The Tech Blog Writer Podcast

What can leadership in Formula One teach the rest of us about business transformation? In this episode of the Tech Talks Daily Podcast, I sit down with Seb Sheppard, whose career has taken him from flying helicopters in the Royal Navy to leading engineering teams in Formula One and steering post-merger integrations across multiple industries. His story isn't just one of impressive career shifts but of understanding what truly drives high performance—people, trust, and focus. Seb shares how growing up in Chile and working across different cultures taught him the value of clear communication and empathy in leadership. He explains why protecting technical teams from distractions can often be the most productive thing a leader can do, and how wellbeing initiatives work best when driven by employees themselves rather than top-down policies. Drawing on his time at Alpine F1, he also reveals the delicate balance between cost control and performance improvement, describing how he helped grow the engineering team by a third while staying within strict budget limits. Our conversation also explores the human side of mergers and acquisitions. Seb discusses why integration efforts often fail when companies overlook culture and people, and how proactive communication—long before an announcement is made—can make the difference between success and attrition. He also speaks about the evolving relationship between technology and leadership, explaining how AI can be embraced without losing the human element that drives creativity and trust. If you're a leader facing constant change, this episode is a masterclass in adaptability, humility, and practical wisdom. You'll come away with lessons from both the skies and the racetrack that apply directly to your own teams and projects. Connect with Seb Sheppard at www.sebsheppard.com or on LinkedIn at linkedin.com/in/sebsheppard. Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

How do you secure a world where trusted internal traffic now travels over the public internet? That's the question I put to Doug Merritt, CEO of Aviatrix, in this thought-provoking conversation recorded for Tech Talks Daily. Doug brings decades of experience from his time leading Splunk and other major technology players, and he now finds himself at the forefront of reshaping how enterprises think about cloud security. We discuss why the cybersecurity landscape is more treacherous than ever, especially as AI accelerates both defense and attack capabilities. Doug explains why the old "castle and moat" mindset no longer applies in the age of cloud workloads, where perimeters are atomized and workloads are ephemeral. He outlines how identity, endpoint, and network security form a three-legged stool—yet too many organizations focus on one leg while neglecting the others. Doug also shares why embedding protection directly into the network fabric changes the rules for defending the cloud, and how his team at Aviatrix is helping companies close dangerous visibility gaps. We explore the rise of agentic AI, the growing sophistication of lateral movement attacks, and why even trusted identities can pose risk in distributed environments. As we look to the future, Doug argues that the path forward is clear: build on strong foundations, simplify the noise, and make network visibility a first-class citizen in enterprise defense. What do you think—are most organizations ready to shift from bolted-on tools to truly embedded cloud security? I'd love to hear your thoughts after listening. Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

  Every software team, no matter its size or sophistication, has wrestled with the same quiet threat, technical debt. But what if the issue isn't just messy code or outdated frameworks, but something more human? That's the question Ernesto Tagwerker, Founder and CEO of OmbuLabs.ai, has been asking as he works at the intersection of AI, developer experience, and legacy modernization. In this episode of Tech Talks Daily, Ernesto joins me from Philadelphia to unpack why technical debt is so misunderstood and why the term has drifted far from Ward Cunningham's original metaphor. He argues that many teams treat it as a one-off cleanup task when, in reality, it's a living health issue that must be managed continuously. As he explains, "Every time you have to work around messy code, you're paying interest. And if later never comes, that interest piles up until progress grinds to a halt." We explore how AI is changing the way engineers think about remediation and developer experience (DX). Ernesto shares how OmbuLabs.ai uses AI agents to automate parts of the Rails upgrade process, scanning codebases for deprecations and generating actionable plans for clients. But his caution is clear, these tools are only as smart as the people orchestrating them. When used carelessly, they can generate invisible layers of new debt just as fast as they resolve the old. Ernesto also reflects on research from Google that reveals how "technical debt" varies wildly between teams and projects. He explains why leadership alignment is vital, how recurring surveys can help identify developer pain points, and why organizations should measure "technical health" rather than chase the unrealistic goal of zero debt. We discuss the cultural shift required for long-term success and why allocating even 10 to 20 percent of each sprint to DX improvements can dramatically reduce burnout and turnover. Finally, Ernesto offers his take on the future. AI will continue to automate repetitive work and surface smarter insights, but human oversight remains non-negotiable. In his words, "AI agents are only as good as their human operator." This conversation goes beyond code reviews and sprint retrospectives. It's about redefining what progress means in software development, healthier systems, happier developers, and smarter collaboration between humans and machines. Listen now to hear how Ernesto Tagwerker and OmbuLabs.ai are rethinking technical debt, DX, and AI-driven engineering for the decade ahead.   Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

What if business intelligence didn't stop at answering what happened, but could finally explain why? In this episode of Tech Talks Daily, I sit back down with Alberto Pan, Chief Technology Officer at Denodo, to unpack how Deep Query is redefining enterprise AI through reasoning, transparency, and context. We explore how Deep Query functions as an AI reasoning agent capable of performing open-ended research across live, governed enterprise data. Instead of relying on pre-built dashboards or static reports, it builds and executes multi-step analyses through Denodo's logical data layer, unifying fragmented data sources in real time. Alberto explains how this semantic layer provides the business meaning and governance that traditional GenAI tools lack, transforming AI from a surface-level Q&A system into a trusted analytical partner. Our conversation also digs into the bigger picture of explainable AI. Deep Query reports include a full appendix of executed queries, allowing users to trace every insight back to its source. Alberto breaks down why this level of auditability matters for enterprise trust and how Denodo's support for the Model Context Protocol (MCP) opens the door to more interoperable, agentic AI systems. As we discuss how Deep Query compares with RAG models and data lakehouses, Alberto offers a glimpse into the future of business intelligence—one where analysts become guides for AI-driven research assistants, and decision-makers gain faster, deeper, and more transparent insights than ever before. So what does the rise of reasoning agents like Deep Query mean for the next generation of enterprise AI? And how close are we to a world where AI truly understands the why behind the data? Tune in and share your thoughts after listening.   Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

For years, many businesses believed that Apple devices were inherently secure. That illusion has faded. In this episode of Tech Talks Daily, I speak with Adam Boynton, Senior Security Strategy Manager at Jamf, about why visibility across macOS and iOS is no longer a luxury but a necessity. Adam explains how Jamf has evolved from device management to full Apple-native security intelligence, protecting over 75,000 organizations and more than 32 million devices. He shares how attackers no longer target individual operating systems but entire ecosystems, exploiting the gaps between how Apple secures its platforms and how enterprises actually monitor them.  From real-world cases to lessons learned at Jamf's annual JNUC conference, Adam describes how telemetry provides security teams with the truth about what's really happening on their endpoints, enabling them to transition from reactive incident response to proactive defense. Our conversation covers everything from the architectural blind spots that traditional Windows-centric tools can't see to the rise of AI-driven analysis that turns complex forensic investigations into minutes-long processes. We also explore how Jamf's partnerships, such as those with Elastic, are creating an open and integrated future for enterprise security, blending deep Apple signals with cross-platform context. For anyone still clinging to the myth that macOS or iOS "just work" without attention to security, this episode is a wake-up call. Adam outlines practical advice on patching, mobile hygiene, and zero trust, while revealing how Jamf's latest innovations are quietly making the most secure way the easiest way for users. Listen to hear how Jamf is redefining modern Apple security, turning management, identity, and protection into a seamless whole, and why accurate visibility—not assumptions—is now the objective measure of cybersecurity readiness. Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

There was a time when a computer science degree almost guaranteed a fast track into a well-paid career. But that promise is slipping. In today's Tech Talks Daily episode, I reconnect with Hans de Visser, Chief Technology Officer at Mendix, to discuss why recent graduates are finding it more challenging than ever to secure their first role in technology, and what they can do about it. Hans brings decades of experience in software engineering and low-code innovation, and his perspective on today's market is both sobering and optimistic. We discuss new research indicating a sharp decline in junior developer openings since 2024 and explore how the rapid rise of AI has altered the hiring equation.  The expectation now is that young developers arrive fluent in automation, generative AI, and multidisciplinary tools —skills that few university programs can thoroughly teach. Yet, as Hans points out, this doesn't mean opportunity has vanished. It just looks different. Our conversation unpacks what this new reality means for aspiring developers. Hans explains how Mendix evaluates candidates by testing their ability to think critically about AI-assisted code rather than generate it.  He explains why graduates must master both traditional software foundations and modern tools, such as low-code platforms and agile applications. And he offers advice on building a mindset of lifelong learning, staying curious, experimenting with new tools, and understanding how AI can amplify rather than replace human creativity. For anyone feeling disheartened by the tightening job market, Hans offers balance and hope. He believes that as the definition of software developer evolves, new hybrid roles will emerge at the intersection of business, creativity, and technology. The graduates who will thrive are those who treat AI as a collaborator, not a competitor. Listen to this episode to hear how Mendix is helping redefine what it means to build software in the age of AI, and why today's tech graduates may need to think less about securing a single job title and more about creating a career that never stops learning. Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.    

