Tech Talks Daily

Have you noticed how every week brings a new headline about AI driven fraud, yet it still feels hard to tell what is real risk and what is noise? In this Tech Talks Daily episode, I'm joined by Tommy Nicholas, CEO of Alloy, for a candid conversation that cuts through the fear driven commentary and gets into what fraud teams are actually dealing with right now. We start with a simple but important distinction that gets blurred all the time. Tommy separates classic "fraud," where institutions take the hit, from "scams," where individuals are manipulated into handing over money or access. That framing changes how you think about solutions, accountability, and where AI is making things worse. Tommy also shares why he believes fraud losses are often massively underreported. It is not because people are trying to hide the truth, it is because organizations rarely have a single, clean view of losses across every product line and channel. Add messy labeling, split ownership across teams, and reporting becomes a best effort estimate rather than an objective number. That reality matters if you're building board level narratives, budgets, or risk models on top of survey data. From there, we talk about what organizations are getting right. Tommy argues there is no magical "undetectable" attack that forces teams to give up, but there is a very real breakdown happening in old fallbacks, especially human review of images and video. The bigger shift he sees is banks and fintechs finally pushing for consistent tooling across every channel, web, mobile, branch, call center, support tickets, because fraud does not respect internal org charts. We then get into why Alloy's AI Assistant is an interesting signal for where agentic AI is heading in regulated work. Tommy explains that agents are only useful when they have rigorous context, strong sources of truth, and clear workflows. Otherwise they guess, and "looks good" is not the same as "safe to run in production." He also lays out where agents can genuinely outperform humans, like scaling investigations during sudden surges, while keeping processes auditable and repeatable. We close by looking ahead at agentic commerce, and why Tommy thinks the breakthrough will arrive through weird, emergent behavior rather than a neat protocol roll out. When you listen back, do you think the next big leap in fraud prevention will come from better models, better data, or better operational discipline, and what would you bet on if your own customers were the ones on the line?

How do you prepare an entire generation for a world where AI is already shaping how we work, create, and solve problems? In this episode of Tech Talks Daily, I'm joined by Dr. Tara Nattrass, Chief Innovation Strategist for Education at Lenovo, for a grounded and thoughtful conversation about what responsible AI integration really looks like in K–12 classrooms. Tara brings more than 25 years of experience inside school districts, including serving as Assistant Superintendent for Teaching and Learning in Arlington Public Schools, so this isn't a theory-led discussion. It's informed by lived experience. We explore how the conversation has shifted over the past 18 months. AI has been present in schools for years through adaptive software and analytics, but the arrival of generative and now agentic AI tools has accelerated everything. As Tara explains, the debate is no longer about whether AI should be in schools. It's about how to approach it responsibly, strategically, and in ways that genuinely improve learning outcomes. A big theme in our conversation is AI literacy. Tara breaks this down in practical terms, moving beyond technical understanding to include critical thinking, creativity, collaboration, and the ability to evaluate risk and bias. She shares real examples of students designing AI tools to solve problems in their communities, shifting the focus from passive consumption to active creation. We also talk about infrastructure readiness. Many school systems have bold ambitions around AI, but there is often a gap between vision and technical capability. AI-ready devices, intelligent infrastructure, cybersecurity, and data governance all play a role in making innovation sustainable rather than experimental. Lenovo's approach, as Tara describes it, centers on building education ecosystems rather than simply refreshing hardware. There is also a careful balance to strike between innovation, privacy, and inclusion. From hybrid AI models to questions around where data is stored and who can access it, schools are navigating complex decisions. Tara shares how Lenovo partners with districts, policymakers, and organizations such as ISTE and ASCD to align infrastructure, professional learning, and governance frameworks. Looking ahead, we discuss what will separate school systems that truly benefit from AI from those that simply layer new tools onto old teaching models. Vision, educator upskilling, cybersecurity, and rethinking assessment all feature prominently in her answer. If you are working in education, technology leadership, or policy, this conversation offers a practical view of how AI-ready classrooms are being built today and what still needs to happen next. As always, I'd love to hear your thoughts. How is AI reshaping learning in your organization, and are you ready for what comes next?

