Who Is Winning The AI Race? The Clarivate AI50 Report Has The Receipts

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.