What does cybersecurity look like beyond Earth's atmosphere? That's the question at the heart of this conversation with Kristiina Omri, Vice President of Special Programs at CybExer Technologies, and Aare Reintam, the company's COO. We met in Tallinn on the eve of the Software Defined Space Conference to explore how Estonia, in collaboration with the European Space Agency, is helping define the future of space cybersecurity through the world's first Space Cyber Range. The story begins unexpectedly—with a childhood memory of marmalade in a tube, the same kind sent to Soviet astronauts in orbit. For Aare Reintam, that small detail became the first spark of fascination with space, one that decades later evolved into CybExer's partnership with ESA. Together they've created a digital testing environment where satellites, ground stations, and communication protocols can be stress-tested for cyber resilience long before launch.  It's a bold move in an era when satellites underpin everything from GPS and precision farming to air travel and climate observation, yet often rely on decades-old technology vulnerable to attack. Kristiina Omri explains how the Space Cyber Range replicates real-world missions, allowing engineers and analysts to train under simulated attack conditions that feel indistinguishable from their actual control systems. The range combines the precision of digital twins with the competitive intensity of cyber exercises, preparing teams for threats that can ripple from orbit to everyday life on Earth. The conversation covers everything from the growing space-debris crisis to the global shortage of cybersecurity professionals, and the urgent need to blend space engineering with cyber education. We also discuss the deeper strategic implications. What happens when quantum computing enters the battlefield? How should Europe prepare for the convergence of cyber and kinetic threats in orbit? And what lessons can be learned from Estonia's leadership in NATO cyber defense as it extends that expertise to the stars? By the end of the discussion, one theme stands out clearly: the future of cybersecurity is no longer confined to our planet. From digital twins to orbital trust networks, CybExer Technologies and the European Space Agency are proving that the next frontier for cyber readiness lies in space itself.

In this episode, I sit down in Tallinn with Madis Võõras, Head of the Estonian Space Office at Enterprise Estonia, to unpack how Estonia is carving out a real role in the European space sector through brains, code, and smart partnerships. Madis explains how his team connects Estonian companies with the European Space Agency, brings public investment back into the local economy, and uses space projects as a launchpad for globally competitive products and services. He shares why Estonia's sweet spot is software, how the country's digital public infrastructure became a reference point for European programs, and why the next wave of value will come from data, cybersecurity, and rapid deployment rather than rockets alone. We also talk about what it takes to build a space economy in a market of 1.3 million people. Madis walks through lessons from early contracts, the rise of an Earth observation data hub, and a business incubator that has already helped dozens of founders move from idea to revenue. He is candid about the gaps too, including the need for more hardware depth and the reality that international cooperation is the fastest route to scale. From optical communications between Tallinn and Helsinki to the practical use of AI inside satellite programs, you will hear a pragmatic roadmap rather than hype. If you want a grounded look at how space policy meets startup grit, and why collaboration with the European Space Agency is a catalyst rather than a finish line, this conversation is for you. What should Estonia prioritize next to punch above its weight in the global space economy, and where do you see the biggest opportunities for software and AI in space services? Share your thoughts and join the discussion.

What role does cybersecurity play when the battlefield extends beyond Earth's atmosphere? In this special episode recorded live in Tallinn for the fifth anniversary of the Software Defined Space Conference, I sit down with Kalev Koidumäe, CEO of the Estonian Defence and Aerospace Industry Association, to explore how software and security are transforming the future of space and defense. Kalev shares how Estonia, a nation of just 1.3 million people, has built global credibility through innovation, collaboration, and cyber resilience. From the lessons of the 2007 state-level cyberattack to the country's integration of space technologies within NATO's defense framework, Estonia has developed a model that combines agility with strategic foresight. Our conversation spans everything from the evolution of Estonia's space sector to its growing ecosystem of AI-driven defense technologies, autonomous systems, and satellite solutions. Kalev also explains how lessons from the war in Ukraine are reshaping Europe's defense landscape and accelerating the need for resilient, software-defined systems. What makes this discussion particularly fascinating is the balance Estonia maintains between national sovereignty and international cooperation. Kalev explains how the country's reserve army model, cyber education initiatives, and public-private partnerships have created an ecosystem where innovation is both strategic and deeply rooted in civic responsibility. It's a blueprint for how smaller nations can play a meaningful role in global security through ingenuity and collaboration. As the world navigates an era of heightened geopolitical tension and rapid technological advancement, this discussion offers a glimpse into how small nations can make a big impact in securing both cyberspace and outer space. So what can larger nations learn from Estonia's approach to innovation, readiness, and cyber defense? And how might software continue to redefine the future of space security? Share your thoughts after listening.

Artificial intelligence has changed how we think about service, but few companies have bridged the gap between automation and genuine intelligence. In this episode of Tech Talks Daily, I'm joined by Puneet Mehta, CEO of Netomi, to discuss how customer experience is evolving in an age where AI doesn't just respond but plans, acts, and optimizes in real time. Puneet has been building in AI long before the current hype cycle. Backed by early investors such as Greg Brockman of OpenAI and the founders of DeepMind, Netomi has become one of the leading platforms driving AI-powered customer experience for global enterprises. Their technology quietly powers interactions at airlines, insurers, and retailers that most of us use every day. What makes Netomi stand out is not its scale but the philosophy behind it. Rather than designing AI to replace humans, Netomi built an agent-centric model where AI and people work together. Puneet explains how their Autopilot and Co-Pilot modes allow human agents to stay in control while AI accelerates everything from response time to insight generation. It is an approach that sees humans teaching AI, AI assisting humans, and both learning from each other to create what he calls an agentic factory. We explore how Netomi's platform can deploy at Fortune 50 scale in record time without forcing companies to overhaul existing systems. Puneet reveals how pre-built integrations, AI recipes, and a no-code studio allow business teams to roll out solutions in weeks rather than months. The focus is on rapid time-to-value, trust, and safety through what he calls sanctioned AI, a framework that ensures governance, transparency, and compliance in every customer interaction. As our conversation unfolds, Puneet describes how this evolution is transforming the contact center from a cost center into a loyalty engine. By using AI to anticipate needs and resolve issues before customers reach out, companies are creating experiences that feel more personal, more proactive, and more human. This is a glimpse into the future of enterprise AI, where trust, speed, and empathy define the next generation of customer experience. Listen now to hear how Netomi is reimagining the role of AI in service and setting new standards for how businesses build relationships at scale.