What does autonomous IT really look like when you move beyond the slideware and start wiring systems together in the real world? At Dynatrace Perform in Las Vegas, I sat down with Pablo Stern, EVP and GM of Technology Workflow Products at ServiceNow, to unpack exactly that. Pablo leads the teams focused on CIOs and CISOs, building the workflows and security products that sit at the heart of modern IT organizations. From service desks and command centers to risk and asset management, his remit is clear: enable AI to work for people, not the other way around. We began with ServiceNow's deepening multi-year partnership with Dynatrace. While the announcement made headlines, Pablo was quick to point out that the real story starts with customers. This collaboration is rooted in a shared goal of helping joint customers reduce outages, improve SLA adherence, and shrink mean time to resolution. The vision of autonomous IT operations is not about hype. It is about connecting observability data with deterministic workflows so that insight can evolve into coordinated, system-level action. Pablo walked me through the maturity curve he sees emerging. First came AI-powered insight, summarizing data and surfacing signals from noise. Then came task automation, drafting knowledge articles, paging teams, triggering predefined playbooks. The next step, and the one that excites him most, is orchestrated autonomy. That means stitching together skills, agents, and workflows into systems that can drive end-to-end outcomes. It is a journey measured in years, not months, and it depends as much on digitizing process and building trust as it does on technology. We also explored root cause analysis, still one of the biggest time drains in IT. By combining Dynatrace's AI-driven observability with ServiceNow's workflow engine, enterprises can automate forensic steps, correlate events faster, and shorten the time spent on major incident bridges where teams debate ownership. Even incremental improvements in accuracy can save hours when incidents strike. Trust, of course, remains central. Pablo was candid that full self-healing systems are still some distance away. What we will see first is relief automation, controlled failovers, scripted actions suggested by machines but approved by humans. Over time, as confidence grows and processes become fully digitized, the balance will shift. Beyond the technology, a consistent theme ran through our conversation. Outcomes have not changed. Enterprises still want higher availability, faster resolution, better employee experiences. What is changing is the how. ServiceNow is reimagining its platform to deliver those outcomes at a much higher standard, not through incremental tweaks, but through rethinking workflows for an AI-first world. From design partnerships with banks building pre-flight change checks, to internal teams acting as the toughest customers, this was a grounded, practical conversation about where autonomous operations are headed and what it will take to get there. If you are a CIO, CISO, or IT leader wondering how to move from theory to execution, this episode offers a clear-eyed look behind the curtain.

What happens when nearly half of organizations admit they have no AI-specific security controls, yet AI-driven data leaks are accelerating at the same time? In this episode of Tech Talks Daily, I spoke with Aayush Choudhry, CEO and co-founder of Scrut Automation, about what he sees as a blind spot in the cybersecurity industry. While much of the market continues to design tools for Fortune 500 enterprises with deep pockets and large security teams, Aayush argues that the real existential risk sits with the 99 percent of businesses that cannot survive a serious breach. Aayush brings a founder's perspective shaped by firsthand pain. Before launching Scrut, he and his co-founder experienced the grind of managing compliance and security as a cloud-native startup trying to sell into enterprises. They were outsiders to GRC and security at the time, forced to learn from first principles. That experience became the foundation for Scrut Automation, a modern GRC platform built specifically for small and mid-sized companies that cannot afford six-month implementations, armies of consultants, or half-million-dollar tooling budgets. We explore why treating compliance and security as separate functions increases risk for smaller organizations. In the mid-market, the same small team is often responsible for both. When compliance is handled as a box-ticking exercise and security as a separate technical discipline, gaps emerge. Scrut's approach converges governance, risk, and security signals into a unified layer that translates hundreds of technical alerts into context-aware risks that actually matter to the business. Our conversation also tackles AI complacency. Using the classic confidentiality, integrity, and availability framework, Aayush outlines what minimum viable AI security hygiene looks like in practice. That includes ensuring AI agents are not over-privileged compared to the humans they represent, placing guardrails around sensitive data fed into models, and extending supply chain security thinking to agentic integrations. For resource-constrained teams, these are not theoretical concerns. They are daily realities. Perhaps most compelling is his view that AI can act as a force multiplier for small teams. By embedding accumulated expertise into agents trained on anonymized patterns and edge cases, Scrut aims to democratize security know-how that would otherwise require multiple full-time analysts. The goal is simple but ambitious: make enterprise-grade security outcomes accessible without enterprise-grade headcount. If you are leading a small or mid-sized business and wondering how to balance growth, compliance, and AI risk without breaking the bank, this conversation offers a candid look from the trenches.

