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*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1" data-turn-id= "9168b9fb-9cc7-4a32-9cf3-0f12c0141fb6" data-testid= "conversation-turn-5" data-scroll-anchor="false" data-turn="user"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "request-69932a54-73c0-8395-979c-6bc9e24ea9ee-0" data-testid= "conversation-turn-6" data-scroll-anchor="true" data-turn= "assistant"> After stepping off stage from moderating a panel, a Senior Frontend Developer from Capgemini waited to say hello. She asked for a quick photo, and within minutes, we were deep in conversation about hackathons, women in tech, mentoring, and the pride she felt watching Egypt host a platform of this scale. Her name is Alaa Ali Kortoma, and what began as a quick introduction turned into her very first podcast appearance. In today's episode, you will hear directly from someone on the ground in Cairo about what AI Everywhere means to her, to Egypt, and to a generation of more than 750,000 graduates entering the workforce each year. We talk about bridging the gap between academia and industry, shrinking the distance between startups and investors, and why she believes AI represents opportunity rather than replacement. If AI really is everywhere, it should look like a possibility. It should look like inclusion. It should look like young women mentoring at hackathons. It should look like national strategies focused on responsible adoption and skills development. So let me beam your ears to Cairo and introduce you to Alaa Ali Kortoma. And after spending three days at AI Everything MEA, what does AI Everywhere mean to me? It is not hype. It is not a headline. It is policymakers embedding AI into public services. It is engineers building Arabic language models tailored to local needs. It is healthcare systems using AI to detect disease earlier. It is investors listening to founders. It is young professionals investing in themselves. One phrase from this conversation will stay with me long after the microphones were turned off. Proud and full of possibility. Over the last decade, I have seen technology stories unfold across continents, but Cairo reminded me why I started this podcast in the first place. Technology becomes powerful when it connects people. When it builds confidence. When it proves that innovation is not reserved for a select few regions. AI is often framed as a Silicon Valley or East Asia story. What I witnessed in Egypt suggests something broader is taking shape. Capital is flowing differently. Partnerships are forming across Africa and the Middle East. Talent is visible. Voices are confident. So if AI can thrive beside the Nile, if it can empower graduates in Cairo to see opportunity rather than threat, then perhaps AI really is everywhere. The final question is this. What does AI Everywhere look like where you are, and what role are you playing in shaping it? Wherever you are listening from, I would love to hear your story too.

In this episode, I'm joined by Bill Briggs, CTO at Deloitte, for a straight-talking conversation about why so many organizations get stuck in what he calls "pilot purgatory," and what it takes to move from impressive demos to measurable outcomes. Bill has spent nearly three decades helping leaders translate the "what" of new technology into the "so what," and the "now what," and he brings that lens to everything from GenAI to agentic systems, core modernization, and the messy reality of technical debt. We start with a moment of real-world context, Bill calling in from San Francisco with Super Bowl week chaos nearby, and the funny way Waymo selfies quickly turn into "oh, another Waymo" once the novelty fades. That same pattern shows up in enterprise tech, where shiny tools can grab attention fast, while the harder work, data foundations, APIs, governance, and process redesign, gets pushed to the side. Bill breaks down why layering AI on top of old workflows can backfire, including the idea that you can "weaponize inefficiency" and end up paying for it twice, once in complexity and again in compute costs. From there, we get into his "innovation flywheel" view, where