What if the biggest barrier to AI adoption isn't the technology itself, but our ability to learn, adapt, and reskill? That question sits at the heart of my conversation with Sagar Goel, Managing Director and Partner at Boston Consulting Group, who leads the firm's global work on digital workforce development and reskilling. Speaking from Singapore, Sagar brings a rare combination of data, strategy, and humanity to the discussion on how AI is reshaping the global workforce—and why the frontline is struggling to keep up. Drawing on BCG's latest "AI at Work" research, Sagar reveals a surprising trend: frontline AI usage has stalled at around 50 percent for the first time. He explains why many companies are still approaching AI as a tool rollout rather than a behavioral and cultural shift. According to him, employees often don't know where or how to use AI effectively, leadership support is lacking, and training programs are too shallow to spark genuine adoption. The result is a productivity paradox—AI potential without real impact. Sagar also unpacks another counterintuitive finding: leaders are more worried than their teams about losing their jobs to automation. He attributes this to leaders' heightened awareness of structural disruption and their own vulnerability in adapting mid-career. Meanwhile, countries across the Global South are outpacing the US in AI adoption, driven by youthful populations, economic necessity, and a hunger for differentiation in tight job markets. Throughout the discussion, Sagar draws a clear line between upskilling and reskilling—two terms often used interchangeably but representing distinct needs. Upskilling, he explains, should embed AI fluency into daily workflows from the CEO down, while reskilling must redeploy people into new, higher-value roles as automation accelerates. He cites IKEA's decision to retrain 8,000 call center staff into design consultants as a model example of turning disruption into opportunity. We close with a candid reflection on leadership responsibility in the age of AI. For Sagar, the message is simple but profound: if skills don't show up on your balance sheet, they won't show up in your business performance. As the half-life of skills shrinks to five years, he urges CEOs to integrate workforce readiness directly into strategy, or risk being outpaced by those who do. This episode is a grounded, data-driven look at what it truly takes to prepare people—not just machines—for an AI-driven world.

What if your cybersecurity strategy could become your biggest sales advantage? In this episode, I sit down with Taylor Hersom, Founder and CEO of Eden Data, to explore how startups can transform compliance from a box-ticking exercise into a true growth engine. Taylor's journey is remarkable: a former Deloitte executive who quit his job the week before the world shut down, launched Eden Data on Upwork with just $16, and went on to build one of the fastest-growing cybersecurity advisory firms, now recently acquired by Riveron. His story blends grit, reinvention, and a deep understanding of how trust has become the real currency of modern business. We talk about how to safeguard customer data in an AI-first world and why shadow AI has become the new shadow IT problem for many companies. Taylor explains why cybersecurity can no longer sit in the IT department alone, sharing examples of how marketing and customer experience leaders are now leveraging security as a differentiator to win contracts and customer loyalty. He also opens up about the human side of cyber risk, why most breaches stem from simple mistakes, and how gamifying awareness training can be more effective than annual compliance videos no one remembers. As AI rapidly reshapes the digital landscape, Taylor shares his perspective on what future global standards for AI security could look like and how leaders can bake compliance and security into their products from day one. His view is clear: the next generation of successful startups will treat cybersecurity as both a foundation and a brand promise. How ready are you to make trust part of your growth strategy? And how will your business adapt as cybersecurity becomes not just a requirement, but a competitive edge? Share your thoughts after listening.

What happens when early-stage founders realise their go-to-market strategy just isn't working? Do they double down on outdated advice or take a fresh look at how modern buyers actually engage?  In this episode of Tech Talks Daily, I sit down with Richard Lowry, founder of Springboard IQ, to unpack how he's helping startups rebuild broken GTM strategies in just seven days through a crowdsourced, operator-led model that challenges everything we think we know about growth. Richard explains how Springboard IQ brings together six active operators to co-create a go-to-market blueprint that's fast, focused, and grounded in the realities of today's market. This approach delivers practical strategy and design rather than execution, giving founders clarity on where to focus their time and energy. As Richard puts it, founders should save their passion for the demo because that's where it really matters. The conversation explores why technical founders often mis-hire sales talent, why relying on outdated accelerator advice can derail growth, and why many teams hit a “GTM wall” long before real scale begins. We also discuss why the future of GTM might look very different from the digital-first strategies of the past. As inboxes flood with automated outreach and AI-generated content, Richard believes human-led activation through curated events, community experiences, and even spontaneous moments of connection will define the next era of startup growth. It's a conversation that blends practical lessons, honest stories (including one involving a soup kitchen in Lisbon), and a call to bring the human element back to how we sell, connect, and grow. So, could a crowdsourcing strategy from active operators be the smarter way for startups to go to market? And in an era of AI-saturated noise, will the next big differentiator simply be showing up in person? I'd love to hear your thoughts after you listen.

What happens when a world-class badminton player trades the court for the courtroom and then the boardroom? In this episode of Tech Talks Daily, I sit down with Tom Dunlop, CEO and co-founder of Summize, to explore how a former Great Britain athlete became one of the most forward-thinking leaders in legal technology. Tom shares how his journey from sport to law to entrepreneurship shaped his leadership philosophy and his belief in “high agency,” the mindset of taking ownership, driving action, and leading from the front. We talk about how that outlook helped him transform the traditional image of legal work into something faster, smarter, and more collaborative through Summize's AI-powered Contract Lifecycle Management platform. Rather than forcing users to adopt new software, Summize integrates directly into tools people already use like Teams, Slack, Outlook, and Word, embedding contract management seamlessly into everyday workflows. We also explore Tom's reflections on brand building in a historically conservative industry, the mental shift from risk-averse lawyer to decisive founder, and why he believes legal leaders should embrace innovation as a way to strengthen their role at the boardroom table. His story is as much about personal reinvention as it is about technological disruption, revealing how determination, discipline, and curiosity can reshape even the most traditional professions. So, how do you balance precision with risk when you move from legal advisor to entrepreneur? And what lessons from sport, law, and leadership can help us all perform better in the fast-changing world of work? I'd love to hear your thoughts after listening.

What if the key to creating cleaner, faster, and more efficient cities isn't building new infrastructure, but rethinking how we move what we already have? In this episode of Tech Talks Daily, I'm joined by Richard Savoie, co-founder of Adiona, whose AI-powered logistics platform is transforming how goods travel through urban environments. Richard's background as a patent-holding engineer and mentor in the medical device field gives him a unique perspective on precision, quality, and human-centered design. At Adiona, he applies that same discipline to logistics, helping delivery networks run smarter, leaner, and more sustainably. His FlexOps platform uses AI to optimise routes, model EV fleet conversions, and create digital twins of operations so companies can reduce emissions and increase efficiency—without replacing the people who make it all work. In our conversation, Richard shares why he believes in a humanistic approach to AI that empowers drivers, dispatchers, and warehouse workers instead of automating them out of existence. We also explore how Adiona's scenario modelling helps global brands like Coca-Cola and Australia Post cut costs while meeting 2030 sustainability goals, and what the future might hold as AI and robotics begin to converge for last-meter delivery. So, could the next big sustainability breakthrough come from reimagining the routes that already exist? And how might AI reshape the logistics networks that keep our world moving? I'd love to hear your thoughts after listening.

What if the key to unlocking real AI transformation isn't a new enterprise platform or an executive directive, but something much simpler: listening to the innovators already inside your company?  That's the idea behind AppDirect CTO Andy Sen's philosophy on bottom-up AI adoption. In this episode, we talk about why the most effective AI strategies often begin at the grassroots level, led by curious employees who experiment first and ask for permission later. Andy explains how AppDirect built a culture of AI experimentation by giving employees a secure “digital playground” to test ideas, measure results, and scale what works. From developers using AI to write half of the company's new code to non-technical staff building internal tools, AppDirect's approach has driven measurable productivity gains while cutting costs and improving efficiency. Rather than dictating from the top, leaders are encouraged to ask questions, support innovation, and apply a “yes, but” mindset that modifies solutions for governance and compliance instead of shutting them down. As organizations everywhere wrestle with how to scale AI responsibly, Andy offers a fresh take on balance by empowering employees to build while ensuring security and oversight. We also explore the rise of developer-focused platforms like devs.ai, which allow teams to safely create agentic solutions across different large language models. So, are your employees already innovating with AI while leadership lags behind? And what might your business discover if you stopped trying to control AI adoption and started observing where it's already thriving? Let me know your thoughts after listening.      

