, aligns finance with engineering, and introduces the guardrails needed to scale responsibly. If you are investing in AI or planning your next move, this episode offers a clear lens on how to turn potential into performance. Useful Links Connect with Jay Litkey from Flexera Learn More About Flexera Visit the May Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

How do you bring people together to do better work when everything around them feels increasingly complex, distributed, and uncertain? In today's episode, I sat down with Jessica Guistolise from Lucid Software, and what struck me straight away was her belief that work has always been a group project, even if many organizations still behave as though it is not.  Jessica shared how much of the friction we experience at work comes from misalignment, unclear expectations, and a lack of shared understanding. When teams are spread across time zones, systems, and now AI-powered workflows, those gaps only widen. Her perspective is simple but powerful. When people can actually see the work, rather than interpret it through documents, meetings, or assumptions, something shifts. Conversations become clearer, decisions become faster, and collaboration starts to feel human again. We also explored how visual collaboration platforms like those from Lucid Software are helping teams move away from scattered tools and disconnected workflows toward a more unified way of working. Jessica described it as having everything on one workbench, where teams can brainstorm, plan, and execute without constantly switching context.  What really stayed with me was her focus on inclusivity in collaboration. Not everyone contributes in the same way, and visual environments can create space for different thinking styles, whether someone is outspoken, reflective, or somewhere in between. That idea of creating a shared language across teams, roles, and even personalities feels increasingly relevant in a world where communication often breaks down. Of course, no conversation right now would be complete without talking about AI. Jessica offered a refreshingly honest view. There is uncertainty, and there should be. But rather than avoiding it, she believes leaders need to make AI visible, map how it is used, define where human judgment matters, and encourage teams to experiment openly.  One of the most interesting ideas she shared was reframing mistakes as early learnings. When teams feel safe to test, fail, and share what they discover, progress accelerates. When fear or blame enters the picture, everything slows down. We also touched on AI literacy and what it really means in practice. For Jessica, it comes down to clarity. Clear workflows, clear guardrails, and clear expectations about accountability. AI might assist, but humans remain responsible for outcomes. That mindset, combined with leadership that actively participates in experimentation, creates an environment where people feel confident stepping forward rather than holding back. This conversation left me thinking about how many organizations are still trying to layer AI onto unclear processes and expecting better results. Jessica's message is that clarity comes first, then technology can amplify it.  So if work really is a group project, are we giving our teams the visibility and confidence they need to succeed, or are we still asking them to figure it out in the dark?

What happens when the very pricing model meant to speed up AI adoption ends up slowing it down? In this episode of Tech Talks Daily, I sit down with Sameet Gupte, CEO and co-founder of EvoluteIQ, to discuss a part of the enterprise AI story that still doesn't get enough attention. While so much of the conversation around AI focuses on models, copilots, and the latest agentic promises, Sameet brings the discussion back to a business reality that every enterprise leader understands. If the economics do not work, adoption stalls. And if success in a pilot makes the final rollout even more expensive, something has gone wrong long before the board signs off on scale. Sameet argues that many organizations are still trapped by legacy pricing structures built for an earlier generation of automation. Per-user and per-bot pricing may look manageable at the pilot stage. Once a company tries to expand automation across departments, processes, and geographies, the numbers can quickly stop making sense. That creates what many now call pilot purgatory, where a company proves something can work, but cannot justify taking it any further. It is a problem rooted in incentives, procurement, and fragmented technology stacks, and it is one that CFOs are watching very closely. What I found especially interesting in this conversation is how Sameet frames the issue. He believes most enterprises do not actually have an automation problem. They have an orchestration problem. In other words, the challenge is rarely a lack of tools. It is getting all the systems, workflows, approvals, data flows, and legacy infrastructure to work together to produce a clean business outcome. That idea changes the conversation from buying isolated features to rethinking the process as a whole. We also discuss why outcomes-based pricing is increasingly resonating with enterprise buyers. Sameet explains why predictable costs, transparent commercial models, and shared accountability are helping move automation conversations out of innovation teams and into the CFO's office. For public companies and large global enterprises, that matters. Leaders want fewer surprises, fewer overlapping vendors, and a much clearer line between spend and return. There is also a broader theme running through this episode about where the market is heading next. Sameet sees real urgency around vendor consolidation, enterprise simplification, and the need to rethink how AI is introduced into the business. His view is that companies need to pause, define what they actually want AI to do, and then choose tools that fit the business, rather than reshaping the business around the latest platform pitch. If you are trying to make sense of AI adoption beyond the hype, this conversation offers a practical and timely perspective on pricing, scale, and what real transformation could look like inside the enterprise. After listening, do you think the future of enterprise AI will be shaped as much by commercial models as by the technology itself, and what are you seeing in your own organization? Useful Links Connect with Sameet Gupte, CEO and co-founder of EvoluteIQ Learn More About EvoluteIQ

  How many bad customer experiences does it take before someone walks away for good? In my conversation with Amitha Pulijala, we explore why the answer might be fewer than most businesses are prepared for, and what that means for anyone investing in AI-powered customer experience. New research from Cyara reveals a stark reality. Twenty-eight percent of consumers will abandon a brand after just one poor interaction, and nearly half will do the same after only two or three. That leaves very little room for error at a time when more organizations are introducing AI into customer journeys, often at speed and at scale. Amitha, who leads product strategy in the AI and CX space, brings a grounded perspective shaped by years of working with large enterprises and complex contact center environments. What stood out in our discussion is how the real challenge is no longer about whether AI can handle customer interactions. In many cases, it already can. The issue is whether customers trust it enough to let it try. We unpack the growing perception gap: 73 percent of consumers still believe human agents resolve issues faster, even though AI systems can deliver near-instant responses. That disconnect often comes down to past experiences, from bots that fail to understand context to systems that trap users in frustrating loops with no clear way out. There is also a clear line that customers draw around where AI belongs. Routine, high-volume tasks such as password resets or appointment confirmations are widely accepted. But when conversations shift toward financial security, healthcare, or legal advice, expectations change. People want human judgment involved and reassurance that the outcome is reliable. What makes this conversation particularly relevant is the generational divide shaping expectations. Younger users are far more open to AI-led interactions, provided they work seamlessly. Older generations remain more cautious, often preferring the certainty of speaking with a human. That creates a design challenge for businesses trying to serve everyone without alienating anyone. Throughout the episode, Amitha emphasizes that trust is built through experience, not intention. That means testing AI systems in real-world conditions, monitoring how they perform over time, and ensuring that when things do go wrong, the transition to a human feels smooth and informed rather than abrupt and frustrating. This is not a conversation about replacing humans with machines. It is about understanding where AI can add speed and efficiency, where it should support human agents, and where it should step back entirely. The organizations getting this balance right are not the ones deploying AI the fastest, but the ones validating it most carefully before customers ever see it. As businesses race to embed AI at every touchpoint, a bigger question emerges. Are we building systems that customers actually trust, or are we creating new points of friction that push them away?   Useful Links Connect with Amitha on LinkedIn Survey Data Cyara Website Follow Cyara on LinkedIn

What does it really take to move AI from endless experimentation into something that creates real business value? In this episode, I sat down with Tom Alexander, Head of Innovation and Transformation at CrossCountry Consulting, to talk about why so many organizations still struggle to turn AI ambition into meaningful outcomes. Tom works closely with executive and CFO teams that are either unsure where to begin or frustrated that early AI efforts have not delivered what they hoped for. We talked about why this is rarely just a technology issue. In many cases, the real blockers are ownership, change management, weak alignment across the business, and a failure to connect AI initiatives to the problems that matter most. One of the big themes in our conversation was the need to treat AI as an enterprise-wide program rather than a collection of isolated tools. Tom shared how leaders can focus on business processes first, identify where automation can genuinely improve performance, and avoid getting distracted by hype. We also unpacked the growing accountability challenge around AI, including who should own it, how stakeholders can align, and why strong foundations in data, governance, and training matter so much. This episode is packed with practical takeaways for anyone trying to make sense of AI adoption inside a business. If you are trying to figure out where to start, how to scale, or how to avoid another stalled initiative, there is a lot in here for you. After listening, I would love to hear your thoughts. How is your organization approaching AI, and where do you think most businesses are still getting it wrong? Useful Links CrossCountry website Connect with Tom Alexander on LinkedIn Field Notes podcast

Is the endpoint still just a device, or has it quietly become one of the most important control points in modern enterprise security? Recording live from IGEL Now And Next in Miami, I sat down once again with Darren Fields for what has become an annual check-in on how fast the industry is really changing. And this time, the conversation feels very different. Over the last 12 months, the discussion has moved well beyond traditional endpoint management. From global supply chain pressure driven by AI demand to rising hardware costs and unpredictable refresh cycles, the assumptions that once shaped endpoint strategy are starting to fall apart. Darren shares how organizations are now being forced into difficult decisions, absorb rising costs, delay investment, or rethink the model entirely. We also explore how that shift is changing the conversation at the leadership level. What was once seen as a procurement decision is increasingly being reframed as a resilience strategy. Extending hardware life, reducing dependency on supply chains, and maintaining operational continuity are becoming just as important as performance and cost. Security, of course, sits at the center of it all. With the majority of breaches still originating at the endpoint, Darren highlights how organizations are starting to rethink where they focus their efforts. Rather than focusing solely on data centers and cloud environments, there is growing recognition that control, visibility, and enforcement must occur at the edge. The conversation also touches on the reality of modern cyber threats. From constant attack attempts to incidents that leave organizations offline for weeks, the challenge is no longer just restoring systems but restoring access. And that shift has major implications for how recovery and continuity are designed moving forward. We also look at the growing convergence of IT and OT, the role of contextual access, and the balancing act between stronger security and user experience. With organizations at very different stages of their journey, there is no single path forward, but there is a clear sense that change is already underway. So as the pace of technology, risk, and demand continues to accelerate, one question remains. Are organizations adapting fast enough, or are they still relying on models that no longer reflect the world they are operating in? What do you think, are we finally seeing a shift toward treating the endpoint as a strategic priority, or is there still a gap between awareness and action?