progress depends on getting AI into the hands of everyday teams, building trust beyond the C-suite, and embedding guardrails into engineering pipelines so safety and discipline do not rely on wishful thinking. We also dig into technical debt with a framing I suspect will stick with a lot of listeners. Bill explains three types, malfeasance, misfeasance, and non-feasance, and why most debt comes from understandable trade-offs, not bad intent. It leads into a practical discussion on how to prioritize modernization without falling for simplistic "cloud good, mainframe bad" narratives. We finish with a myth-busting riff on infrastructure choices, a quick look at what he sees coming next in physical AI and robotics, and a human ending that somehow lands on Beach Boys songs and pinball machines, because tech leadership is still leadership, and leaders are still people. So after hearing Bill's take, where do you think your organization is right now, measurable outcomes, success theater, or somewhere in between, and what would you change first, and please share your thoughts?   Useful Links Connect With Bill Briggs Deloitte Tech Trends 2026 report Deloitte The State of AI in the Enterprise report

Perform 2026 felt like a turning point for Dynatrace, and when Steve Tack joined me for his fourth appearance on the show, it was clear this was not business as usual.  We began with a little Perform nostalgia, from Dave Anderson's unforgettable "Full Stack Baby" moment to the debut of AI Rick on the keynote stage. But the humor quickly gave way to substance. Because beneath the spectacle, Dynatrace introduced something that signals a broader shift in observability: Dynatrace Intelligence. Steve was candid about the problem they set out to solve. Too much focus on ingesting data. Too much time spent stitching tools together. Too many dashboards. Too many alerts. The real opportunity, he argued, is turning telemetry into trusted, automated action. And that means blending deterministic AI with agentic systems in a way enterprises can actually trust. We unpacked what that looks like in practice. From United Airlines using a digital cockpit to improve operational performance, to TELUS and Vodafone demonstrating measurable ROI on stage, the emphasis at Perform was firmly on production outcomes rather than pilot projects. As Steve put it, the industry has spent long enough in "pilot purgatory." The next phase demands real-world deployment and real return. A big part of that confidence comes from the foundations Dynatrace has laid with Grail and Smartscape. By combining unified telemetry in its data lakehouse with real-time topology mapping and causal AI, Dynatrace is positioning itself as the engine behind explainable, trustworthy automation. When hyperscaler agents from AWS, Azure, or Google Cloud call Dynatrace Intelligence, they are expected to receive answers grounded in causal context rather than probabilistic guesswork. We also explored what this means for developers, who often carry the burden of alert fatigue and fragmented tooling. New integrations into VS Code, Slack, Atlassian, and ServiceNow aim to bring observability directly into the developer workflow. The goal is simple in theory and complex in execution: keep engineers in their flow, reduce toil, and amplify human decision-making rather than replace it. Of course, autonomy raises questions about risk. Steve acknowledged that for now, humans remain firmly in the loop, with most agentic interactions still requiring checkpoints. But as trust grows, so will the willingness to let systems self-optimize, self-heal, and remediate issues automatically. We closed by zooming out. In a market saturated with AI claims, Steve encouraged listeners to bet on change rather than cling to the status quo. There will be hype. There will be agent washing. But there is also real value emerging for those prepared to experiment, learn, and scale responsibly. If you want to understand where AI observability is heading, and how deterministic and agentic intelligence can coexist inside enterprise operations, this episode offers a grounded, practical perspective straight from the Perform show floor.