What if artificial intelligence could help you find, value, and buy an online business in minutes instead of months? That's the idea behind Lauren AI, Flippa's new proprietary deal origination tool designed to make mergers and acquisitions accessible for everyone, not just the elite few. In this conversation, Blake Hutchison, CEO of Flippa, returns to share how the company is using AI to democratize business ownership for what he calls “the 99%.” Blake explains how Lauren AI indexes over five million digital businesses weekly, analyzing everything from revenue models and tech stacks to traffic and growth potential. Using natural language prompts, it builds a hyper-specific buyer mandate and surfaces opportunities tailored to each individual's skills, goals, and budget. What used to cost thousands of dollars in retainer fees for M&A analysts can now be achieved with a few clicks and a $1.99 outreach. For Blake, it's about eliminating friction in the acquisition process while giving everyday entrepreneurs access to real, data-driven deal flow. The conversation also explores Flippa's new partnership with SeedLegals and the launch of “Flip and Raise.” This initiative helps UK founders perform a Delaware Flip, reincorporating their business in the US so they can raise capital directly from Flippa's network of 75,000 accredited investors. The result is a more connected global marketplace, where cross border deals are not just possible but encouraged. Across the episode, Blake reflects on his mission to lower the barriers to entrepreneurship, his belief in AI as an enabler of ownership, and the early examples of global deals made possible by the platform. He also shares a personal moment of gratitude for his wife, whose leap of faith from San Francisco to Melbourne mirrors the kind of courage he sees in founders every day. This episode captures the spirit of modern entrepreneurship, tech enabled, global, and deeply human. It's a story of how AI is quietly reshaping one of the most traditional areas of business, turning mergers and acquisitions into something anyone, anywhere, can take part in.

What happens when the ancient magic of theatre meets the disruptive energy of artificial intelligence? In this episode of The Tech Talks Daily Podcast, I sit down with Emmy-nominated producer and 440 Media founder Jeffery Keilholtz to unpack how AI is reshaping entertainment, licensing, and the very soul of live performance. From his time leading Broadway Licensing Global, home to thousands of acclaimed titles including Harry Potter and the Cursed Child, Jeffery brings a rare blend of creative and commercial insight to one of the most transformative moments in entertainment history. Our conversation explores how live entertainment faces twin challenges of visibility and scarcity in a digital age. As Jeffery explains, the rise of ChatGPT has changed how people search, discover, and decide what to see, forcing a shift from SEO to AEO, or Answer Engine Optimization. Yet even as AI floods the world with abundance, theatre's scarcity, that irreplaceable “live, local, urgent” energy, is becoming more precious than ever. Together we examine how AI can simultaneously empower and endanger creative industries, from copyright battles worth billions to the promise of smarter audience engagement and new paths to discover hidden works. Jeffery also shares his framework for balancing technology and artistry, urging creators to stay nimble like a blade of grass. He argues that surviving this era of AI-driven disruption requires humility, flexibility, and a renewed belief in human connection. It's a powerful reminder that the heartbeat of theatre, and perhaps of creativity itself, still belongs to people gathered together in the same room, sharing something that can never be replicated by a machine. How do you see AI reshaping the arts and entertainment world? Is it an existential threat to creativity or the tool that will help artists reach new heights? I'd love to hear your thoughts.      

What happens when classrooms become laboratories for artificial intelligence? As AI tools find their way into schools, from lesson planning to student assessments, educators and parents are wrestling with how to balance innovation and security. In this conversation, I sit down with Jurgita Lapienytė, Chief Editor at Cybernews, to unpack how AI adoption in education is reshaping learning, privacy, and the safety of our youngest digital citizens. Jurgita brings a rare dual perspective as both a technology journalist and a mother. We explore how AI's growing influence could improve access to knowledge while eroding fundamental cognitive skills if introduced too early or without balance. She compares today's reliance on AI to the way GPS changed navigation, convenient but potentially disorienting when overused.  Together, we look at how schools can encourage analog learning before turning to technological shortcuts and why teacher training is crucial for building true tech readiness. But beneath the excitement lies a darker reality. With 82 percent of K-12 schools hit by cyber incidents in the last 18 months, education is fast becoming one of the most targeted sectors. Jurgita explains how AI is supercharging attacks from phishing to deepfakes and why schools must view data protection as an essential part of innovation rather than an afterthought. We discuss the growing risks around student data, the ease with which even innocent photos can be exploited, and why privacy policies need a complete rethink before more AI tools enter the classroom. This episode isn't about rejecting technology, it's about using it responsibly. Jurgita's insights remind us that AI's value in education depends on how thoughtfully it's implemented and how prepared we are to protect the people it's meant to serve. So what does a secure classroom really look like in the age of AI, and how can schools, policymakers, and parents work together to create one? I'd love to hear your thoughts, how should we balance innovation with safety in our children's digital future?

What happens when simplicity meets AI on the world's biggest tech stage? In this episode, recorded live at GITEX Global in Dubai, I sit down with Sohaib Zaheer, Senior Vice President and General Manager at DigitalOcean, to talk about how the company is staying true to its founding vision of accessibility and simplicity while entering the age of AI. For years, DigitalOcean has been known as the cloud that “speaks the language of builders,” empowering developers and startups to innovate without unnecessary complexity. Now, with the launch of its Gradient AI platform and Cloudways Copilot, the company is bringing that same philosophy to AI development, helping teams go from idea to production-ready agents without huge DevOps teams or fragmented toolchains. Sohaib explains how DigitalOcean's unified stack is making AI agent development faster, easier, and more transparent. We discuss the startling statistic that 95% of AI projects never make it past the prototype stage, and explore how Gradient AI aims to change that through agent templates, debugging tools, and built-in guardrails. We also look under the hood at AI inferencing, GPU optimization, and why performance and cost efficiency still matter as much as cutting-edge innovation. If you have ever wondered how AI can become truly accessible, or how simplicity might just be the next big breakthrough, this conversation offers a grounded, real-world perspective from one of the most down-to-earth leaders in cloud technology. Recorded live on the show floor at GITEX Global, this episode is a reminder that great tech is not about hype, it is about helping people build, test, and create with confidence.

I'm taking you behind the scenes of GITEX Global with someone who lives and breathes the energy that makes this event what it is. Daniela Muente, Global Marketing Director at GNX, joins me to share how the world's largest technology showcase comes together, what drives its incredible growth, and why Dubai has become such a powerful crossroads for innovation. GITEX Global isn't just another tech conference. It's where conversations about AI, sustainability, smart cities, cybersecurity, and digital transformation collide with real-world solutions and human stories. With more than 6,800 companies and over 200,000 attendees from across the globe, Daniela explains how her team brings this massive ecosystem to life every year—curating an experience that connects startups, enterprises, governments, and everyday innovators under one roof. In this episode, Daniela reflects on how storytelling, community, and purpose shape the identity of GNX. We discuss how the event celebrates diversity in technology, why the Middle East is fast becoming a global tech hub, and what it takes to orchestrate an event that captures the imagination of the world.        

Deepfakes used to be a niche curiosity. Today they have become a sophisticated tool for manipulation, persuasion, and exploitation. In this episode of Tech Talks Daily, I sit down with Aleksander Gorkowienko, Head of Penetration Testing at Risk Crew, to examine how artificial intelligence has transformed deepfakes from playful face swaps into full-scale multimedia attacks designed to deceive even the most vigilant among us. Aleksander explains how we have entered the age of Deepfakes 2.0, where fake video, audio, images, and text merge to create hyper realistic digital experiences. These aren't the crude social media edits of a few years ago. They are now weaponized as tools for emotional manipulation, exploiting fear, urgency, and trust to trick victims into transferring money, sharing data, or compromising systems. Aleksander walks through real world examples of how criminals build these illusions, using stolen digital footprints to impersonate executives, family members, and trusted colleagues in live video calls. We discuss how AI's accessibility has accelerated this problem. With free tools and moderate computing power, almost anyone can now create a convincing fake offline. Aleksander shares how this ease of creation erodes trust online, making it harder to distinguish truth from fabrication. He also reveals how attackers rely less on technology itself and more on psychology, engineering scenarios that push people into acting before thinking. From a defense standpoint, Aleksander offers clear, actionable insights. He talks about the importance of multi factor verification, context based awareness, and fostering what he calls “streetwise vigilance” in the digital world. He compares it to walking through a city at night; you wouldn't flaunt your valuables, so why overshare online? We explore how organizations can conduct training and simulations to teach employees to pause, question, and verify before reacting. This episode is a timely warning for every business and individual operating in a world where reality can be faked in seconds. Aleksander's rule of thumb is simple but powerful: never trust a single source of information. Cross check, slow down, and think before you act. Because in the age of AI deception, trust must be earned every time. Listen now to hear Aleksander's firsthand perspective on how deepfakes are changing cybersecurity and what we can all do to stay one step ahead.