What does it really take to move from talking about Zero Trust… to actually making it work in the real world? Recording live from IGEL Now And Next in Miami, I caught up with John Walsh for what has now become something of a tradition, our third conversation together, and one that reflects just how much has changed in the last 12 months. When we last spoke, the focus was on securing the edge and rethinking security through a preventative lens. This time, the conversation has expanded from IT to OT, from devices to platforms, and from theory to real-world implementation across manufacturing floors, healthcare environments, and government organizations. John shared how IGEL is increasingly being adopted as a global standard across both IT and operational environments, bringing new challenges and new insights. From kiosks and signage on factory floors to shared workstations in hospitals, the need for persona-based and now context-aware access is becoming far more than a technical concept. It is shaping how organizations think about identity, risk, and control at scale. We also explored how the idea of the "adaptive secure desktop" is evolving beyond traditional VDI thinking. Instead of static devices, the focus is shifting toward environments that respond dynamically to the user, their role, their location, and the level of risk in that moment. It raises an important question. How do you deliver that level of control without introducing friction for the user? AI inevitably entered the conversation, but not in the way many might expect. Rather than focusing on features, John highlighted the acceleration of threat velocity. The time between vulnerability discovery and exploitation is shrinking rapidly, and with AI amplifying that speed, traditional detection and response models are struggling to keep up. The implication is clear. Security strategies need to shift toward prevention and control, not just reaction. We also touched on emerging challenges around agentic AI, non-human identities, and the need to apply Zero Trust principles beyond people to machines. As organizations begin to explore these new models, questions around identity, access, and guardrails are becoming more complex and more urgent. And throughout the conversation, one theme kept coming back and reducing complexity while increasing control. Whether it is through immutable operating systems, centralized policy enforcement, or contextual access, the goal is to simplify the environment while strengthening security outcomes. As organizations continue their journey toward modernization, one question remains: Are we still layering new technology onto old models, or are we ready to rethink how access, identity, and control are delivered from the ground up? What do you think, is Zero Trust finally becoming real at the endpoint, or is there still a gap between strategy and execution?

How long would it actually take your organization to recover every endpoint after a major cyber incident? Recording live from IGEL Now And Next in Miami, I sat down with James Millington to explore a question that most businesses think they've answered, but rarely have. Because when you move beyond theory and start mapping out the real process, the numbers tell a very different story. James shared examples from real organizations that tried to calculate recovery at scale. One estimated it would take over 5,000 person-hours to rebuild their estate. Another believed they could recover quickly, until they realized the scale of their environment made that assumption unrealistic. It raises a deeper question. Are we focusing too much on recovery and not enough on resilience?  The conversation quickly moved into what James calls the "endpoint recovery gap." While most organizations have invested heavily in data center resilience, failover environments, and backup strategies, far fewer have a clear plan for reconnecting users when endpoints are compromised. And without a working endpoint, even the most advanced infrastructure becomes inaccessible.  We also explored why so many organizations continue to rely on reimaging devices as a primary recovery strategy, despite the time, complexity, and operational disruption it creates. In many cases, it's not just slow. It's impractical at scale. And perhaps more concerning, some organizations still admit to having no defined plan at all. One of the most memorable moments in the conversation came through a simple analogy. For years, we've been carrying the weight of outdated endpoint strategies, even though the solution has been sitting in front of us. Just like it took thousands of years to put wheels on a suitcase, the shift toward simpler, more resilient models often requires a moment of realization before change actually happens. As application delivery continues to move toward SaaS, DaaS, and cloud environments, the role of the endpoint is also being redefined. Analysts are now calling for a move toward immutable, non-persistent endpoints that reduce attack surface and enable faster recovery. But as James points out, the real challenge is not awareness. It's an action. As organizations continue to invest in security, infrastructure, and AI, one question remains: Are we still planning for recovery from failure, or are we finally designing systems that avoid it in the first place? What do you think, are businesses ready to rethink endpoint strategy, or are we still carrying the baggage of the past?

What does it actually take to rethink the endpoint in a world shaped by AI, Zero Trust, and the growing convergence of IT and operational technology? Recording live from IGEL Now and Next in Miami, I sat down with Matthias Haas to unpack what he describes as a genuine transformation moment for enterprise computing. This wasn't a conversation about incremental change. It was about challenging long-held assumptions around devices, security models, and how work is delivered in modern organizations. Matthias shared how the idea of the "adaptive secure desktop" is moving beyond traditional thinking around VDI and desktop delivery. Instead of treating endpoints as static devices, the focus is shifting toward dynamic, context-aware environments that respond to who the user is, where they are, and what they need access to in that moment. It raises an important question for any organization. Are we still designing for devices, or for outcomes? We also explored the growing complexity that comes with flexibility. With multiple ways to deliver applications across SaaS, DaaS, browsers, and local environments, there's a real risk of recreating the same fragmented systems companies are trying to move away from. Matthias offered insight into how orchestration, policy enforcement, and centralized management can help bring order to that complexity without adding friction for users. Another key theme was the shift from static security models to continuous, contextual decision-making. As organizations move toward Zero Trust, the ability to evaluate risk in real time becomes essential. But that raises a delicate balance. How do you strengthen security without slowing people down? And how do you ensure that the user experience doesn't become the casualty of tighter controls? The conversation also touched on the challenges of bringing IT and OT environments together. While the opportunity to unify these worlds is significant, the realities are far more complex. Different risk tolerances, legacy systems, and operational priorities all come into play. Matthias offered a candid perspective on what it will take to make that convergence work in practice, not just in theory. So as enterprises continue to rethink their infrastructure in an AI-driven world, one question keeps coming up. Are we simply layering new technology onto old models, or are we ready to fundamentally change how the endpoint fits into the bigger picture? What do you think, are organizations truly ready to embrace adaptive, context-driven computing, or are we still holding on to outdated ways of working?

How do you rebuild an entire industry that most people accept as slow, fragmented, and frustrating? In this episode, I sit down with Dan Lifshits, co-founder of Dwelly, to explore how AI is being used to rethink the rental market from the inside out. What struck me most in this conversation is how Dwelly isn't approaching property management as a software layer you simply bolt on. Instead, they are acquiring rental agencies and rebuilding the operating model itself, embedding AI into every workflow, from tenant communication to maintenance coordination and rent collection. It is a very different mindset, and one that challenges how many businesses think about digital transformation. Dan brings a fascinating perspective shaped by his time competing in high-growth environments at companies like Uber and Gett. We talk about what those years taught him about scaling complex, operational businesses and how those lessons now apply to one of the largest and least digitized sectors in the economy. There is a clear parallel between ride-hailing and rentals, both are fragmented, both rely on two-sided marketplaces, and both have historically depended on manual processes that struggle to scale. As Dan explains, "long-term residential rentals ticks very similar boxes" to ride-hailing, which makes it ripe for reinvention. We also spend time unpacking what an AI-powered rollup actually means in practice. This is where the conversation becomes particularly interesting for founders and business leaders. Rather than selling software into traditional businesses and hoping for adoption, Dwelly takes control of both the operations and the technology. That allows them to redesign workflows, remove bottlenecks, and deliver a more consistent experience for landlords and tenants alike. The result is a model where a single operator can manage hundreds, even thousands, of properties with a level of service that would have been impossible just a few years ago. Of course, there are bigger implications here too. If this model works at scale, it raises questions about how many other service industries could be rebuilt in a similar way. It also highlights the growing role of venture-backed rollups, particularly with firms like General Catalyst backing this approach as a new investment category. But it is not without challenges. Changing operational behavior, integrating acquisitions, and maintaining service quality while scaling fast are all complex problems that cannot be solved by technology alone. This episode left me thinking about where the real value in AI sits. Is it in the tools themselves, or in the willingness to rethink how a business actually operates? And if AI can transform something as established as property management, which industries are next in line for the same kind of reinvention? I would love to hear your thoughts. Are AI-powered rollups the future of service industries, or do they introduce a new set of risks we are only beginning to understand?