What happens when the noise around AI starts to drown out the actual business value it is meant to deliver? In this episode of Tech Talks Daily, I sat down with Adam Field, Chief AI and Product Officer at Tungsten Automation, fresh from the conversations unfolding at Davos. While headlines continue to celebrate agentic AI and sweeping automation claims, Adam offered a grounded perspective shaped by decades of experience turning AI pilots into measurable, ROI-driven deployments. His view is simple. The hype cycle may be accelerating, but many organizations still struggle with the fundamentals. Adam described a common boardroom dynamic. "What do we want? AI. What do we want it to do? We're not sure." That pressure to move fast often collides with a deeper reality. Software has shifted from deterministic to probabilistic. Leaders who grew up expecting the same inputs to always produce the same outputs now face systems that behave differently by design. Measuring value in that environment requires a different mindset. One of the most compelling ideas in our conversation was Adam's concept of "boring AI." While splashy announcements about replacing hundreds of employees grab attention, he argues that real returns often come from quieter use cases. At Tungsten Automation, that means intelligent document processing, extracting trusted, AI-ready data from the 80 percent of enterprise information that is unstructured. Contracts, invoices, transcripts, compliance paperwork. The work may not trend on social media, but it saves time, improves accuracy, and fits directly into daily workflows. We also explored accountability. AI can compress output, but it concentrates responsibility. When generative tools make architectural or compliance decisions, the liability does not shift to the model. Organizations remain accountable for privacy, ethics, and customer trust. Adam shared his own experience rebuilding a legacy application in days using AI code generation, only to discover licensing and compliance nuances that required human judgment. The lesson was clear. AI amplifies capability, yet human oversight remains essential. For leaders searching for signals that an AI strategy will actually deliver long-term returns, Adam pointed to two patterns from the small percentage of projects that succeed. First, integration into daily workflows drives adoption. Second, partnering with trusted vendors often reduces risk compared to attempting everything in-house. In a world flooded with open-source experiments and "X is dead" headlines, discipline and focus still matter. Tungsten Automation has spent four decades evolving alongside automation technologies, previously known as Kofax. Today, the company applies large language models and agentic workflows to transform unstructured data into decision-ready insights across finance, logistics, banking, and insurance. It is a reminder that the future of AI may be less about replacing people and more about removing friction so humans can do the work they were actually hired to do. So as AI investment continues to grow and pressure for returns intensifies, the question becomes harder to ignore. Are we chasing the headlines, or are we building systems that quietly deliver value where it counts? Useful Links Connect with Adam Field Learn more about Tungsten Automation Upcoming Events

How do you build a $30 million ARR business with just three people and a fleet of AI agents doing the heavy lifting? In this episode of Tech Talks Daily, I connected with Amos Joseph, CEO of Swan AI. From the moment we joked about AI notetakers silently observing our conversation, it was clear this discussion would go beyond surface-level automation talk. Amos is attempting something bold. He is building what he calls an autonomous business, one designed to scale with intelligence rather than headcount. Amos has already built and exited two B2B startups using the traditional growth-at-all-costs model. Raise early, hire fast, expand the vision, chase valuation. This time, he is rewriting that script entirely. Swan AI is built around ARR per employee, human-AI collaboration, and what he describes as scaling employees rather than scaling the org chart. With more than 200 customers and only three founders, Swan is already testing whether AI agents can run real go-to-market operations autonomously. We explored why over 90 percent of AI implementations fail and why grassroots experimentation consistently outperforms executive mandates. Amos argues that companies looking outward for AI solutions before understanding their internal bottlenecks are simply scaling chaos. The organizations that succeed start with process clarity, define what humans should do versus what should be automated, and then allow AI to execute within that structure. It is a powerful reminder that becoming AI-native has less to do with tools and more to do with operational self-awareness. We also unpacked the difference between automation and agentic AI. Traditional automation follows deterministic steps coded in advance. Agentic AI shifts decision-making power to the model itself. The AI decides what to do next, introducing statistical reasoning rather than predefined logic. That shift in agency changes everything about how workflows operate and how leaders think about control. Perhaps most fascinating is how Swan generates pipeline entirely through LinkedIn. No paid ads. No outbound. Amos has built an AI-driven engine that creates content, monitors engagement, qualifies prospects, and nurtures relationships at scale. It is an experiment in trust-based distribution powered by agents, not marketing budgets. This conversation reframes what growth can look like in an AI-native world. If scaling no longer equals hiring, and if every employee becomes a manager of AI agents, what does leadership look like next? How do founders build organizations that amplify human zones of genius rather than bury them under coordination overhead? If you are questioning long-held assumptions about team size, growth, and AI adoption, this episode will give you plenty to think about.