What if artificial intelligence could help end world hunger? In this special episode recorded live from GITEX Global in Dubai, I sit down with Magan Naidoo, Chief Data Officer at the United Nations World Food Programme, to discuss how data and AI are transforming humanitarian work at scale. Magan paints a powerful picture of the global food security crisis, where hundreds of millions of people face hunger across more than 80 countries. He explains how the World Food Programme is using technology to predict food shortages, optimise supply chains, and deliver aid faster and more effectively. Behind every algorithm sits a simple goal: getting food to those who need it, when they need it most. We explore how AI models are helping the organisation make sense of enormous datasets, identifying patterns that humans alone could not process quickly enough. From predicting drought-related crop failures to reducing the cost of food delivery through smarter routing, Magan reveals how data-driven decisions are saving both time and lives. He also shares the organisation's commitment to ethical AI, strong data governance, and privacy protection in every region they operate. As the only UN agency with a formal AI strategy, the World Food Programme is setting a benchmark for how large-scale institutions can use technology responsibly and effectively. Magan's story highlights the importance of trust, collaboration, and resilience in a mission where failure is not an option. Could AI truly be the key to solving one of humanity's oldest challenges? And what lessons can every organisation learn from how the World Food Programme blends compassion with computation? Tune in, then share your thoughts.

This week has reminded me why I love what I do. I have spoken with people from the US, China, Dubai, Bulgaria, and South Africa, and even discovered that one of this show's regular listeners had made the journey from the Netherlands to be here at GITEX Global. Over five sessions on the AI Stage, I have covered everything from autonomous cars to how AI could help the UN World Food Programme tackle hunger. We have explored how Serbia achieved tenfold growth through AI and how new tools can now verify misinformation simply by checking a video link. But beneath all the tech talk, what has stood out most to me is how technology connects people. In a world that often feels divided, it is refreshing to see how collaboration and shared curiosity can still bridge cultures and spark ideas. It is those moments, when technology brings people together, that remind me why this podcast exists. That spirit of connection is exactly what inspired my quick chat with Iancho Dimitrov from Living Homes, a company based in Dubai but shaped by global perspectives. Originally from Bulgaria, Iancho and his team are building what they call an “AI-native intelligent home,” a home that does not just automate switches but truly understands its inhabitants. From monitoring wellbeing and improving sleep to creating safe, supportive spaces for families across generations, Living Homes is redefining what it means to live smart. So while this conversation might be short, it captures something powerful. It is proof that innovation is not only about hardware or software, it is about empathy, understanding, and the shared drive to build a better way of living. In a week where the world gathered in one city to imagine the future, Iancho's vision is a reminder that technology works best when it feels human.

What if the next generation of computing was not something you held or wore, but something you looked through? In this special episode recorded live from GITEX Global in Dubai, I speak with Roman Axelrod, founder of EXPANCEO, a deep tech company creating AI-powered smart contact lenses designed to merge augmented reality, biosensing, and what he calls digital superpowers. Roman explains how his company moved from an ambitious idea to becoming the first deep tech unicorn in the Gulf region, now valued at more than 1.3 billion dollars. Over the past five years, his team of physicists and engineers in Dubai has built more than fifteen prototypes and secured a wide range of patents, all aimed at developing what they see as the ultimate interface for AI-driven computing. These lenses can display digital images, measure biological signals such as glucose and intraocular pressure, and may one day eliminate the need for screens altogether. He reflects on the early days of disbelief, when even friends told him to give up, and how perseverance became the deciding factor. For Roman, success meant proving that deep tech innovation is possible outside Silicon Valley. He shares how Dubai's ecosystem, low taxation, and access to world-class talent helped make that vision real. His story offers practical insight for founders who are told their ideas are impossible until they can show a working prototype. We also explore what this means for the future of human-computer interaction. Roman believes these lenses will help us communicate directly with intelligent systems, turning science fiction into everyday life. His message to entrepreneurs is simple: be stubborn, stay curious, and keep building. Could AI contact lenses redefine computing itself? Listen to the conversation and share your thoughts.

What if the real breakthrough in AI isn't the model itself, but the data that gives it knowledge? In this episode of Tech Talks Daily, I sit down with Edo Liberty, founder and Chief Scientist of Pinecone, to unpack how vector databases have quietly become the backbone of modern AI infrastructure. We explore why retrieval-augmented generation (RAG) works so effectively out of the box, and why fine-tuning large models often adds complexity without real-world value. Edo shares how Pinecone's research revealed that different models—from OpenAI to Anthropic—require differently structured context to perform well, a discovery that's reshaping how enterprises think about AI implementation. As the former Director of Research at Yahoo and AWS, Edo offers a grounded perspective on where the real innovation is happening. He explains how the shift from traditional data structures to vector representations is redefining how machines “know” and retrieve information, creating smarter, context-aware systems. We also touch on his recent transition to Chief Scientist, his excitement for returning to hands-on research, and why he believes the convergence of AI and data represents the defining technological shift of our lifetime. So, what does it mean for developers, business leaders, and anyone building with AI when knowledge becomes an accessible layer of infrastructure? Can we build systems that truly “know” as humans do? Join the conversation, and after listening, I'd love to hear your thoughts—do you think the future of AI lies in the models or in the data that feeds them?

What happens when an AI strategy meets the real-world complexity of healthcare, law, and finance? That's the challenge at the heart of my conversation with Mark Sherwood, CIO of Wolters Kluwer, a global leader in professional information services. With over three decades in technology leadership across Microsoft, Symantec, and Nuance, Mark brings a rare combination of enterprise depth and hands-on pragmatism to the AI discussion. Mark explains why cloud-native architecture and data governance are the twin foundations of trustworthy AI. He shares how Wolters Kluwer is embedding AI across highly regulated industries—from helping doctors access life-saving insights through natural language queries to giving tax and legal professionals faster, more accurate guidance on complex regulations. Behind the innovation lies a disciplined approach: governing data, managing risk, and building confidence in AI systems that must meet the highest standards of accuracy and compliance. We also explore how to build high-trust, low-friction partnerships between IT and business teams to prevent shadow IT while accelerating digital transformation. Mark offers candid insights into the rise of AI agents, the emerging risks of quantum security, and why he believes that high-quality data is the most valuable currency in digital transformation. His philosophy is simple: speed means nothing without trust, and trust starts with clean, well-governed data. From cloud transformation to the future of AI regulation, this episode offers a grounded look at how global enterprises can scale responsibly in an era where innovation often outruns policy. So as AI becomes inseparable from how professionals think and work, how do we balance speed with stewardship? And are we truly ready for the ethical, technical, and quantum frontiers ahead? Share your thoughts after the episode.

What does it take to build AI that enterprises can actually trust? That's the question I explored with Nirankush “Kush” Panchbhai, Senior Vice President of Platform Fundamentals at ServiceNow, in a conversation about AI governance, human-centered design, and how the company's AI Control Tower is reshaping enterprise adoption. Kush describes the AI Control Tower as an “air traffic controller” for AI agents, a central command center that provides visibility, accountability, and governance across every part of an organization's AI ecosystem. It embeds compliance, legal, and risk workflows directly into the development process, replacing endless approval cycles with automated guardrails that accelerate innovation rather than slow it down. The result is a system where humans remain firmly in control, supported by transparent, explainable AI that acts as a teammate rather than a tool. We also discuss how ServiceNow is helping enterprises move beyond the “POC palooza” of pilot projects that never scale. By treating AI agents as members of a digital workforce—with performance tracking, retraining, and measurable ROI—companies can finally connect investment to real outcomes. Governance, in this context, isn't a constraint; it's a catalyst for confidence and adoption. At its core, ServiceNow's philosophy is about taking the work out of work, not the human out of work. From password resets to process automation, AI is freeing employees to focus on creative, high-value problem-solving while building trust through transparency and accountability. As organizations begin managing both human and digital workforces, one question lingers: can AI governance truly become the accelerator that turns trust into enterprise-scale transformation? And what does it take to ensure AI always serves people, not the other way around? Share your thoughts after the episode.