How are businesses supposed to grow when technology is moving faster than regulation, customer expectations keep shifting, and AI is changing the rules in real time? In this episode, I sat down with Derya Matras, Vice President of EMEA at Meta, to talk about what growth really looks like for businesses operating in Europe, the Middle East, and Africa right now. This was a fascinating conversation because it went far beyond the usual talking points around AI and advertising. Derya brought a broader view of the pressure many businesses are under today, from macroeconomic uncertainty and political complexity to changing consumer behavior, tighter margins, and the need to adapt to a world where AI is now part of everyday decision making. What really stood out to me was her point that this moment is about far more than adopting new tools. It is about culture, leadership, and having the discipline to know what you are actually trying to achieve. Derya spoke about the importance of having a clear North Star goal, getting the foundations right, and making sure businesses are not simply adding AI into broken systems or unclear strategies. Because as she put it, AI can make everything more powerful, but it can also amplify mistakes. That is such an important point, especially at a time when so many companies are racing to show they are doing something with AI without always knowing what success should look like. We also explored how Meta sees its role in supporting growth across Europe's digital economy. Derya shared insights into how Meta's platforms are helping businesses of all sizes reach customers in ways they simply could not do on their own. For large companies, that may mean better measurement, faster optimization, and more personalized engagement. But for smaller businesses, the stakes can be even higher. She shared examples that brought those numbers to life, including entrepreneurs using Instagram and WhatsApp to reach global markets, support their families, and create jobs in ways that would have been out of reach just a few years ago. Another part of the conversation I found especially interesting was the tension between innovation and regulation in Europe. Derya was honest about how complicated and fragmented the environment has become, and how that complexity can slow progress or delay the rollout of new products. At the same time, she made a strong case that Europe still has a real opportunity ahead if it can find the right balance. That balance matters not only for big tech companies, but for startups, small businesses, creators, and the wider economy that increasingly depends on digital tools to compete and grow. We also talked about creativity, measurement, AI assistants, wearables, and even how these technologies are beginning to shape life at home as much as at work. It all made for a conversation that felt very current, but also deeply practical. So as AI becomes woven into advertising, business operations, and everyday life, are organizations truly building the foundations they need to benefit from it, or are they still chasing the next shiny thing? And what do you think Europe needs to get right to make sure innovation and opportunity can keep moving forward?

What happens when AI ambition starts moving faster than the infrastructure built to support it? In this episode, I spoke with Lee Caswell, SVP of Product and Solutions at Nutanix, about the latest Enterprise Cloud Index and what it tells us about where enterprise IT really is right now. There is no shortage of AI headlines, product launches, and promises about what comes next, but this conversation gets behind the noise and into the operational reality that many business and technology leaders are now facing. As Lee explained, AI is not arriving in isolation. It is pulling containers, data strategy, hardware decisions, governance, and application modernization along with it. One of the biggest themes in our conversation was the growing link between AI workloads and container adoption. Lee made the point that applications still sit at the top of the org chart, and infrastructure exists to serve them. As more AI-enabled applications are built by developers who favor containers and Kubernetes-based environments, enterprises are being pushed to rethink how they support those new workloads. We talked about why containers are becoming such an important part of modern application strategy, how they help organizations handle distributed AI use cases, and why many businesses are trying to balance speed and flexibility without giving up the resilience and control they have spent years building into their infrastructure. We also spent time on the less glamorous side of AI adoption, but arguably the part that matters most. Shadow AI, data sovereignty, unpredictable token costs, and infrastructure readiness are all becoming board-level issues. Lee shared why so many organizations are realizing that AI cannot simply be layered onto existing systems without deeper changes underneath. New hardware, new software, new governance models, and a more consistent approach across edge, on-prem, private cloud, and public cloud environments are all part of the picture now. What I enjoyed most about this conversation was that it never framed AI as magic. It framed it as work. Real work that demands better architecture, sharper oversight, and faster decision-making from IT teams that are already under pressure. So if your organization is racing to adopt AI, are you also building the foundation needed to support it responsibly, and where do you think the biggest risk sits right now? Share your thoughts with me.

How far can we trust research that is generated without asking a single human being? In this episode, I sat down with Jordan Harper from Qualtrics to unpack one of the most talked-about developments at the Qualtrics X4 Summit, synthetic research. It is a topic that sparks curiosity, excitement, and a fair amount of skepticism in equal measure. And honestly, that tension is exactly why this conversation matters. Jordan brings a rare mix of scientific thinking and real-world technology experience, which makes him well placed to cut through the hype. We explored what synthetic panels actually are, and just as importantly, what they are not. While many assume this is simply about asking a large language model for answers, the reality is far more nuanced. The approach Jordan and his team are building is grounded in how humans respond to surveys, trained on vast datasets to reflect the inconsistencies, biases, and unpredictability that make human insight valuable in the first place. What stood out throughout our conversation was the idea that synthetic research should be seen as additive rather than a replacement. It offers speed, flexibility, and the ability to test ideas quickly, but it does not replace the depth and lived experience that only real people can provide. In fact, some of the most interesting insights come from comparing synthetic responses with human ones, revealing patterns, biases, and even blind spots in traditional research methods. We also got into the practical side of things. From controlling for issues like survey fatigue and social desirability bias, to experimenting with question design in ways that would be difficult with human respondents, synthetic research opens up new ways of working. At the same time, it raises important questions about validation, trust, and where to draw the line when decisions carry real-world consequences. For me, this episode is about perspective. In a world where AI is accelerating everything, it can be tempting to look for shortcuts. But as Jordan explains, the real value comes from using these tools thoughtfully, alongside human insight rather than in place of it. So as this technology continues to evolve, how should researchers and business leaders strike that balance? And where could synthetic research help you ask better questions before you make your next big decision?

What does customer experience really mean when every company claims to put the customer first? In this episode, I sat down with Jeannie Walters, founder of Experience Investigators, to unpack why so many organizations talk about customer experience yet struggle to turn it into something that drives real business outcomes. With more than two decades of hands-on work across industries, Jeannie brings a perspective that cuts through the noise and focuses on what actually works inside complex organizations. Our conversation took place at the Qualtrics X4 Summit, where one theme kept resurfacing. While AI dominated headlines, there was a noticeable shift back toward strategy, discipline, and accountability.  Jeannie has been making that case for years. As she explained, customer experience cannot sit on the sidelines as a reporting function or a collection of metrics. It has to become a daily business discipline, one that shapes decisions across leadership, operations, and culture. We explored the thinking behind her new book, Experience Is Everything, and the patterns she has seen repeated across organizations. Leaders invest in tools, gather feedback, and build dashboards, yet still struggle to connect those efforts to outcomes like retention, revenue, and long-term trust. Jeannie argues that the missing piece is often clarity.  What does customer-centric actually mean for your organization? What are you trying to achieve, and how will you measure success in a way that matters to the business? Without those answers, even the best technology will fall short. There were also some honest reflections on AI. While it is accelerating everything, it also raises the stakes. Customers are becoming more aware of how their data is used, and trust is becoming harder to earn and easier to lose. That creates both an opportunity and a risk. Organizations that treat customer experience as a strategic priority can use AI to strengthen relationships, while those that treat it as a cost center may simply scale poor experiences faster. What stood out most in this conversation was the shift from theory to action. From redefining teams that were stuck reporting on metrics to empowering them to lead business change, Jeannie shared practical examples of how mindset, strategy, and execution come together. It is a reminder that customer experience is not owned by one team. It is something that either shows up in every interaction or not at all. So as AI continues to reshape how businesses operate, are we using it to deepen trust and deliver better experiences, or are we simply amplifying what already exists? And where does customer experience truly sit inside your organization today?

What does a great patient experience really look like when people are at their most vulnerable? In this episode, I sat down with Stanford Health Care's SVP and Chief Patient Experience and Operational Performance Officer, Alpa Vyas, to explore how one of the world's leading healthcare organizations is rethinking the human side of care. From the outside, healthcare is often seen as a system of processes, technology, and clinical outcomes. But as Alpa explains, every interaction sits within a deeply emotional moment in someone's life, where fear, uncertainty, and complexity collide. That reality shapes everything. Our conversation goes back to the early days of Stanford's transformation, where Alpa recognized a gap that many organizations still struggle with today. Improvement efforts were underway, systems were being optimized, yet the patient voice was largely absent. Inspired by design thinking principles from Stanford's own d.school, her team began with empathy as the foundation. That shift changed the direction of everything that followed, from how feedback was gathered to how decisions were made across the organization. We also explored the role of technology, and where it truly fits. There is often a temptation to lead with AI or automation, but Alpa brings the focus back to culture, behavior, and trust. Technology, including platforms like Qualtrics, became powerful once the right questions were being asked and the right mindset was in place. Moving from delayed paper surveys to real-time feedback transformed not only how quickly issues could be addressed, but how patients felt heard. One story stood out where a patient received a follow-up call before even leaving the parking lot, a simple moment that redefined their perception of care. We also touched on "Operation Blue Sky," an initiative that looks beyond traditional surveys to capture insight from call recordings, messages, and other unstructured data sources. It opens the door to a future where healthcare providers can anticipate problems before they happen and intervene at the right moment. That raises important questions around pace, trust, and readiness, especially in an industry that has good reason to move carefully. This episode is ultimately a conversation about balance. Between innovation and responsibility, between efficiency and empathy, and between data and human connection. So how do we ensure that as healthcare becomes more advanced, it also becomes more human? And what lessons from this journey could apply far beyond healthcare?