Is Bitcoin still just a digital store of value, or is it quietly evolving into the financial engine of a new on-chain economy? In this episode of Tech Talks Daily, I sat down with Callan Sarre, Co-Founder of Threshold Labs, to explore what happens when the world's most recognized crypto asset stops sitting idle and starts becoming programmable capital. We recorded against the backdrop of a sharp market correction that wiped out value across crypto and traditional assets alike, making for a timely and honest conversation about volatility, maturity, and why Bitcoin's next chapter may be defined by utility rather than price speculation.  Callan explains how the rise of ETFs and institutional flows is reshaping ownership, while decentralized infrastructure is working to ensure users can still access the asset's underlying power. At the heart of our discussion is tBTC, a trust-minimized bridge that moves native Bitcoin into DeFi without handing control to centralized custodians. Callan breaks down how Threshold's decentralized custody model works in practice and why removing single points of failure matters in a post-FTX world. We also explore the behavioral barriers that have kept long-term holders from putting their BTC to work, the real risks behind Bitcoin yield strategies, and the infrastructure required to make these tools accessible to a broader audience through familiar Web2-style experiences. The conversation also takes a global turn as we look at why Asia is accelerating Bitcoin innovation, how regulation is driving institutional adoption in Western markets, and what the shift from DAO-led governance to a lab execution model reveals about the realities of building at scale.  Looking ahead five years, Callan paints a picture of an integrated on-chain financial system where Bitcoin can be borrowed against, deployed, and settled instantly across shared liquidity rails, while still preserving the principles that made it attractive in the first place. So if Bitcoin becomes productive capital and the majority of financial activity moves on-chain, what does that mean for traditional finance, for long-term holders, and for the next wave of builders? And are we ready for a world where the most secure monetary asset also becomes the most composable?

What actually happens when AI stops being a cloud-only experiment and starts running on desks, in labs, and inside real teams trying to ship real work? In this episode, I sit down with Logan Lawler, Senior Director at Dell Technologies, to unpack how AI workloads are really being built and supported on the ground today. Logan leads Dell's Precision and Pro Max AI Solutions business and hosts Dell's own Reshaping Workflows podcast, giving him a rare vantage point into how engineers, developers, creatives, and data teams are actually working, not how marketing slides suggest they should be. We start by cutting through the noise around AI PCs. At every conference stage, Logan breaks down what genuinely matters when choosing hardware for AI work. CPUs, GPUs, NPUs, memory, and software stacks all play different roles, and misunderstanding those roles often leads teams to overspend or underspec. Logan explains why all AI workstations qualify as AI PCs, but not all AI PCs are suitable for serious AI work, and why GPUs remain central for anyone doing real model development, fine-tuning, or inference at scale. From there, the conversation shifts to a broader architectural rethink. As AI workloads grow heavier and data sensitivity increases, many organizations are reconsidering where compute should live. Logan shares how GPU-powered Dell workstations, storage-rich environments, and hybrid cloud setups are giving teams more control over performance, cost, and data. We explore why local compute is becoming attractive again, how modern GPUs now rival small server setups, and why hybrid workflows, local for development and cloud for deployment, are becoming the default rather than the exception. One of the most compelling parts of the discussion comes when Logan connects hardware choices back to business reality. Drawing on real-world examples, he explains how teams use local AI environments to move faster, reduce cloud costs, and avoid getting locked into architectures that are hard to unwind later. This is not about abandoning the cloud, but about being intentional from the start, mainly as AI usage spreads beyond developers into marketing, operations, and everyday business roles. We also step back to reflect on a deeper challenge. As AI becomes easier to use, what happens to critical thinking, curiosity, and learning? Logan shares a candid perspective, shaped by his experiences as a parent, technologist, and podcast host, raising questions about how tools should support rather than replace thinking. If you are trying to make sense of AI PCs, local versus cloud compute, or how teams are really reshaping workflows with AI hardware today, this conversation offers grounded insight from someone living at the center of it. Are we designing systems that genuinely empower people to think better and build faster, or are we sleepwalking into decisions we will regret later? How do you want your own AI workflow to evolve? Useful Links TLDR AI newsletter and the Neurons. The Reshaping Workflows podcast Connect with Logan Lawler Follow Dell Technologies on LinkedIn