What if the biggest sustainability challenge in tech isn't hardware or cloud emissions, but the invisible mountain of unused data sitting in storage? That's the question driving my conversation with Piero Gallucci, Vice President and General Manager for NetApp UK and Ireland, as we discuss how single-use data is quietly shaping the environmental and financial footprint of enterprise IT. Piero explains that 38 percent of stored data is never used again, yet it continues to consume energy and resources indefinitely. He describes how this digital hoarding—often driven by regulatory caution and the overvaluation of data—has become one of the most overlooked contributors to emissions in modern infrastructure. With the rise of AI accelerating data growth by an estimated 50 percent, the challenge is no longer simply about capacity but responsibility. Through examples such as Aston Martin Formula One and the NFL, Piero outlines how NetApp is helping organizations identify unused data, automate lifecycle policies, and design intelligent, energy-efficient infrastructure that supports both innovation and sustainability. We also explore the tension between AI adoption and environmental impact. As enterprises rush to train new models, Piero argues that smarter data governance, not bigger datasets, is the key to sustainable AI. He highlights the importance of educating teams on the true cost of data—both financial and environmental—and why leaders must build intentional strategies that align performance with purpose. NetApp's vision is clear: make data management as sustainable as it is powerful. But as AI reshapes how we store and use information, can the tech industry finally balance digital growth with environmental stewardship? And what would your company look like if every byte of data had to justify its existence? Share your thoughts after the episode.

What if the next big leap in business AI isn't generative at all, but predictive? That's the question at the heart of my conversation with Zohar Bronfman, CEO and co-founder of Pecan AI, a company helping business teams forecast outcomes with precision and turn historical data into future insights. Zohar explains why he believes predictive AI will deliver far greater enterprise value than the generative models dominating headlines. He points to research showing that most generative AI projects fail to produce ROI, while predictive systems built on a company's own data can directly improve revenue, reduce churn, and guide smarter decisions. With Pecan's no-code platform, marketing and operations teams can now create predictive models without needing data scientists—bridging the gap between technical expertise and business execution. Through stories like Little Spoon's, a direct-to-consumer baby food brand that used Pecan AI to identify and retain at-risk customers, Zohar illustrates how predictive analytics turns data into real business impact. He also shares common mistakes companies make when implementing AI—starting with unclear objectives and misaligned resources—and why success depends on defining the problem before choosing the tool. Looking ahead, Zohar envisions predictive AI as the backbone of every organization, shifting business intelligence from reactive analysis to proactive action. As companies move beyond dashboards and toward dynamic decision-making, predictive insights may soon become as fundamental as spreadsheets. So, if your company could anticipate every challenge before it happened, how different would your strategy look? And are business leaders finally ready to treat predictive AI as core infrastructure rather than a passing trend? Share your thoughts after the episode.

What happens when artificial intelligence meets the everyday heroes of local government? That's the question driving my conversation with Justin Dennis, co-founder and COO of Urban SDK, a geospatial AI company helping more than 250 North American cities make faster, safer, and smarter decisions. Justin shares how a Smart Cities Challenge from the U.S. Department of Transportation inspired him to co-found Urban SDK in 2018, and why he believes the future of public safety depends on replacing manual data collection with real-time intelligence. From traffic fatalities to hurricane recovery, he explains how the company's HALO platform gives local leaders and emergency responders the insights they need to act before crises escalate. In a single platform, they can identify dangerous road zones, predict high-risk intersections, coordinate clean-up operations, and rebuild infrastructure based on data rather than guesswork. We also explore how AI is quietly reshaping government operations, from disaster management to traffic enforcement. Justin discusses the challenges of introducing cutting-edge technology into systems that still rely on spreadsheets and siloed workflows. Yet his optimism is clear. He believes governments are beginning to embrace AI not as a buzzword but as a practical tool to save time, resources, and lives. As one Florida community recently reported a 40 percent drop in traffic fatalities, the impact is already measurable. Urban SDK's story is about technology meeting public service with purpose. So as we enter another year of rapid AI progress, how can data-driven insights continue to empower local leaders to protect citizens and improve quality of life? And what could your city achieve if every decision were powered by real-time intelligence? Share your thoughts after the episode.

Two years after our last conversation, Raj Koneru, CEO and Founder of Kore.ai, returns to discuss how the world of AI has changed and how much of it still needs to. When we first spoke, conversational AI was promising. Now it is powering over a billion interactions every day for companies like LG, Coca-Cola, and Blue Cross Blue Shield. Yet Raj argues that the next real breakthrough will not come from novelty, but from accessibility. In this episode, Raj explains why the future of AI depends on open collaboration rather than vendor lock-in. Kore.ai's partnerships with Microsoft, AWS, and G42's Inception in the UAE reflect a commitment to interoperability and shared innovation. He offers a rare look into what happens “above the line,” where enterprises actually design and deploy AI agents, compared to the massive “below the line” investments driving the hardware, cloud, and model layers of AI. For Raj, platforms like Kore.ai act as the bridge, translating technical potential into business outcomes. We also explore what true democratization of AI looks like in practice. Raj believes no-code platforms are key to giving both large and small businesses the power to build their own agents without deep technical skills. He discusses the challenges of scaling responsibly, managing latency, ensuring governance, and keeping AI secure and transparent. From the shift toward on-device AI in smartphones to the lessons learned from running one of the world's largest enterprise AI platforms, his perspective blends realism with optimism. This conversation is a reminder that progress in AI will not be defined by who owns the biggest model but by who makes the technology usable, ethical, and open to everyone. Raj's message is simple but powerful: read widely, question everything, and collaborate boldly.

In this episode, Mike Baker, Vice President and Global CISO at DXC Technology, says the cyber industry has been focusing on the wrong side of AI. He believes too many companies use it only to block threats instead of studying how criminals use it to scale phishing, bypass defenses, and deploy adaptive malware. Attackers are learning faster than ever, and security teams must catch up. Mike argues that defenders need to think differently and use AI as both protection and opportunity. He shares how DXC is already doing this. The company has brought autonomous AI agents into its security operations through a partnership with 7AI. These agents process alerts that used to require hours of human effort. The result is faster detection, less burnout, and more time for analysts to investigate real threats. By cutting manual work by more than eighty percent, DXC has shown how AI can make cybersecurity teams stronger, not smaller. Zero Trust remains a core part of DXC's strategy. Mike calls it a journey that never ends. It needs cultural change, constant learning, and leadership that keeps security invisible to end users. AI now plays a role here too, improving identity checks and spotting access issues in real time. Yet, he reminds us, AI still needs people in the loop for oversight and judgment. We also talk about supply chain risks. Too many companies still treat risk assessments as one-time tasks. Mike pushes for continuous monitoring and close collaboration with suppliers. He closes the conversation on a hopeful note. AI will not replace people in cybersecurity, he says. It will make their work more meaningful and more effective if used with care and common sense.