What happens when customer experience stops being a soft metric and starts becoming a direct driver of revenue, retention, and real-time action? In this episode, I sat down with Jeff Gelfuso, SVP and Chief Product and Experience Officer at Qualtrics, during X4 Summit in Seattle to talk about how AI is changing the way businesses understand and improve customer relationships. Jeff shared how his role sits at the point where product, experience, and business outcomes meet, helping customers use Qualtrics in ways that are both practical and measurable. One of the biggest themes in our conversation was the shift from simply listening to customers to actually doing something in the moment. For years, many companies have relied on surveys, dashboards, and reports that told them what had already gone wrong. Jeff explained how that model is changing fast. With AI, organizations can now understand signals as they happen and trigger action before a poor experience turns into churn, frustration, or lost revenue. We talked about examples from brands like Marriott and TruGreen, and this is where the conversation became especially interesting. In TruGreen's case, AI-powered analysis helped reveal that service quality, not price, was the real reason customers were leaving. That kind of insight changed the conversation from guesswork to financial impact. When one point of retention can mean $10 million in annual revenue, experience suddenly becomes a boardroom issue, not just a customer service metric. Jeff also offered a refreshingly clear view on agentic AI. Instead of treating it as another layer of hype, he described it as a way to turn experience data into action, using context to help businesses close the loop faster and with greater precision. That means moving beyond smarter dashboards and toward systems that can surface priorities, recommend next steps, and help teams act without getting buried in complexity. Another standout part of the discussion was how Qualtrics is helping customers move beyond pilot purgatory. Jeff was candid that meaningful AI progress still takes work, focus, and the discipline to solve the right problems first. The companies seeing real value are not trying to do everything at once. They are identifying specific use cases, tying them to real business outcomes, and building from there. What I enjoyed most about this conversation was how clearly Jeff connected technology to human experience. Yes, there was plenty of discussion around AI, automation, and context, but at the heart of it all was something much simpler. Better experiences build stronger relationships, and stronger relationships drive loyalty, trust, and growth. So if your business is still treating experience as a nice-to-have instead of a measurable driver of performance, what might you be missing right in front of you? I would love to hear your thoughts after listening.

What does it really mean to lead in AI when the headlines are loud, the claims are endless, and the real signals are often buried under hype? In this episode, I sit down with Ed White from Clarivate to make sense of one of the most important questions in technology right now, who is actually leading the AI innovation race, and what does the data really tell us?  Ed leads the Clarivate Centre for IP and Innovation Research, where his team analyzes enormous volumes of intellectual property and innovation data to understand where technology is heading, who is building it, and which ideas are likely to shape the future. That matters because AI is no longer a side story inside tech. It is becoming an economic issue, a business issue, and increasingly a geopolitical one too. Our conversation centers on fresh Clarivate research showing that AI patent filings passed 1.1 million overall by 2025, with growth accelerating at a pace that is hard to ignore. Ed helps unpack what that actually means in practical terms.   I found this especially interesting because the report does not simply point to the familiar names everyone already talks about. It also highlights academic institutions, automotive companies, and businesses working behind the scenes with far less noise. What I enjoyed most about this discussion is that Ed brings a rare mix of technical depth and real clarity. He does not just throw out huge numbers and leave them hanging there. He explains what they mean for investors, enterprise leaders, governments, and anyone trying to understand where this market is heading next.  We also get into one of the biggest tensions in AI today, the balance between speed and assurance. That part really stayed with me. In a market obsessed with moving fast, Ed makes a strong case that trust, explainability, and usability may end up shaping who actually wins. This is a conversation about much more than patents. It is about power, strategy, timing, and how innovation spreads across borders, industries, and institutions. If you want to cut through the noise and hear a more data-led view of the AI race, this episode will give you plenty to think about. As always, I would love to hear what stood out to you most after listening, so please share your thoughts with me. When you look at the AI race today, do you think the real leaders are the companies making the most noise, or the ones quietly building for the long term?

What does it really take to move AI from impressive demos into the hands of the people who keep the world running every day? In this episode of Tech Talks Daily, I sat down with Kriti Sharma, CEO of IFS Nexus Black, to explore a side of AI that rarely gets the spotlight. While much of the conversation around artificial intelligence focuses on chatbots and copilots, Kriti is working in environments where failure is not an option. Manufacturing plants, energy grids, airlines, and field service operations all depend on precision, experience, and consistency. What struck me early in our conversation was how she reframes the entire AI debate. The challenge is not building the technology, it is building trust in it. Kriti's journey into AI began long before it became a boardroom priority. From building her first robot as a teenager to advising global organizations and policymakers, she has always focused on solving real problems rather than chasing trends. That perspective carries through into her work today, where she spends time on factory floors wearing safety gear alongside engineers and technicians.  It is a hands-on approach that reveals something many leaders miss. People do not adopt AI because it is advanced. They adopt it when it solves a problem they recognize in their day-to-day work. One of the most interesting themes we explored was the widening gap between what AI can do and how quickly organizations are ready to use it. Kriti described how that gap plays out on the ground, especially among deskless workers who make up the majority of the global workforce. In these environments, the conversation is far less about replacing jobs and far more about preserving knowledge, improving consistency, and helping people perform at their best. When a veteran worker with decades of experience walks out the door, that expertise often leaves with them. AI, when designed well, can help capture and share that knowledge across an entire workforce. We also discussed how IFS Nexus Black is tackling what many describe as "pilot purgatory," where companies experiment with AI but struggle to deploy it at scale. Kriti shared how building solutions alongside customers, rather than handing over generic tools, leads to faster adoption and measurable results. Real-world examples brought this to life, including how industrial AI is helping organizations move from reactive firefighting to proactive decision-making, reducing downtime and improving operational performance in ways that directly impact the bottom line. As our conversation moved toward the future, Kriti offered a clear message for leaders. The best way to prepare for AI is to start using it. Not as a novelty, but as a daily tool that can amplify how work gets done. The organizations that encourage experimentation and share those learnings across teams are the ones most likely to see real impact. So as AI continues to evolve at pace, the question is no longer whether the technology is ready. It is whether organizations and their people are ready to meet it halfway, and what happens if they are not?

How are global payment systems quietly shifting beneath our feet, and what does that mean for businesses trying to grow across borders? In this episode of Tech Talks Daily, I sat down with Stuart Neal, CEO of Boku, to unpack a transformation that many consumers barely notice but every global business feels. Payments have long been dominated by familiar names like Visa and Mastercard, yet Stuart explains how that dominance is slowly being challenged by a surge in local payment methods. From mobile wallets in emerging markets to direct carrier billing in places where credit cards are far from universal, the way people pay is becoming far more fragmented, and far more local. What stood out for me in this conversation was the geopolitical and economic dimension behind it all. Stuart highlighted how events like the pandemic and even global conflicts have pushed governments and central banks to rethink their reliance on external payment networks. When entire payment systems can be switched off overnight, it forces countries to consider building their own infrastructure. That shift is not only about sovereignty, it is about control over financial ecosystems, consumer behavior, and ultimately economic stability. We also explored what this means for businesses still operating with a card-first mindset. While card payments are not disappearing, their relative share is being overtaken by a growing ecosystem of alternative methods. That creates both opportunity and complexity. Companies now face the challenge of integrating hundreds of payment options across multiple markets, each with its own regulations, currencies, and customer expectations. Stuart offered a candid view that for most organizations, building this infrastructure alone is unrealistic, which is why aggregation platforms like Boku are stepping in to bridge that gap. The conversation then turned toward the future, particularly the rise of agentic AI and what Stuart described as the "last mile problem" in payments. While AI may soon handle discovery and purchasing decisions, the moment of payment still requires trust, authentication, and verification. That friction is not a flaw, it is a safeguard, and it raises important questions about how seamless commerce can really become. We also touched on subscription fatigue, cross-border expansion, and the lessons global brands like Microsoft and Netflix have learned about meeting customers where they are. One thing became clear throughout our discussion. If you ignore local payment preferences, you are effectively turning away a large portion of your potential audience. So as payment methods continue to evolve and diversify, are businesses ready to rethink their assumptions about how money moves, or will they risk being left behind in a world that is becoming increasingly local at scale?

What does it really take to turn a massive AI infrastructure investment into actual business value? In this episode, I'm joined by Alex Bouzari, founder and CEO of DDN, for a conversation that gets right to the heart of where AI infrastructure is heading next. There is a lot of noise in the market about faster chips, larger models, and bigger data centers, but Alex argues that the real story has changed. According to him, GPUs are no longer the main constraint. The true bottleneck now lies in the data layer, where data is moved, cached, served, and managed across increasingly complex AI environments. That shift matters because many organizations are still thinking about AI in terms of hardware acquisition. Buy more GPUs, add more power, build more capacity. But as Alex explains, that mindset misses the bigger picture.  If your data architecture cannot keep pace, those expensive systems stall, efficiency drops, and the return on investment quickly becomes shaky. It was a timely discussion, especially as NVIDIA's Rubin platform points toward rack-scale AI factories where compute, networking, storage, and offload all need to work together as one operational system. One part I found especially interesting was Alex's focus on measuring efficiency. He argued that the future winners in AI will not simply be the companies with the most hardware. They will be the ones who think like industrial operators, measuring cost per token, rack utilization, time-to-value, and power consumption per unit of intelligence output. That is a very different conversation from the hype cycle, and it is one that business leaders need to hear. AI value is no longer about showing that something can work. It is about proving that it can work predictably, securely, and economically at scale. We also talked about DDN's collaboration with NVIDIA, the role of BlueField-4 DPUs, and why inference performance now depends on intelligent memory architecture and data movement just as much as raw compute. Alex shared how DDN is helping customers reach up to 99 percent GPU utilization and reduce time to first token for long context workloads. Those numbers are impressive on their own, but what matters most is what they represent—better throughput, lower waste, and AI systems that move from science project to production reality. There is also an important leadership lesson running through this conversation. DDN has been profitable for over a decade, powers more than one million GPUs worldwide, and has built its business by staying close to real customer pain points. Alex speaks with the kind of clarity that comes from building through constraints rather than simply talking around them. If AI factories are going to define the next phase of enterprise technology, how should leaders rethink infrastructure, efficiency, and value creation before they invest in the next wave, and what do you think?