What does it really take to move AI from experimentation into something enterprises can trust, scale, and rely on every day? In this episode of Tech Talks Daily, I'm joined by Rob Lay, CTO and Solutions Engineering Director for Cisco UK and Ireland, recorded in the run-up to Cisco Live EMEA in Amsterdam. As agentic AI dominates conference agendas on both sides of the Atlantic, this conversation steps away from model hype. It focuses on the less glamorous, but far more decisive layer underneath it all: infrastructure. Rob explains why the biggest constraint on scaling AI agents in production is no longer imagination or ambition, but the readiness of the environments those agents run on. We talk about how legacy technical debt, latency, fragmented networks, and disconnected security tools can quietly undermine AI investments long before leaders see any return. As organizations move out of pilot mode and into real execution, those cracks become impossible to ignore. A big part of the discussion centers on why AI changes the relationship between network, compute, and security teams. Traditional silos struggle to keep up as autonomous systems make decisions at machine speed. Rob shares how Cisco is approaching this shift through tighter integration across the stack, with security designed directly into the network rather than bolted on later. When AI agents act independently, routing everything through centralized chokepoints does not hold up. We also explore how operational complexity is evolving. Tool sprawl is already overwhelming many IT leaders, and agent sprawl is clearly coming next. Rob outlines Cisco's platform strategy, including how agent-driven operations, human oversight, and context-aware automation are shaping a new approach to day-to-day resilience. This leads into a wider conversation about digital resilience as a business issue, where visibility, assurance, and learning from incidents matter more than static continuity plans that only get tested once a year. For European leaders in particular, data sovereignty and control remain at the forefront. Rob explains how Cisco is responding with flexible deployment models, local data residency options, and air-gapped environments that support AI innovation without forcing customers into a single rigid operating model. We close by looking at where enterprises are actually seeing value today, where expectations are still running ahead of reality, and what leaders attending Cisco Live should really be listening to as announcements roll in. If you are responsible for infrastructure, security, or technology strategy in an AI-driven organization, this conversation offers a grounded view of what needs to be ready before agents can truly deliver on their promise. As AI-powered systems start to move faster than most roadmaps anticipated, are you confident the foundations underneath them are ready to keep up, and what would you change if you were starting that journey today? Useful Links Connect with Rob Lay Cisco Live Follow Cisco on LinkedIn

What does it really take to move enterprise AI from impressive demos to decisions that show up in quarterly results? One year into his role as Global Managing Partner at IBM Consulting, Neil Dhar sits at the intersection of strategy, capital allocation, and technology execution. Leading the firm's Americas business and a team of close to 100,000 consultants, he has a front-row view into how large organizations are reassessing their AI investments. From global healthcare leaders like Medtronic to luxury retail brands such as Neiman Marcus, the conversation has shifted. Early proofs of concept helped executives understand what was possible. Now the focus is firmly on proof of value and on whether AI can drive growth, competitiveness, and measurable return. In this episode, I speak with Neil Dhar about what has changed in the boardroom over the past year and why ROI has become the central question. Drawing on more than three decades in finance and private equity, including senior leadership roles at PwC, Neil explains why AI is increasingly being treated as a capital allocation decision rather than a technology experiment. Every dollar invested has to earn its place, whether through productivity gains, operational improvement, or new revenue opportunities. Vanity projects no longer survive scrutiny, especially when boards and investors expect results on a much shorter timeline. We also explore how IBM is applying these same principles internally. Neil shares how the company has identified hundreds of workflows across the business, prioritized those with the strongest economic impact, and used AI and automation to drive large-scale productivity gains. The result is a potential $4.5 billion in annual run rate savings by 2025, with those gains being reinvested into innovation, people, and future growth. It is a candid look at what happens when AI strategy, leadership accountability, and disciplined execution come together inside a global organization. If you are a business leader trying to separate real value from hype, or someone wrestling with how to justify AI spend beyond experimentation, this conversation offers a grounded perspective on what enterprise AI looks like when it is treated as a business decision rather than a technology trend. Are you ready to rethink how AI earns its place inside your organization, and what proof of value really means in 2026? Useful Links Connect With Neil Dhar IBM Institute for Business Value, "The Enterprise in 2030" study Learn More About IBM Consulting