What happens when the future of teamwork collides with the power of AI? That's the question at the heart of this episode as Tiffany from Atlassian joins me from Barcelona during Team 25, where Atlassian is showcasing how AI-powered collaboration is redefining how work gets done. We talk about how Atlassian's mission to unleash the potential of every team is coming to life through its bold decisions, from sunsetting data center products to expanding its multi-cloud partnerships with Google. Tiffany offers a front-row view of how Atlassian's evolving cloud platform is designed to help customers work smarter while enabling secure, scalable innovation across some of the world's most complex enterprises. The conversation also uncovers the thinking behind the teamwork graph, Atlassian's powerful data intelligence layer that connects billions of work objects to create truly personalized AI experiences. Tiffany shares how companies like Royal Caribbean and Mercedes-Benz are already seeing measurable performance gains and how AI is becoming a real teammate that unifies knowledge, connects tools, and drives better outcomes. We discuss what it means to build a “system of work,” why flexibility and context matter, and how Atlassian's open approach allows teams to build custom systems tailored to their own culture and workflows. Beyond the technology, this is a story about continuous learning, adaptability, and human-centered progress. Tiffany's reflections on learning from the toughest customers, embracing change, and reimagining the browser as an active workspace reveal how Atlassian is blending AI with empathy and purpose. As AI becomes inseparable from teamwork, what steps will you take to unleash what your team can do next? I'd love to hear your thoughts.

As AI tools race into every corner of software development, a simple question keeps coming back to me. Will AI replace human testers, or will it force us to rethink what great testing looks like in the first place. In today's conversation, I talk with Santiago Komadina Geffroy, a Software Engineer at Jalasoft and an educator with Jala University, about what changes, what stays, and what teams should do next. Santiago shares how his day job and teaching intersect. He points to a gap he sees often. Engineers are experimenting with large language models without fully understanding how they work, which leads to overconfidence and avoidable rework. He argues for clearer interaction patterns between tools and people. Think less about magic prompts and more about protocols, context sharing, and agent to agent collaboration. That shift frees testers to do the thinking work that AI still struggles with, from exploratory testing and usability judgment to spotting the weird edge cases that only show up when real humans use real products. We also get into bias and ethics. AI is only as fair as the data it learns from, and that matters in healthcare, finance, and hiring where a mistake can carry life changing consequences. Santiago calls for stronger education around data quality, authorship, privacy, and environmental impact, not as a side note but as part of how engineers are trained. He believes governance helps teams move faster with fewer regrets when they take AI into production. Security sits in the mix too. Many AI tools need deep system access. If compromised, they can distort results or leak sensitive information. Santiago is candid about the limits of any single safeguard. He recommends a culture of shared responsibility where engineers understand when to call in security specialists and how to design workflows that keep humans in the loop for consequential decisions. We close with what Jalasoft has learned from building with AI inside a nearshore model in South America. More thinking time. Smaller, controllable scopes. Clear lines between routine automation and human judgment. The headline is simple. AI will change testing. Human testers will remain at the heart of quality.

Here's the thing. Most of us still picture a hotel lobby with a counter, a queue, and someone typing furiously while we wait after a long flight. In this episode, I sit with Richard Valtr, founder of Mews, to ask whether that scene is quietly fading. Backed by Tiger Global, Goldman Sachs, and Battery Ventures, Mews recently raised 75 million dollars to scale an AI-powered platform that already processes more than 10 billion dollars in payments each year. Richard argues the real bottleneck in hospitality isn't software. It's mindset. If hotels rethought workflows around guests rather than systems, the front desk would feel less like a checkpoint and more like a welcome. Richard shares the origin story of building for hoteliers as well as guests, and why the property management system should function like a central nervous system. He explains how automation handles the repetitive pieces of check-in so staff can actually look people in the eye and start a conversation. That's the promise of AI here. Not gimmicks, but orchestration across bookings, payments, inventory, and service so the boring parts disappear into the background and the human parts come forward. We also talk about underused tech. Richard uses a memorable comparison for many hotel platforms that have Ferrari-level capability but get driven like Volvos. The data is there. The intent to serve is there. What's missing is the leadership confidence to rewire the stack, measure outcomes, and keep pushing. When that happens, hotels stop thinking only in terms of rooms and start monetizing the full journey. Daybeds, coworking passes, last-mile upgrades, spa time after back-to-back meetings. AI can surface the right offer at the right moment without turning the experience into a sales pitch. By the end, Richard paints a picture of hospitality where screens fade, transactions happen on the guest's time, and every interaction feels more personal precisely because the admin has been taken out of the way. If you want a grounded view of how AI will change hotels without stripping away the reason we love staying in them, this conversation is a helpful place to start.

When a company quietly builds world-class storage and virtualization software for twenty years, it usually means they have been too busy solving real problems to shout about it. That is what makes euroNAS and its founder, Tvrtko Fritz, such an interesting story. In this episode, I reconnect with Tvrtko after meeting him on the IT Press Tour in Amsterdam to learn how his company evolved from “NAS for the masses” into a trusted enterprise alternative in a market filled with bigger names. Tvrtko shares how euroNAS began with a simple idea that administrators should not have to battle complex infrastructure to keep systems running. Over time, that belief shaped a complete platform covering hyper-converged virtualization, Ceph-based storage, and instant backup and recovery. He recalls the story of a dentist who lost a full day of work waiting for a slow restore, which inspired euroNAS to create instant recovery that restores in seconds rather than hours. We also discuss how their intuitive graphical interface has turned Ceph from a daunting project that once took a week to set up into something that can be configured in twenty minutes. That change has opened advanced storage to universities, managed service providers, and enterprises handling petabyte-scale workloads. We also tackle a topic that many in IT are thinking about right now: VMware. With licensing changes frustrating customers, Tvrtko explains how euroNAS has become the quiet plan B for many organizations seeking stability and control. Its perpetual per-node licensing model removes the pressure of forced subscriptions, while tools such as the VM import wizard make migration faster and less painful. What stands out most is that Tvrtko still takes part in customer support himself, using real conversations to guide product development and keep the company close to the people who depend on it. Looking ahead, Tvrtko outlines how euroNAS is growing through partnerships with major hardware vendors and through its expanding role in AI infrastructure, where demand for scalable storage continues to rise. The conversation highlights the value of engineering-led companies that build with care, focus on reliability, and give customers genuine ownership of their systems. If you want to understand what practical innovation looks like in enterprise storage, this episode will remind you why simplicity still wins.

AI hype has been loud for three years, but most leaders still tell me the real work begins after the demo. That was the starting point for my conversation with Christina Ellwood, co-founder of AI Realized, a community built to help enterprises move from pilots to production with less noise and more results.  Christina has a calm, practical way of explaining why progress has accelerated from a tiny fraction of companies in production to roughly one in five this year, and why many of the remaining blockers have little to do with model choice and everything to do with people, policy, and permission to ship. We talk about the messy middle between a proof of concept and a live service that customers can rely on. According to Christina, the most complex problems are organizational. Teams need upskilling, guardrails, and clear deployment guidelines to ensure effective execution. Legal and brand risk create hesitation. Boards want more substantial evidence and better controls. That is where leadership shows up in a very human way.  The skill she hears most often from successful program leads is humility. No one knows everything here, and the leaders who admit that, invite challenge, and keep learning are the ones getting to value without creating chaos. I loved her point that cross-organisational leadership is fast becoming the hidden superpower as AI connects systems and workflows that used to sit in separate silos. We also look forward to the 2025 AI Realized Summit, scheduled for November 5 in San Francisco. Attendance is intentionally capped at 500 to maintain high-quality conversation and genuine networking. Expect Fortune 2000 use cases across multiple industries, a healthy mix of predictive and generative work, and practical talk on small language models, multi-model strategies, and running models inside your security perimeter.  Eric Siegel will keynote on combining predictive analytics with generative techniques, and you will hear from executives at companies including Amazon, Audible, Red Hat, and Zscaler. Christina highlights one example from Fandom that combines predictive ad targeting with generative tools to enhance brand safety and suitability, a trend I expect to see repeated throughout the day. If you are leading AI programs and need fewer slogans and more proof, this episode will feel like a deep breath. We explore how to move faster while staying responsible, why smaller and multi-model setups are gaining traction, and how to build confidence with your board without overpromising.