Are employees really ready for AI in the workplace, or are we moving faster than people can realistically keep up? In this episode, I'm joined by David Evans, Chief Product Strategist at GoTo, to explore what is actually happening inside organizations as AI becomes part of everyday work. There is a growing assumption that businesses are already well on their way, with employees confidently using AI tools and leaders rolling out strategies at pace. But David brings a more measured view, backed by research and real-world insight, that suggests the picture is far more complex. One of the biggest themes in our conversation is the gap between expectation and reality. Many companies assume that younger employees, particularly Gen Z, naturally understand how to use AI in a professional setting. David challenges that idea directly. He explains that while familiarity with technology is high, the ability to apply AI effectively, responsibly, and in a business context is something that every generation is still learning. Without clear guidance, training, and governance, organizations risk creating confusion rather than progress. We also talk about how AI is quietly becoming embedded in everyday workflows. Instead of replacing roles outright, it is helping people shift their focus toward higher-value work. That shift is already visible in areas like customer support, where contact centers are evolving through smarter automation, better tools for agents, and a growing acceptance of remote and distributed teams. David shares what this could look like over the next year, and why the balance between human and machine will remain central to delivering good experiences. Another area we explore is the growing need for integration. Many organizations are dealing with fragmented communication tools, rising costs, and increasing complexity. David explains why there is a clear move toward unified platforms that bring communication, collaboration, and AI together in a more cohesive way. That includes the rise of conversational AI, with tools like AI receptionists becoming easier to deploy and more widely trusted. Of course, none of this happens without challenges. Security, data privacy, and the risks associated with shadow IT and generative AI are becoming more visible. David outlines how technology providers are responding, and what leaders need to think about as they balance innovation with responsibility. This conversation offers a grounded look at where workplace AI is heading, cutting through assumptions and focusing on what leaders need to understand right now. So as AI becomes part of the fabric of everyday work, are organizations doing enough to support their people, or are they expecting too much too soon?

How can companies be drowning in customer data and still struggle to make better decisions? In this episode, I speak with Jochem van der Veer, CEO and co-founder of TheyDo, about a problem that many business leaders quietly recognize but rarely solve. Organizations are investing heavily in customer experience and AI, yet the results often fall short. There is more data than ever before, more dashboards, more reporting, and still a disconnect between insight and action. Jochem offers a refreshing perspective shaped by his work with global brands like Ford, Atlassian, Cisco, and Home Depot. He explains that the issue is not a lack of data, but a lack of alignment. Teams operate in silos, each working with their own version of the truth, which leads to fragmented decisions that make sense internally but fail from the customer's point of view. It is not intentional, but the outcome is the same. A disconnected experience that slows progress and creates hidden costs across the business. We spend time unpacking what this looks like in practice. Many customer experience teams are still focused on collecting and reporting data rather than influencing decisions. Insights travel up the organization, often reaching senior leadership, but rarely translate into meaningful action. That gap, as Jochem describes it, turns customer experience into a cost center rather than a driver of growth. What makes this conversation particularly relevant right now is the role of AI. While AI has made it easier to process vast amounts of unstructured data, it has also exposed how unprepared many organizations are to act on it. Jochem shares how experience intelligence is emerging as a new way of thinking, one that connects customer feedback, operational data, and business outcomes into a single, actionable view. It shifts the focus from understanding what happened to deciding what to do next. We also explore the partnership between TheyDo and PwC, and how combining structured frameworks with journey management technology can help organizations move from strategy to execution. From reducing wasted investment to identifying the real root causes behind customer issues, there is a clear opportunity to rethink how decisions are made. This episode challenges some widely held assumptions, including the idea that customer experience is a standalone function. Instead, it is becoming a capability that needs to be embedded across the entire organization. So as AI continues to accelerate the pace of business, are companies ready to move beyond reporting and finally turn customer insight into meaningful action?

What happens when financial markets stop reacting to data and start reacting to narratives in real time? In this episode, I'm joined by Wilson Chan, CEO and founder of Permutable AI, to explore how artificial intelligence is reshaping the way financial institutions interpret the world around them. Wilson brings a rare perspective, combining years of experience as a trader with a deep background in computer science, and it shows in the way he describes this shift.  We talk about how markets are moving away from traditional quant models and toward AI-native systems that can reason over vast amounts of unstructured global information. That includes everything from policy changes and geopolitical events to the subtle ways narratives form and spread across media. What stood out to me in this conversation is how Wilson challenges the idea that markets are driven purely by fundamentals. Instead, he argues that perception and reality are increasingly intertwined.  If enough people believe a story, that belief can influence price movements just as much as financial performance. Permutable AI is built on this idea, scanning hundreds of thousands of articles in real time to identify how narratives evolve and impact commodities, energy markets, and currencies. It's a fascinating shift that raises important questions about how investors separate meaningful insight from noise. We also explore the role of vertical LLMs and why generic AI models fall short in financial environments. Wilson explains how embedding financial relationships and ontology directly into models creates outputs that are structured, traceable, and ready for decision-making. That focus on explainability and auditability becomes even more important as AI systems take on greater responsibility. If something goes wrong, understanding why it happened is what maintains trust, and without that, adoption quickly stalls. There's also a broader conversation here about where all of this is heading. From multi-agent systems replacing traditional analytics stacks to the ambition to build a full-world simulator for capital markets, it feels like we are at the early stages of something much bigger. But at the same time, Wilson is honest about the challenges, from integration hurdles to the human skills gap that continues to hold many organizations back. So if markets are now shaped by narratives, AI reasoning, and real-time global signals, how should business leaders and investors rethink their decision-making in the future?

What does customer experience look like inside a company most people associate with switches, infrastructure, and engineering rather than surveys, empathy, and brand perception? In this episode, recorded at the Qualtrics X4 event in Seattle, I sit down with Jerome Boissou, Head of Global Customer and Brand Experience at Legrand. Jerome has been with the company for 28 years and now leads a customer experience program designed to help Legrand better understand a customer base that is changing fast.  This matters because Legrand is no longer serving only its traditional markets. The company now operates across a huge product portfolio, serves commercial buildings as well as residential markets, and plays a significant role in areas such as data centers and hospitality. At the heart of our conversation is Legrand's "Best Of Us" program, which was originally launched in 2018 and then revamped in 2021. Jerome explains that the original focus was on personas and journey mapping, but the company soon realized it needed a more quantitative approach too. What followed was a broader strategy built around three connected pillars: customer satisfaction, customer centricity, and brand equity. Rather than treating customer experience as a dashboard exercise, Legrand is using those pillars to improve business performance, spread customer knowledge internally, and help teams understand what different customer groups really want, expect, and struggle with. One of the strongest themes in this conversation is that feedback without action creates frustration. Jerome is very clear on that point. He explains how Legrand built a "close the loop" process, then went further with what the company calls a "customer room" process. That means identifying pain points and weak signals, routing them to the right internal teams, tracking them with KPIs, and making sure action follows. He shares that 100 percent of detractors are meant to be handled through that closed-loop approach, and that around 80 percent of pain points can be solved as quick wins. That is a refreshing reminder that customer experience only matters when it changes something. We also talk about the scale of measuring experience in a global B2B organization. Legrand runs yearly relational surveys for both direct and indirect customers, covering around 50 different personas, and supplements that with transactional surveys across 17 touchpoints. These include digital interactions, training, product launches, and post-case feedback from call centers.  Jerome explains how Qualtrics became a key part of making that global program work, helping Legrand roll out surveys worldwide and giving teams a way to analyze feedback more easily and consistently. Of course, this being a tech podcast recorded at X4, we also get into AI. But what stood out to me is that Jerome does not talk about AI as a magic layer dropped on top of everything. He talks about context. In fact, context becomes one of the defining ideas in our conversation. Capturing feedback is useful, but understanding the environment around that feedback is what allows better decisions to happen. For Jerome, that is where AI becomes more useful, especially when it is trained within the reality of Legrand's complex markets rather than operating as a generic tool. Another part of this episode I found especially interesting is how Legrand brings employees into the customer experience process. Jerome shares an example of sending the same surveys to employees and asking them to answer from the customer's point of view. By comparing employee perception with actual customer feedback, Legrand can spot gaps, adjust training, and help teams build more empathy. In one case, factory teams thought customers were far less satisfied than they really were, simply because the internal metrics they saw every day focused only on pressure and output. Reframing that with real customer satisfaction data, including a product quality satisfaction score of around 95 percent, helped restore pride and perspective. This episode is really about something bigger than surveys or software. It is about how a global company can embed customer thinking into the culture, make people feel part of the process, and use data in a way that stays human. Jerome makes a strong case that customer experience in B2B is not separate from performance. It shapes brand perception, trust, internal alignment, and ultimately business outcomes. I'd love to hear your thoughts. How is your organization making sure customer feedback leads to action rather than just another report?