How do you build enterprise software for the companies that keep the world turning, while also building a leadership culture where people can actually thrive? In this episode of Tech Talks Daily, I spoke with Kerrie Jordan, Group VP of Product Management at Epicor, about her journey from studying literature to helping shape cloud ERP strategy at a global software company serving more than 20,000 customers worldwide. Kerrie's story is a reminder that there is no single path into technology leadership. Sometimes the foundations are laid in unexpected places, through storytelling, creativity, and a deep curiosity about people. Kerrie shares how her early career in product lifecycle management opened her eyes to the human side of software. Interviewing customers and writing case studies showed her that behind every system implementation is a personal story, a career milestone, or a business trying to survive and grow. That perspective still shapes how she approaches product and marketing today at Epicor, a company recently recognized as a Leader in the Gartner Magic Quadrant for Cloud ERP for Product-Centric Enterprises for the third consecutive year. But this conversation goes far beyond market recognition. We talk openly about burnout, resilience, and the reality of leading through pressure. Kerrie reflects on the importance of protecting time, creating space to reconnect, and building a culture where empathy is practiced, not just discussed. Her view of leadership is grounded in communication, psychological safety, and being tough on problems rather than people. Mentorship is another thread running throughout our discussion. Kerrie explains why powerful mentorship is not passive. It requires vulnerability, preparation, and a willingness to hear difficult advice. A single phrase from a mentor early in her career, "stick-to-itiveness," continues to shape how she approaches hard problems today. We also explore the future of women in manufacturing and technology. Kerrie highlights the need for intentional change across education, early career development, and leadership visibility. She believes technology, particularly AI, can expand access, enable upskilling, and introduce flexibility that supports long-term career growth. At the same time, she makes a simple but powerful point. Women in tech want the same thing as anyone else: the space and autonomy to do their jobs well. From customer co-innovation and community-driven product roadmaps to inclusive leadership under commercial pressure, this episode offers a candid look at what it really takes to lead in enterprise technology today. If you are building products, leading teams, or questioning your own next career step, I think you will find something in Kerrie's story that resonates.

Why do so many of us feel busy all day, yet struggle to point to the meaningful work we actually completed? In this episode of Tech Talks Daily, I sit down with Tomás Dostal Freire, CIO of Miro, to unpack a challenge that quietly drains modern organizations. Tomás brings experience from companies like Google, Netflix, and Booking.com, and now leads both IT and business acceleration at Miro. His focus is simple but ambitious. Move beyond AI experimentation and rethink how work itself gets done. We explore new research revealing that for every hour of creative work, employees lose up to three hours to meetings, admin, emails, and maintenance tasks. That ratio is more than an inconvenience. It affects decision-making speed, employee satisfaction, and ultimately a company's ability to compete. Tomás argues that future candidates will choose employers based on how much unnecessary internal work they are expected to tolerate. In other words, reducing busy work is quickly becoming a talent strategy. One of the biggest culprits? Context switching. With dozens of browser tabs open and information scattered across tools, teams spend more time stitching together fragments than making decisions. Tomás describes how duplication of work, outdated systems, and a lack of shared context quietly erode momentum. AI, he believes, should not create more noise or another standalone tool. It needs to be embedded where collaboration already happens. We discuss the difference between single-player AI moments, where individuals use tools in isolation, and multiplayer AI collaboration, where shared context allows teams to move faster together. At Miro, this philosophy has shaped what they call an AI Innovation Workspace, a shared canvas where human insight and AI assistance coexist in real time. Tomás also shares practical advice for leaders who want to reclaim creative time. Start by identifying tasks you dislike doing that could easily be handled by someone junior. That list often reveals what AI can already automate. Then focus on building transferable skills like cognitive agility and first-principles thinking, rather than chasing every new tool. If you are wrestling with burnout, fragmented workflows, or wondering how AI can genuinely improve collaboration without overwhelming teams, this conversation offers a grounded, optimistic perspective. And yes, we even add a Beatles classic to the Spotify playlist along the way.