How Do Marketplaces Turn AI Ambition Into Scalable, Trusted Enterprise Reality? That is the question I explore in this episode with Julie Teigland, Global Vice Chair for Alliances and Ecosystems at EY, someone who sits right at the intersection of enterprise demand, technology platforms, and the ecosystems that increasingly power modern AI adoption. As organizations race to deploy AI at scale, many are discovering that the real challenge is not a lack of tools, but the complexity of choosing, integrating, governing, and standing behind those decisions with confidence. Julie explains why marketplaces are becoming a powerful mechanism for reducing friction in this process, helping enterprises move beyond experimentation toward AI solutions that are trusted, scalable, and aligned with real business outcomes. We talk about how marketplaces can collapse complexity, curate choice, and bring much needed clarity to leaders who are overwhelmed by the sheer volume of AI options available today. Julie also shares how EY approaches this challenge through its "client zero" mindset, turning the lens inward and treating EY itself as the first marketplace customer. By doing so, EY stress tests governance, security, and integration at real enterprise scale, serving tens of thousands of clients, running hundreds of thousands of servers, and processing hundreds of millions of transactions every day. That internal experience shapes how EY helps clients navigate trust, accountability, and cross-vendor integration risks, particularly as AI becomes more embedded into workflows and decision-making. We also explore how strong alliances with cloud leaders like Microsoft and SAP are shaping how AI solutions are vetted, standardized, and deployed across industries, as well as how regulation, particularly in Europe, is influencing a shift toward responsibility by design. This conversation goes beyond technology to focus on orchestration, trust, and outcomes, and why marketplaces are evolving from simple app stores into something far more strategic for enterprise AI. If you are trying to understand how ecosystems, governance, and marketplaces can help turn AI from isolated projects into sustained business value, this episode offers a thoughtful and grounded perspective.  I would love to know what resonated with you most. How do you see marketplaces shaping the future of AI adoption inside your organization? Useful LInks Connect With Julie Teigland Learn More About EY

As someone who spends a lot of time covering AI announcements, product launches, and conference stages, it is easy to forget that most AI today is still built for desks, screens, and digital workflows. Yet the reality is that the vast majority of the global workforce operates in the physical world, on roads, construction sites, depots, and job sites where mistakes are measured in injuries, collisions, and lives lost. That gap between where AI innovation happens and where real risk exists is exactly why I wanted to sit down with Amish Babu, CTO at Motive. In this episode, I speak with Amish about what it truly means to build AI for the physical economy. We unpack why designing AI for vehicles, fleets, and safety-critical environments is fundamentally different from building AI for emails, documents, or dashboards. Amish explains why latency, trust, and reliability are non-negotiable when AI is embedded directly into vehicles, and why edge AI, multimodal sensing, and on-device compute are essential when milliseconds matter. This is a conversation about AI that has to work perfectly in messy, unpredictable, real-world conditions. We also explore how Motive approaches AI as a full system, combining hardware, software, and models into a single platform built specifically for life on the road. Amish shares how AI can help prevent collisions, support drivers in the moment, and create measurable safety and operational outcomes for fleets operating across transportation, construction, energy, and public sector environments. Along the way, we challenge common misconceptions around AI in vehicles, including the idea that it is about surveillance rather than protection, or that all AI systems are created equal when lives are on the line. If you are interested in how AI moves beyond productivity tools and into high-stakes environments where safety, accountability, and trust matter most, this episode offers a grounded and practical perspective from someone building these systems every day. I would love to hear your thoughts on this one. How do you see the role of AI evolving as it moves deeper into the physical world? Useful Links Connect with Amish Babu Learn More About Motive How Motive's AI works: Real-time edge intelligence, humans-in-the-loop, and continuous improvement.