What does it take to turn millions of customer interactions into meaningful relationships instead of missed opportunities? In this episode, recorded live at the Qualtrics X4 Summit in Seattle, I sit down with James Bauman, Senior Director and Head of Experience, Analytics, and Insights at TruGreen. James leads customer experience, analytics, and retention strategy across a business that manages around 60 million customer touchpoints every year. And as he explains, that scale creates both opportunity and risk. At the center of our conversation is a challenge he describes as the "leaky bucket." TruGreen was investing heavily in acquiring customers, but too many were slipping away due to inconsistent experiences and missed moments. The real question became how to understand what customers actually need, when they need it, and how to respond in a way that builds trust and long-term loyalty. We explore how TruGreen built an omnichannel customer experience program designed to listen across every interaction, from digital channels to service calls, and connect that feedback with real customer behavior. But what stood out to me was how they moved beyond simply collecting feedback and into taking action in the moment. That's where AI agents come in. Rather than relying solely on traditional follow-up processes, TruGreen is now embedding AI directly into customer check-ins and surveys. These agents respond in real time, using context from the customer's history and recent interactions to provide relevant, immediate support. It changes the experience from something reactive to something far more responsive. The impact has been significant. James shares how AI agents are now addressing around 51% of customer concerns upfront and cutting escalations by more than 30%. At the same time, they are freeing up human teams to focus on the conversations that truly require empathy and relationship-building, rather than spending time on repetitive follow-ups that may never get a response. We also talk about the reality behind making this work. There's no shortcut. The speed of implementation came from the groundwork TruGreen had already put in place, building a strong data foundation and connecting systems across the business. Without that, the AI would lack the context needed to be useful. James also challenges some of the common narratives around AI. It's not something you can simply switch on and expect instant results. But it's also far from hype when applied thoughtfully. In his experience, AI agents can deliver real value, both in customer outcomes and business performance, when they are placed in the right moments and supported by the right data. For me, this conversation is a reminder that customer experience is shifting. It's moving away from slow feedback loops and into something far more immediate, where businesses can listen, understand, and act in real time. And I'd love to hear your perspective. Are you seeing AI agents genuinely improve customer experience in your organization, or are you still trying to figure out where they fit? Useful Links Connect with James Bauman Learn more about TruGreen Qualtrics X4 Summit    

What does it really take to move from AI hype to something that actually works inside a business? In this episode, I sit down with Shibani Ahuja, SVP of Enterprise IT Strategy at Salesforce, to talk about why so many enterprise AI projects stall long before they deliver real value. While the market is full of noise around agents, copilots, and automation, Shibani makes the case that the real issue is often much simpler and much harder at the same time. Design. She explains why model capability alone will never rescue poor architecture, weak governance, or unclear data ownership. Our conversation goes well beyond the usual agentic AI headlines. Shibani shares what she learned from speaking with hundreds of C-suite leaders over the past year, and why many early enterprise AI conversations were too focused on models instead of ecosystems. We unpack the difference between predictive, generative, and agentic AI, why trusted data means more than having lots of information, and how Salesforce's own internal journey revealed conflicting knowledge, governance gaps, and the importance of determinism in enterprise settings. I also loved Shibani's perspective on the human side of this transformation. We talk about why successful organizations are framing agents as a capacity multiplier rather than a headcount story, how to bring employees along through visible wins and shared learning, and why the best starting point is often a simple, boring use case that removes pain for frontline teams. She also shares her thoughts on the eight design principles for the agentic enterprise, the myths that frustrate her most, and what will separate the leaders from the laggards over the next 18 to 24 months. This is a conversation for anyone feeling pressure to do something with AI, but wanting a clearer view of what meaningful progress actually looks like. Are businesses building the right foundations for an agentic future, or are too many still mistaking experimentation for strategy? Have a listen and let me know your thoughts. Useful Links Connect with Shibani Ahuja Agentic Enterprise Architecture    

How is AI reshaping our relationship with work, and what does that mean for the tools we rely on every day? In this episode of Tech Talks Daily, I'm joined by Cory McElroy, Vice President of Commercial Product Management at HP. Our conversation begins with a reflection on one of the most famous garages in technology history. The original HP garage in Palo Alto is often described as the birthplace of Silicon Valley, and standing there recently reminded me how far the industry has come since those early days. But as Cory explains, we may be entering another turning point. The nature of work has shifted rapidly in just a few years. Hybrid work is now the norm for millions of people, and expectations around workplace technology have changed with it. Employees no longer see technology as a basic productivity tool. They expect it to adapt to them, reduce friction, and help them focus on meaningful work. Cory shares insights from HP's Work Relationship Index, which highlights a striking reality. Only around 20 percent of employees say they have a healthy relationship with work. That number sounds concerning at first, but it also points to an opportunity. When organizations provide the right tools and experiences, employees become more productive, more creative, and more likely to stay. A big theme throughout our conversation is the growing role of AI directly on devices. Running AI locally on PCs changes how people interact with technology. Tasks that once took hours, such as analyzing documents or extracting insights from data, can now happen almost instantly. In some internal deployments at HP, employees reported saving up to four hours each week. We also talk about the hardware innovations that are emerging in response to this shift. Cory explains how new devices like the HP EliteBook X and the EliteBoard reflect a rethink of the PC itself. The EliteBoard, for example, integrates a full PC inside a keyboard, allowing users to connect to any display and instantly access desktop-level performance. It is a design that reflects the flexibility people now expect from modern workspaces. Looking ahead, Cory believes the next few years will bring even bigger change. Devices will increasingly understand context, connect seamlessly with other tools, and respond to natural language requests. Instead of jumping between multiple applications to complete a task, users may simply ask their device to assemble information and produce the outcome they need. So as AI becomes embedded into the devices we use every day and work continues to evolve, what would a truly frictionless workday look like for you, and how will your relationship with technology change as a result?

How do you secure a modern business when identities no longer belong only to employees, but also to partners, machines, applications, and increasingly AI agents? In this episode of Tech Talks Daily, I sat down with Paul Zolfaghari, President of Saviynt, to unpack why identity security has moved from a background IT function to one of the defining challenges facing modern enterprises. Over the past decade, the identity problem has expanded far beyond the traditional office worker logging into internal systems. Today's organizations must manage access across a vast digital ecosystem that includes contractors, suppliers, customers, APIs, machines, and now autonomous AI agents. Paul explains how this shift has fundamentally changed the way security leaders think about identity governance. The challenge is no longer limited to preventing unauthorized access from outside attackers. Instead, companies must manage the complex question of who, or what, should have access to specific data, systems, and processes at any given moment. When thousands of employees, partners, and automated systems interact across multiple cloud platforms, the complexity grows rapidly. We also explore how the rise of non-human identities is reshaping the security landscape. Machines, software services, and AI agents now operate alongside human employees inside enterprise environments. In many cases, these digital identities are already beginning to outnumber people. As AI agents gain the ability to gather information, adapt to context, and take actions autonomously, organizations must rethink how access permissions are granted, monitored, and governed. Another theme that emerged during our conversation is the idea that identity security is not only about protection. While it clearly sits within the cybersecurity domain, Paul argues that identity governance also acts as a business enabler. When the right people and systems can access the right information at the right time, organizations operate more efficiently and collaborate more effectively across complex supply chains and partner ecosystems. We also discussed findings from Saviynt's CISO AI Risk Report, which highlights a growing concern among security leaders. AI adoption is accelerating rapidly, often moving faster than the governance frameworks designed to manage it. This creates a challenge for organizations trying to adopt AI responsibly while maintaining visibility and control over how these technologies interact with enterprise systems. With more than 600 enterprise customers and a recent $700 million growth investment backing its expansion, Saviynt is operating in a market that many investors now view as one of the defining layers of modern digital infrastructure. Identity, in many ways, is becoming the control plane for how businesses operate in an AI driven world. Looking ahead, Paul believes organizations must begin preparing for a future where digital identities dramatically outnumber human employees. That shift will require new approaches to governance, visibility, and control. So as AI adoption accelerates and businesses continue expanding across cloud platforms and digital ecosystems, one question becomes impossible to ignore. Is identity security ready to serve as the foundation for how organizations operate in the next decade? Useful Links Connect with Paul Zolfaghari Check out the Saviynt Website Follow on Facebook, LinkedIn, and X

How prepared are organizations for a world where today's encrypted communications could be quietly stored and cracked years from now? In this episode of Tech Talks Daily, I sat down with Nate Jenniges, Senior Vice President and General Manager at BlackBerry, to talk about why the conversation around quantum computing is moving from academic curiosity to operational reality.  For many leaders, quantum threats still feel distant, something for researchers and cryptographers to worry about. But as Nate explained, governments and adversaries are already capturing encrypted data today with the expectation that it can be decrypted later when quantum capabilities mature. This idea of "harvest now, decrypt later" attacks completely changes the timeline for security planning. If sensitive information needs to remain confidential for five, ten, or even twenty years, the exposure may already have started. That means the challenge is no longer theoretical. It is becoming a strategic issue that boards, CISOs, and government leaders must begin addressing right now. One of the most interesting parts of our conversation focused on something many people rarely think about. Metadata. While encryption protects the content of a message or phone call, the surrounding patterns often reveal just as much. Who spoke to whom, how often, from where, and at what time can tell a surprisingly detailed story. With modern analytics and AI tools, these patterns can expose command structures, business relationships, or crisis response activity even if the message itself remains encrypted. Nate explained why this is becoming a frontline issue in the emerging post-quantum era. As organizations integrate AI into communication platforms, new forms of metadata are emerging from model interactions, system queries, and inference activities. That means protecting communications requires a broader view than simply upgrading encryption algorithms. We also explored how governments and highly regulated sectors are preparing for this shift. BlackBerry today operates in a very different space than many people remember, focusing on identity-verified, mission-critical communications used by governments and institutions that cannot afford uncertainty. These systems are designed to operate during the moments that matter most, whether that involves cyber incident response, national security coordination, or emergency response to climate-related events. Another theme that stood out was the leadership challenge behind quantum readiness. Nate believes organizations should avoid treating quantum as a separate security initiative. Instead, it should be integrated into the technology refresh cycles that companies already manage, including hardware updates, software upgrades, and certificate renewals. The organizations that begin asking the right questions today will avoid scrambling later when regulatory expectations tighten and deadlines arrive. By the end of our conversation, one message became very clear. The first real defense in the post-quantum era may not come from stronger encryption alone. It may come from understanding and controlling the communication patterns and metadata that surround every digital interaction. As quantum computing research accelerates and governments begin setting deadlines for post-quantum security readiness, the question becomes increasingly hard to ignore. Are organizations truly prepared for the communications challenges that the next decade may bring?