How do you protect millions in revenue during your busiest hour of the year when your entire business depends on digital performance? At Perform 2026, I caught up with Alex Hibbitt, Engineering Director responsible for the customer platform at Storio Group, to unpack what happens when observability moves from an engineering afterthought to a board-level priority. Storio Group was formed from the merger of Photobox and Albelli, bringing together multiple brands and five separate e-commerce platforms into one unified customer journey. That consolidation created opportunity, but it also exposed risk, especially during peak trading from Black Friday through Black Sunday and into the Christmas rush. Alex shared what it really looks like when downtime is non-negotiable. At peak, Storio's platform can generate up to 1.5 million euros per hour. A single poorly timed incident is not simply a technical problem, it is a direct threat to revenue and customer trust. Before partnering with Dynatrace, the team was relying heavily on centralized logging, processing over a billion log lines a day and depending on engineers to manually interpret signals. It was reactive, labor intensive, and left too much to chance. What stood out for me was how cultural change led the transformation. Rather than imposing a new tool from the top down, Alex and his team built a maturity model engineers could relate to, created internal champions, and framed observability as risk management and business protection. The result was a reported 65 to 70 percent reduction in log costs, a 50 percent drop in mean time to detect overall, and up to 90 percent improvement for the most severe incidents. We also explored how unifying logs, metrics, and traces into a single AI-driven platform helped Storio move from reactive firefighting to proactive detection. During one Black Sunday alone, three major issues were identified early enough to avoid an estimated 4.5 million euros in potential impact. This conversation goes beyond tooling. It is about protecting customer experience, safeguarding revenue during peak demand, and building an engineering culture that embraces change. If your organization is wrestling with cloud costs, fragmented monitoring, or the pressure to deliver flawless digital performance under load, there are some powerful lessons here.

How do you design financial infrastructure that keeps running when the unexpected hits, whether that is a regional outage, a regulatory shift, or a sudden spike in digital demand? In this episode of Tech Talks Daily, I'm joined by Katsutoshi Itoh from Sony and Masahisa Kawashima from NTT, both representing the IOWN Global Forum, to unpack how photonics-based networks could change the foundations of digital finance. Speaking with me from Kyoto, they share how the Innovative Optical and Wireless Network vision is moving beyond theory and into practical, finance-specific use cases. Financial institutions are under constant pressure to deliver uninterrupted services while meeting ever tighter compliance standards. Yet as we discuss, many existing architectures still rely on asynchronous data replication and layered resilience added after the fact. On paper, it works. In a real disruption, gaps quickly appear. Itoh and Kawashima explain how synchronous replication over ultra-low latency optical networks can reduce the risk of data loss while simplifying disaster recovery and lowering operational complexity. We also explore the role of Open All-Photonic Networks and why reducing packet forwarding layers can dramatically cut latency and infrastructure costs. Instead of concentrating compute and storage in dense urban data centers, photonics enables distributed computing across regions while maintaining deterministic performance. That shift opens the door to improved resilience, better infrastructure utilization, and new approaches to scaling without constant over-provisioning. Sustainability sits alongside resilience in this conversation. Rather than treating energy efficiency as a compromise, the IOWN vision distributes power demand geographically, making better use of locally available renewable energy and reducing concentrated load pressures. It is a subtle but important rethink of how infrastructure supports broader societal goals. Looking ahead, we consider what this could mean for digital banking platforms, AI-driven risk management, and cross-border financial services. If infrastructure limitations fall away, institutions can design services around business needs rather than technical constraints. If you are curious about how photonics could underpin the next generation of financial services, this episode offers a grounded and thoughtful perspective. As always, I would love to hear your thoughts after listening.

Have you ever wondered why "compliance" still gets treated like a slow, spreadsheet-heavy chore, even though the rest of the business is moving at machine speed? In this episode of Tech Talks Daily, I sit down with Matt Hillary, Chief Information Security Officer at Drata, to talk about what actually changes when AI and automation land in the middle of governance, risk, and compliance. Matt brings a rare viewpoint because he lives this day-to-day as "customer zero," running Drata internally while also leading IT, security, GRC, and enterprise apps. We get practical fast. Matt shares how AI-assisted questionnaire workflows can turn a 120-question security assessment from a late-afternoon time sink into something you can complete with confidence in minutes, then still make it upstairs in time for dinner. He also explains how automation flips the audit dynamic by moving from random sampling to continuous, full-population checks, using APIs to validate evidence at scale, without hounding control owners unless something is actually wrong. We also talk about what security leadership really looks like when the stakes rise. Matt reflects on lessons from his time at AWS, why curiosity and adaptability matter when the "canvas" keeps changing, and how customer focus becomes the foundation of trust. That theme runs through the whole conversation, including the idea that the CISO role is steadily turning into a chief trust officer role, where integrity, transparency, and credibility under pressure matter as much as tooling. And because burnout is never far away in security, we dig into the human side too. Matt unpacks how automation can reduce cognitive load, but also warns about swapping one kind of pressure for another, especially when teams get trapped producing endless dashboards and vanity metrics instead of focusing on the few measures that actually reduce risk. To wrap things up, Matt leaves a song for the playlist, Illenium's "You're Alive," plus a book recommendation, "Lessons from the Front Lines, Insights from a Cybersecurity Career" by Asaf Karen, which he says stands out for how it treats the human side of security leadership. If you're thinking about modernizing compliance in 2026 without losing the human element, his parting principle is simple and powerful: be intentional, keep asking why, and spend your limited time on what truly matters. So where do you land on this shift toward continuous trust, do you see it becoming the default expectation for buyers and auditors, and what should leaders do now to make sure automation reduces pressure instead of quietly adding more? Share your thoughts with me, I'd love to hear how you're approaching it.

