Tech Talks Daily

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?    

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.      

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?  

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?