Tech Talks Daily

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...

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 BaumanLearn more about TruGreenQualtrics 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 AhujaAgentic 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 withPaul ZolfaghariCheck out theSaviynt WebsiteFollow onFacebook,LinkedIn, andX