*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "54141b02-3c0e-46be-b764-c57b8d9d7ccc" data-testid= "conversation-turn-28" data-scroll-anchor="true" data-turn= "assistant"> In this episode of Tech Talks Daily, I sat down with Jinsook Han, Chief Agentic AI Officer at Genpact, to unpack one of the most misunderstood shifts in enterprise AI right now. Many organizations feel confident about the value AI can deliver, yet only a small fraction are able to move beyond pilots and into autonomous operations that actually scale. Genpact's Autonomy By Design research puts hard data behind that gap, and Jinsook explains why optimism often races ahead of readiness. We explore why agentic AI changes the rules entirely. When AI systems begin to act, decide, and adapt on behalf of the business, familiar operating models start to strain. Jinsook makes a compelling case that agentic AI cannot be treated like another software rollout. It demands a rethink of data, governance, roles, and even how teams define work itself. The shift from tools to teammates alters expectations for people across the organization, from frontline operators to the C-suite, and exposes just how unprepared many companies still are. Governance is a major theme throughout the conversation, but not in the way most leaders expect. Rather than slowing progress, Jinsook argues that governance must become part of how work happens every day. She shares how Genpact approaches agent certification, maturity, and oversight, using vivid analogies to explain why quality and alignment matter more than simply deploying large numbers of agents. We also dig into why many governance models fail, especially when they rely on committees instead of lived understanding. Upskilling sits at the heart of this transformation. Jinsook walks through how Genpact is training more than 130,000 employees for an agentic future, starting with executives themselves. The focus is not on abstract learning, but on proving that today's work looks different from yesterday's. Observability, explainability, and responsible AI are woven into this approach, with command centers designed to monitor both agent performance and health, turning early signals into opportunities rather than panic. This conversation goes well beyond hype. It is about readiness, responsibility, and the reality of building autonomous systems that still depend on human judgment. As organizations rush toward agentic AI, are they truly prepared to change how decisions are made, how people work, and how accountability is defined, or are they still treating AI as a faster hammer rather than a new kind of teammate? Useful Links Connect with Jinsook Han Learn More about Genpact    

What happens when leaders are confident about AI, but the people expected to use it are not ready? In this episode of Tech Talks Daily, I sat down with Caroline Grant from Slalom Consulting to explore one of the most persistent tensions in enterprise AI adoption right now. Boards and executives are spending more, moving faster, and expecting returns sooner than ever, yet many organizations are struggling to translate that ambition into outcomes that scale.  Caroline brings fresh insight from Slalom's latest research into how leadership, culture, and workforce readiness are shaping what actually happens next. We unpack a clear shift in ownership for AI transformation, with CTOs and CDOs increasingly leading organizational redesign rather than HR. That change reflects how deeply AI now cuts across technology, operations, and business models, but it also introduces new risks.  Caroline explains why sidelining people teams can create blind spots around skills, incentives, and trust, especially as roles evolve and uncertainty grows inside the workforce. The result is what Slalom describes as a growing AI disconnect between executive optimism and day-to-day reality. Despite the noise around job losses, the data tells a more nuanced story. Many organizations are creating new AI-related roles at a pace, yet almost all are facing skills gaps that threaten progress. We talk about why reskilling at scale is now unavoidable, how unclear career paths fuel employee distrust, and why focusing only on technical capability misses the human side of adoption.  Caroline also challenges assumptions about skill priorities, warning that deprioritizing empathy, communication, and change leadership could undermine effective human-AI collaboration. We also dig into ROI expectations, with most UK executives now  expecting returns within two years. Caroline shares why that ambition is achievable, where it breaks down, and why so many organizations remain stuck in pilot mode. From governance and decision rights to culture and leadership behavior, this conversation goes beyond tools and platforms to examine what separates experimentation from fundamental transformation. As AI becomes a test of leadership as much as technology, how are you closing the gap between vision and execution within your organization, and are you building a workforce that can keep pace with change rather than resist it? Connect With Caroline Grant from Slalom Consulting The Great AI Disconnect: Slalom's Insights Survey Learn More About Slalom