Why are employees still drowning in administrative work despite years of digital transformation, new software platforms, and constant promises that technology will make work easier? In this episode of Tech Talks Daily, I explore that question with Jason Spry from Ricoh Europe. What begins as a discussion about a new Ricoh research report quickly turns into a much broader conversation about how modern workplaces actually operate day to day. The findings are striking. Employees across Europe are losing an average of 15 hours every week to routine administrative tasks. That is time spent searching for documents, reentering data across systems, preparing reports manually, and navigating layers of disconnected tools. For many organizations, this creates a strange contradiction. Leadership teams often believe that new platforms and software will simplify workflows, yet many employees feel the opposite. The tools designed to make work easier sometimes create additional layers of complexity. Jason shares his perspective from nearly three decades in document processing and outsourcing, explaining how years of digital initiatives have often resulted in systems stacked on top of one another rather than genuinely simplified workflows. The result is a fragmented experience where finding the latest version of a document or locating the right information for a meeting can consume far more time than it should. We also discuss the hidden risks behind these inefficiencies. When documents are scattered across systems or poorly managed, the consequences go beyond frustration. Ricoh's research shows that many organizations have experienced compliance breaches or near misses because important documents were missing, misfiled, or simply impossible to locate at the right moment. Jason explains why governance, visibility, and consistent document management are becoming increasingly important in a world where decisions rely on accurate information. Another theme that runs throughout this conversation is the idea of marginal gains. Small inefficiencies like searching for files, reentering data, or preparing documents for meetings might seem trivial in isolation. Yet when they happen hundreds of times across a workforce, they add up to a serious productivity drain. Jason compares it to the concept of improving performance by one percent at a time. Removing even a few of these micro frustrations can transform how people experience their workday. Naturally, we also talk about automation and AI. But Jason offers a refreshing perspective here as well. Rather than starting with the technology, he argues that organizations should begin by identifying the real pain points employees face. That often means speaking directly with the people doing the work and asking what frustrates them most. Once those challenges are clear, automation and intelligent document management tools can start delivering results quickly, sometimes within weeks rather than years. By the end of the conversation, it becomes clear that solving the admin overload problem does not always require massive transformation projects. Often the answer lies in simplifying processes, connecting systems more intelligently, and removing the small friction points that slow everyone down. So I am curious. How much time do you think your organization loses to administrative work each week, and what simple changes could give employees that time back?

How do you build trust in a business environment where security reviews, compliance demands, and vendor risk checks can slow everything down just when companies are trying to move faster? In this episode, I sit down with Adam Markowitz, CEO and co-founder of Drata, to talk about why trust has become one of the most important business conversations in tech. Adam brings a fascinating perspective to the table. Before building Drata, he worked on NASA's space shuttle program, and today he leads a company that has grown rapidly by helping organizations rethink compliance, governance, risk, and assurance through automation and AI. What stood out to me in this conversation was how clearly he framed the real issue. Compliance may have been where many companies started, but trust is the bigger story. In a world shaped by cloud services, third party vendors, and constant security scrutiny, old point in time audits and reactive processes are starting to look painfully outdated. We also talked about Drata's acquisition of SafeBase and what that says about the direction of the market. Adam explained how security and GRC teams have too often been treated as back office functions, expected to stay quiet and keep the company out of trouble. But he sees things very differently. He argues that these teams can actively help close deals, accelerate revenue, and remove friction from the buying process. That shift matters because trust now plays a direct role in business growth. If customers can quickly get answers to security questions and understand how a company manages risk, sales cycles move faster and security teams stop being bottlenecks at the final stage of a deal. Another part of the conversation that really stayed with me was Adam's view on AI. He sees it as both a tailwind and a test. AI is helping automate highly manual GRC workflows, improve continuous compliance monitoring, and support newer frameworks tied to AI risk itself. At the same time, he is realistic about the pressure this puts on businesses. AI may introduce fresh concerns, but it also shines a harsher light on issues that have been around for years, things like access creep, weak controls, and data integrity problems. That honesty gave this discussion a lot of weight because it moved beyond hype and focused on what companies actually need to do. We also touched on Drata's momentum as a business, from opening a new San Francisco headquarters to expanding globally and moving further into the enterprise market. But even there, Adam kept coming back to culture, discipline, and a deep understanding of the customer problem. For me, that was the thread running through the whole episode. Trust is not a side issue. It is part of how modern companies grow, compete, and prove they can be relied on. If your business still sees compliance as a checkbox exercise or a cost center, this conversation will give you plenty to think about. Where do you see the relationship between trust, security, and growth heading next, and what did this episode make you question about the way your own organization handles compliance? Share your thoughts with me.

What happens when the most frustrating part of customer service, waiting on hold, repeating yourself, and fighting your way through endless phone menus, finally starts to disappear? In this episode, I sit down with Neil Hammerton, CEO and co-founder of Natterbox, to talk about how AI is reshaping customer experience in ways that feel practical rather than theatrical. We begin with a conversation about the gap between what customers have tolerated for years and what they expect now. Whether it is a bank that still puts you through layers of outdated IVR menus or a service team that answers straight away and solves the issue, those experiences stay with us. Neil makes the case that voice is far from dead. In fact, he believes voice is becoming one of the most exciting places to apply AI, especially when businesses want faster, more human interactions at scale. What I found especially interesting was Neil's view that AI should be treated like a new employee. That means training matters. Tone matters. Context matters. If businesses want AI assistants and agents to succeed, they have to teach them how the organization works, how conversations should sound, and when a human needs to step in. We talk about the difference between using AI for simple triage and using it to complete tasks end to end, from handling password resets to helping callers outside office hours or during spikes in demand. Neil also shares why the smartest path is rarely a giant leap. It is usually a series of smaller, lower-risk steps that build confidence and real results over time. We also get into one of the biggest concerns hanging over every AI conversation right now, whether these tools are replacing people or helping them do better work. Neil's answer is refreshingly balanced. In many cases, AI is taking care of the repetitive jobs that frustrate staff and slow down service, while freeing human agents to handle the conversations where empathy, judgment, and experience still matter most. That shift can improve customer experience while also making work more rewarding for the people on the front line. There is also a strong message here for business leaders who are still stuck in pilot mode, testing AI without ever quite moving forward. Neil explains why smart pilots need clear goals, good training data, and realistic expectations. He also shares how Natterbox is using AI internally, including producing board packs in a fraction of the time, while still keeping people involved to check, challenge, and refine the output. This episode is a thoughtful look at where customer experience is heading next, and why the future probably belongs to businesses that know when to let AI lead, when to keep humans in the loop, and how to blend both into something customers actually value. What are your thoughts on the balance between AI efficiency and human connection in customer service, and where do you think businesses are still getting it wrong?

How do you turn trillions of user interactions into meaningful decisions without drowning in data? In this episode of Tech Talks Daily, I sit down with Todd Olson, co-founder and CEO of Pendo, to talk about the future of product-led organizations and why AI is reshaping how software companies grow, build, and compete. Pendo tracks trillions of product usage events to help organizations understand how customers actually interact with their software. That level of data sounds powerful, but it also raises a challenge many teams face today. How do you turn massive data sets into clear signals that teams can act on without falling into analysis paralysis? Todd explains how Pendo approaches this problem by organizing product data around real user journeys, feature adoption, and areas where people drop off. Instead of leaving teams buried in dashboards, the goal is to surface insights that matter. Increasingly, AI is helping by acting as a kind of embedded analyst that highlights the patterns product teams should focus on. Our conversation also revisits the idea behind Todd's book, The Product-Led Organization. When it was published around the time of the pandemic, it argued that great products should do much of the heavy lifting traditionally done by sales or support teams. Looking back now, Todd believes the core idea remains intact. AI simply accelerates the model by allowing companies to experiment faster and scale product-driven experiences with far fewer people. But that shift is also creating tension in the software industry. We talk about the so-called reckoning in SaaS economics and the growing debate around whether AI will make traditional software companies obsolete. Todd offers a more measured perspective. While AI allows anyone to prototype software quickly, the companies that survive will still be the ones solving difficult problems, navigating compliance requirements, and building products that customers trust. Another theme we explore is geography and innovation. Pendo is headquartered in Raleigh, North Carolina, far from the usual coastal tech hubs. Todd shares how building outside Silicon Valley has shaped the company's culture, talent strategy, and mindset. There are advantages to being close to the center of the AI boom, but there is also value in building away from the echo chamber. We also spend time unpacking the rise of AI-assisted development and the trend many people call "vibe coding." Todd believes AI will dramatically reshape product teams, but he also pushes back against the idea that humans will disappear from the development process. Engineers will still need to review code, teach AI systems best practices, and ensure security and reliability. One of the most interesting moments in our conversation comes near the end when Todd shares a belief that originality will become one of the most valuable assets in the age of AI. As automated content and automated code become easier to generate, he believes people will increasingly value craft, taste, and original thinking. So in a world where AI can generate almost anything with a prompt, the real question becomes far more human. What problems are actually worth solving? If you care about the future of software, product strategy, and how AI is reshaping the economics of building companies, this is a conversation that offers plenty to think about. And after listening, I would love to hear your perspective. As AI becomes embedded in every product and workflow, do you believe originality and craft will become the true differentiators in the software industry?