Can designing for human error become the strongest cybersecurity strategy in an AI-accelerated world? In this episode, I sit down with Yaz Bekkar, Principal Consulting Architect for Barracuda XDR and a member of the company's Office of the CTO, to explore why the speed introduced by AI is changing the risk equation for every organization. As automation allows teams to move faster, it also means small mistakes can scale at machine speed. Yaz argues that resilience in 2026 is no longer about trying to prevent every incident. It is about anticipating failure, containing the blast radius, and recovering quickly without bringing the business to a standstill. Our conversation challenges one of the most persistent narratives in security, the idea that people are the weakest link. Yaz explains why safeguarding the workforce begins with reshaping the environment they operate in. When the secure option is also the easiest and fastest path, risky shortcuts begin to disappear. From secure defaults and least-privilege access to paved-road workflows for administrators, he shares practical examples of how organizations can reduce complexity, limit exposure, and support better decisions under pressure. We also tackle the limits of annual compliance training and the cultural shift required to build real cyber resilience. Yaz makes the case for continuous, bite-sized practice embedded into everyday work, from three-minute phishing simulations that teach without blame to short, hands-on misconfiguration drills for technical teams. The result is stronger habits, faster response times, and a security posture designed for real human behavior rather than ideal conditions. If AI is accelerating both innovation and risk, how do leaders move from a prevention-only mindset to resilient operations that protect business continuity when controls fail? And what would change in your organization if every system was designed with the assumption that someone, somewhere, will eventually make a mistake?

At Davos this year, some of the biggest names in tech sent a clear signal. AI is no longer a novelty. It is no longer a proof-of-concept exercise. As Demis Hassabis of Google DeepMind suggested, AI will shape more meaningful work. And Satya Nadella of Microsoft was even more direct. AI only matters if it improves real outcomes for people. So what does that look like inside the enterprise? In this episode of Tech Talks Daily, I'm joined by Andrew Boyagi, Customer CTO at Atlassian, to unpack how the conversation has shifted from experimentation to execution. Developers, in many ways, are the perfect lens for understanding this moment. Over the last two decades, their role has expanded far beyond writing code. They now own products, infrastructure, operations, and business outcomes. AI is simply the next chapter in that evolution. Andrew argues that AI will not replace engineers. It will raise expectations. As intelligent tools absorb repetitive work, the real value moves up the stack. System design. Architectural thinking. Reviewing and refining AI-generated output and orchestrating solutions that solve genuine business problems. And through it all, humans remain firmly in the loop. We also explore what this means for leadership, why mindset is starting to matter more than technical skill alone, how organizations can avoid layering AI on top of broken processes. And why the companies pulling ahead are treating AI as a strategic discipline, not a feature upgrade. This is a conversation grounded in reality. It speaks to product leaders, CTOs, CIOs, and anyone asking a simple but powerful question. If we are investing in AI, what are we actually getting back? And before we close, we look ahead to Team '26 and the themes Andrew and his team are already working on. If this year has been about proving value, what will the next chapter demand from enterprise leaders? As always, I'd love to hear your thoughts. Are you seeing proof of value in your organization yet, or are you still working through the pilot phase?

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 Adam FieldTungsten 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 newsletterand theNeurons.The Reshaping Workflows podcastConnect with Logan LawlerFollow 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 LayCisco LiveCisco 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 DharThe Enterprise in 2030Learn More About IBM Consulting