Is the browser quietly becoming the most powerful and dangerous interface in modern work? In this episode of Tech Talks Daily, I sat down with Karim Toubba, CEO of LastPass, to unpack a shift that many people feel every day but rarely stop to question. The browser is no longer just a window to the internet. It has become the place where work happens, where SaaS lives, and increasingly, where humans and AI agents meet data, credentials, and decisions. From AI-native browsers to prompt-based navigation and headless agents acting on our behalf, the way we access information is changing fast, and so are the risks. Karim shares why this moment feels different from earlier waves like SaaS adoption or remote work. Today, more than ever, productivity, identity, and security collide inside the browser.  Shadow AI is spreading faster than most organizations can track, personal accounts are being used to access powerful AI tools, and sensitive data is being uploaded with little visibility or control. At the same time, attackers have noticed that the browser has become the soft underbelly of the enterprise, with a growing share of malware and breaches originating there. We also explore the rise of agentic AI and what happens when software, not people, starts logging into systems. When an agent books travel, pulls data, or completes workflows on a user's behalf, traditional authentication and access models start to break down. Karim explains why identity, visibility, and control must evolve together, and why secure browser extensions are emerging as a practical foundation for this next phase of computing. The conversation goes deep into what users do not see when AI browsers ask for access to email, calendars, and internal apps, and why convenience often masks long-term exposure. Throughout the discussion, Karim brings a grounded perspective shaped by decades in cybersecurity, from risk-based vulnerability management to enterprise threat intelligence. Rather than pushing fear, he focuses on realistic steps organizations and individuals can take, from understanding what data is being shared, to treating security teams as partners, to using tools that bring passwords, passkeys, and authentication into one trusted place as browsing evolves. As AI reshapes how we search, work, and make decisions, the question is no longer whether the browser matters. It is whether we are ready for it to act as the front door to both our productivity and our risk, so are you securing your browser for the future you are already using today? Connect with Karim Toubba LastPass Threat Intelligence, Mitigation, and Escalation (TIME) team page Phish Bowl Podcast    

What really happens when AI helps teams write code faster, but everything else in the delivery process starts to slow down? In this episode of Tech Talks Daily, I'm joined once again by returning guest and friend of the show, Martin Reynolds, Field CTO at Harness. It has been two years since we last spoke, and a lot has changed since then. Martin has relocated from London to North Carolina, gaining back hours of his working week. Still, the bigger shift has been in how AI is reshaping software delivery inside modern enterprises. Our conversation centers on what Martin calls the AI velocity paradox. Development teams are producing more code at speed, often thanks to AI coding agents, yet testing, security, governance, and release processes are struggling to keep up. The result is a growing gap between how fast software is written and how safely it can be delivered.  Martin shares research showing how this imbalance is already leading to production incidents, hidden vulnerabilities, and mounting technical debt. We also dig into why this AI-driven transition feels different from previous waves, such as cloud, mobile, or DevOps. Many of the same concerns around security, trust, and control still exist, but this time, everything is happening far faster. Martin explains why AI works best as a human amplifier, strengthening good engineering practices while exposing weak ones sooner than ever before. A significant theme in the episode is visibility. From shadow AI usage to expanding attack surfaces, Martin outlines why security teams are finding it harder to see where AI is being used and how data is flowing through systems. Rather than slowing teams down, he argues that the answer lies in embedding governance directly into delivery pipelines, making security automatic rather than an afterthought. We also explore the rise of agentic AI in testing, quality assurance, and security, where specialized agents act like virtual teammates. When well-designed, these agents help developers stay focused while improving reliability and resilience throughout the lifecycle. If you are responsible for engineering, platform, or security teams, this episode offers a grounded look at how to balance speed with responsibility in an AI-native world. As AI becomes part of every stage of software delivery, are your processes designed to safely absorb that change, or are they quietly becoming the bottleneck? Useful Links Learn More About Harness The State of AI in Engineering The State of AI Application Security EngineeringX Follow Harness on LinkedIn Connect With Martin Reynolds Thanks to our sponsors, Alcor, for supporting the show.