Have you ever contacted customer support with a simple request, only to find yourself trapped in a loop of scripted chatbot responses that never actually solve the problem? It's an experience many of us know all too well.  AI has made customer service more conversational over the last few years, yet there is still a gap between answering a question and actually resolving an issue. That gap is exactly where today's conversation begins. In this episode of Tech Talks Daily, I spoke with Mike Szilagyi, SVP and General Manager of Product Management at Genesys Cloud, about a new chapter in AI-powered customer experience. Genesys has announced what it describes as the industry's first agentic virtual agent built on Large Action Models, or LAMs. While Large Language Models have dominated the conversation around AI for the past few years, they have largely focused on generating responses, retrieving knowledge, or answering questions. What they have struggled with is execution. Mike explained how Large Action Models take the next step. Rather than simply telling a customer how to solve a problem, these systems can plan and execute the steps needed to complete a task. Imagine contacting an airline after a sudden flight cancellation.  Instead of navigating multiple menus or repeating information to a human agent, an agentic virtual assistant could understand your situation, check alternative flights, apply airline policies, and complete the rebooking process across several systems. In other words, the AI moves from conversation to action. We also explored how Genesys approached the design of this technology with enterprise governance in mind. From explainable decision paths and audit logs to guardrails that ensure every automated action can be traced and understood, the goal is to make autonomous AI trustworthy inside complex organizations. Mike also shared insights into Genesys' partnership with Scaled Cognition and how integrating specialized models helps deliver reliable execution in real-world customer service environments. Perhaps most interesting was our discussion about the human role in this evolving contact center landscape. As automation begins to handle routine and multi-step workflows, human agents are free to focus on situations that require empathy, judgment, and expertise. That shift raises interesting questions about how organizations design customer experiences in the years ahead. So how will customers respond when virtual agents move beyond answering questions and begin resolving problems on their behalf? And once one brand delivers that experience, will it quickly become the expectation? Useful Links Connect with Mike Szilagyi Learn more about Genesys Genesys Agentic Virtual Agent Powered by LAMs for Enterprise CX Follow on LinkedIn

How do global companies make confident decisions when supply chains are constantly disrupted by tariffs, geopolitical tension, shifting consumer demand, and unpredictable global events? In this episode of Tech Talks Daily, I sat down with Dr. Ashwin Rao, EVP of AI and R&D at o9 Solutions, to talk about how artificial intelligence is changing the way organizations plan, forecast, and respond to uncertainty. Ashwin brings a fascinating mix of experience to the conversation. After earning a PhD in mathematics and computer science, he spent fifteen years on Wall Street working on derivatives trading strategies at Goldman Sachs and Morgan Stanley before moving into the world of enterprise technology. Today, he operates at the meeting point between business and academia as both a senior AI leader and an adjunct professor at Stanford University. Our conversation begins with Ashwin's unusual career path and how those early experiences in finance shaped the way he thinks about risk, decision making, and real world AI deployment. The journey from theoretical mathematics to trading floors and eventually into Silicon Valley offers an interesting lens on how analytical thinking can travel across industries and still remain highly relevant. We then move into the work happening at o9 Solutions, where AI is helping organizations make smarter decisions across supply chain planning, demand forecasting, and inventory management. In a world that Ashwin describes using the acronym VUCA, volatility, uncertainty, complexity, and ambiguity, businesses are under pressure to react faster and make better informed decisions. He explains how enterprise AI platforms can connect fragmented data across departments and create a more complete view of the business. One example he shares brings the concept down to earth. Even predicting how many bananas a grocery store should stock on any given day requires analyzing internal sales trends alongside external signals such as weather, social media trends, and economic conditions. Machine learning systems can now process those signals in real time and continuously update forecasts so businesses can respond quickly to changes. We also explore the rise of neuro- and symbolic AI, a concept Ashwin believes represents the next stage in enterprise decision-making. Rather than relying only on large language models, this approach blends the structured reasoning of symbolic systems with the pattern recognition of neural networks. The result, he suggests, feels less like a chatbot and more like having an expert coach embedded inside the decision-making process. Along the way, we also discuss why many organizations still struggle to embed AI successfully. Technology is only one piece of the puzzle. Ashwin believes the toughest obstacle is organizational change management, bringing teams together, connecting data across silos, and helping leaders guide their organizations through transformation. If you have ever wondered how AI moves beyond chatbots and into the systems that quietly power global supply chains, this conversation offers a thoughtful and practical perspective. So, how prepared is your organization to make decisions in a world defined by volatility and uncertainty, and could AI become the trusted partner that helps guide those choices? Useful Links Ashwin's blog Ashwin's LinkedIn o9 Solutions Website o9 LinkedIn  

What does it take to design a data center for a world where the technology inside it may change several times before the building even opens? In this episode of Tech Talks Daily, I sit down with Jackson Metcalf, Principal at Gensler, to talk about how AI is forcing a complete rethink of data center design. Jackson has spent nearly two decades working on critical facilities, and in our conversation he explains how the shift from traditional cloud workloads to dense AI environments is changing everything from building form and cooling strategy to long-term infrastructure planning. What struck me most in this conversation is the sheer mismatch in timescales. Data centers can take two and a half to three years to design and build, while chip and GPU roadmaps are evolving in cycles of months. Jackson explains why that means designing for a fixed end state no longer makes sense. Instead, the future may belong to facilities built with flexibility at their core, spaces that can be reconfigured, upgraded, and even conceptually rebuilt over time rather than treated as static assets. We also talk about what hyper-flexibility actually means in practice. This is not just a buzzword. It is about designing buildings with enough structural and engineering headroom to support very different cooling and power models over their lifespan. As AI workloads push cabinet densities to levels that would have sounded impossible only a few years ago, the need for plug-and-play mechanical and electrical infrastructure becomes far more than a design preference. It becomes essential. Another fascinating part of the conversation centers on sustainability. Jackson shares why durable, well-built structures can create long-term environmental value, even in an industry often criticized for its energy demands. We discuss embodied carbon, adaptive reuse, and why a high-quality building may have a much better second life than something built purely for short-term speed. That leads into a wider conversation about repositioning underused real estate, from former industrial facilities to vacant office buildings, as potential digital infrastructure. We also get into the growing energy challenge behind AI. With demand for power rising fast, and the US grid under increasing pressure, many operators are now weighing options such as on-site natural gas generation while waiting for cleaner long-term alternatives to mature. Jackson offers a thoughtful perspective on the tension between urgent infrastructure needs and environmental responsibility, as well as the uncertainty surrounding future energy roadmaps. Looking further ahead, I ask Jackson what will define a successful data center campus in the years to come. Will it be raw megawatts, adaptability, carbon intensity, location strategy, or something else entirely? His answer opens up a much bigger conversation about whether these buildings can become more connected to the communities around them, and what role they may play in a future where digital infrastructure is no longer hidden in the background, but central to how society functions. So if AI is pushing data center design to extremes, how do we build facilities that are ready for what comes next without becoming obsolete almost as soon as they open? And what does sustainable, adaptable digital infrastructure really look like in practice?

How close are we to the moment when quantum computing moves from scientific curiosity to real-world infrastructure? In today's episode of Tech Talks Daily, I speak with Christian Weedbrook, Founder and CEO of Xanadu, a company pushing the boundaries of what quantum computers might soon achieve. Xanadu has taken an unconventional route in the race to build practical quantum systems. Instead of relying on electronic approaches used by many others in the field, the company builds quantum computers using photonics, effectively computing with particles of light. Christian explains why this matters and how working with photons could unlock advantages in energy efficiency, scalability, and networking as quantum machines grow into large data center–scale systems. The conversation also arrives at a fascinating moment for the company. Xanadu has announced plans to go public through a SPAC deal that values the company at around $3.1 billion. Christian shares what that milestone means, not only for Xanadu but for the broader quantum ecosystem. According to him, the excitement surrounding quantum computing is no longer limited to research labs. Governments, enterprise partners, and investors are increasingly paying attention as the technology edges closer to commercial relevance. One of the most engaging parts of our conversation is Christian's own journey into the world of quantum physics. Before earning a PhD in photonic quantum computing, he began as a film student who admits he once dreamed of becoming a filmmaker. That winding path eventually led him into physics and entrepreneurship, where he founded Xanadu in 2016 with a mission to make quantum computers useful and accessible to everyone. We also discuss PennyLane, the open-source quantum programming framework developed by Xanadu that has quietly become one of the most widely used tools in the quantum developer community. Now taught in universities across more than 30 countries, PennyLane plays an important role in building the next generation of quantum talent. Christian also shares a realistic timeline for where the industry stands today. Quantum computers already exist, but they remain smaller than what is needed for commercial breakthroughs. Xanadu's roadmap points toward large-scale quantum data centers by the end of the decade, systems capable of tackling problems in drug discovery, materials science, logistics, and finance that traditional computers struggle to simulate. For enterprise leaders listening today, the message is clear. The quantum future is closer than many people assume, and organizations that begin exploring use cases now will be far better prepared when these systems mature. So how should businesses prepare for a computing paradigm based on the mathematics of quantum physics rather than traditional software logic? And what lessons can founders learn from a journey that began with filmmaking ambitions and led to building one of the most ambitious quantum companies in the world? Let's find out together.