Finance leaders know the struggle of managing endless spreadsheets, juggling data from every corner of the business, and trying to plan for a world that changes by the hour. In this episode, I talk with Julio Martínez, Co-Founder and CEO of Abacum, about how his team is helping finance professionals move from reactive reporting to confident, real-time decision making. Abacum was recently named the fastest growing tech company in Spain by Deloitte after increasing revenue by 6,733 percent in just four years. Julio shares the story behind that growth and explains how finance teams are transforming from back-office operators into true strategic partners. He describes how Abacum's platform helps CFOs and FP&A teams create accurate forecasts, automate manual work, and build scenario models that answer “what if” questions in minutes instead of days. We also talk about the role of AI in finance and why current large language models are not yet reliable enough for quantitative use cases. Julio discusses the need for precision, the importance of a human in the loop, and how new hybrid approaches are shaping the future of financial planning. From Barcelona to New York, his journey reflects the global rise of data-driven finance and the growing strength of Spain's startup ecosystem. Julio also leaves listeners with a thoughtful recommendation, Meditations by Marcus Aurelius, a book that continues to inspire him to stay grounded amid rapid change. If you want to understand how technology is redefining financial planning and how strong foundations can fuel extraordinary growth, this conversation with Julio offers a rare look inside the engine of one of Europe's fastest-rising tech companies.

What happens when a CTO and a CIO of a global tech company sit down together to talk about AI? That's the starting point of today's episode, where I'm joined by Jeremy Ung, CTO at Blackline, and Sumit Johar, the company's CIO. Rather than chasing the hype, we focus on what AI really means for executive decision making, governance, and business outcomes. Both leaders open up about how their partnership is blurring the traditional lines between product and IT, and why the board is demanding answers on topics that once sat deep in the technology stack. Jeremy and Sumit explain why AI is not just another SaaS subscription and why expectations have changed so dramatically. For decades, technology was seen as predictable, a rules-based engine that followed instructions without error. AI feels different because it speaks, reasons, and sometimes makes mistakes. That human-like experience is what excites employees, but it is also what unsettles them. This is where education and governance come in, helping teams learn how to question, verify, and trace AI outputs before they make critical decisions. We also explore how AI agents are beginning to work across tools like SharePoint and email, raising new compliance and security questions that CIOs and CTOs must answer together. The conversation turns to AI sprawl, a problem that mirrors the SaaS explosion of a decade ago. With new AI tools emerging every week, enterprises risk overlapping investments and fragmented initiatives. Sumit shares how Blackline uses two governance councils to keep projects aligned. One is dedicated to risk, pulling in voices from legal, security, and privacy. The other is focused on transformation, evaluating whether requests for new AI capabilities make sense, or whether they duplicate what already exists. The signal that sprawl is taking root, he says, is when requests for tools suddenly jump from a few each month to a dozen. We also tackle the build versus buy dilemma. Budgets haven't magically increased just because AI is hot. Jeremy argues that building only makes sense when it reinforces a company's core advantage. Everything else should be bought, integrated, and kept flexible so that organizations can pivot as the AI landscape changes. Both leaders stress that trust, auditability, and value delivery must sit at the center of every investment decision.  

Zeta Global's CTO, Chris Monberg talks about building AI that helps brands grow with repeatable, scalable programs without losing the spark that makes a brand feel human. Zeta's promise is simple to say and hard to do. Help marketers deliver better results with less waste by pairing strong data, clear identity, and practical AI inside the Zeta Marketing Platform. What stood out first was Chris's view of design as a contact sport. He hires builders who live in the work, and he still enjoys rolling up his sleeves himself. That mindset shows up in how Zeta approaches AI for marketing. Rather than shouting for the next click, he wants systems that perceive intent and context. He described an early lesson from retail floors in Seattle. The best experience came from people who noticed a customer's posture and pace before speaking.  Empathetic design translates that awareness into algorithms that understand latent signals and respond with care, not noise. We also dug into a tension many leaders feel. Automation is exciting, but nobody wants generic content. Chris answered with a practical frame. Give marketers a way to create a personal “super agent” that learns from their choices, their brand voice, and the paths they take through the platform. Offload the repetitive chores, keep creative control, and grow pride of ownership. That pride matters because it breeds adoption. When teams feel the system reflects them, they keep using it and keep improving it. Another thread was trust. In Chris's words, the market still underestimates what these tools can do, partly because users are unsure where the value comes from. Zeta is leaning into transparency so teams can see how decisions are made and how results tie back to their inputs. Data and identity are the moat, but privacy and compliance are the foundation. He was candid about the weekly grind of meeting new regulatory needs region by region. That operational discipline shapes how Zeta decides to build, buy, or partner. Acquisitions must make sense on day one and integrate fast, with people as the primary asset. Chris also spoke directly to younger builders who feel stuck. There are no shortcuts. The only way through is work, curiosity, and a willingness to learn in public. He sees small teams pushing new protocols and patterns forward, and he wants more marketers and technologists to join that frontier with clear eyes and a bias for doing. We closed on culture. Zeta Live in New York brings sports and tech onto the same stage, and there is a reason. When the wider world pays attention, ideas travel further. If you care about marketing that respects customers and still moves the needle, this episode will give you a practical blueprint. It is about AI that makes room for people, systems that earn trust, and a product leader who still enjoys getting a little grease under his nails.

I invited Michael Reitblat, CEO and founder of Forter, to unpack a reality many retailers are living with every day. Fraud is no longer a side issue. It shapes conversion rates, customer loyalty, and the bottom line. Michael argues that if you remove the fear of fraud, you unlock growth. That sounds bold, but his lens is practical. Replace guesswork with instant, consistent decisions and you improve both security and the checkout experience. Here's the thing. False declines feel like fraud in disguise. When good customers get blocked, they do not return. Michael explains how Forter uses real-time signals to say yes or no within the transaction, without adding friction. The promise is simple. If a buyer is genuine, let them through. If it is fraud, stop it and cover the chargeback. It is a clean model that puts accountability on the platform, not the merchant. We also talk about what happens when AI agents start buying on our behalf. If software is placing orders, refunding items, or filing disputes, identity and intent become fluid. Michael walks through how trust platforms need to reason about behavior across accounts, devices, and sessions. The goal is confidence at the moment of purchase without slowing anyone down. Michael shares how Forter's scope has expanded from blocking bad actors to enabling smart, business-wide decisions about customers. That means recognizing loyal buyers even if they shop across regions and brands, and spotting synthetic identities that mimic human patterns. It also means measuring success by approvals and lifetime value, not only by stopped attacks. Let me explain why this matters. Retailers are caught between two pains. Ease up and you invite chargebacks. Tighten controls and you lose revenue from good customers. Michael's point is that trust should be a growth lever. If the system is confident, the checkout stays smooth on web and mobile. If the system is unsure, it can ask for the least painful extra step rather than send a blanket decline. We close with practical guidance for leaders. Treat trust as a product. Give teams shared visibility into decisions. Align incentives so fraud, payments, product, and marketing are working from the same truth. Michael's vision is a world where anyone can transact with ease because fraud has been priced out of the experience. That is a conversation worth having, and one retailers can act on today.

Here's the thing. “Smart” has been the buzzword for years, but Richard Leurig argues we're on the cusp of something bolder. In our conversation, the Accruent president drew a clear line between buildings filled with connected systems and buildings that can sense, decide, and act without a person staring at a dashboard all day. Richard shared a retail story that sticks. By wiring refrigeration units with sensors and training models on billions of telemetry points, his team can spot failures 48 to 72 hours before lettuce wilts or milk spoils. That time window turns panic calls at 3 a.m. into planned daytime fixes. It cuts waste, protects revenue, and keeps customers from walking into empty shelves. The bigger idea is a shift from many panes of glass to no pane of glass. Instead of asking people to wrangle alerts, AI agents coordinate HVAC, security, and maintenance, then dispatch the right technician with the right part only when one is truly needed. That is the road to self-healing facilities. Practicalities that matter now Let me explain why this resonates across industries. Whether you run a hospital, a university, a factory, or a grocery chain, you're wrestling with aging infrastructure and short supply of skilled workers. Richard sees the same pattern everywhere. Teams need guidance at the point of work, not another report. Natural language agents that answer plain questions and walk users through a task are winning hearts because they remove friction. Return-to-office adds another layer. Hybrid work has made space usage lumpy. Richard outlined how linking lease data, occupancy, and booking behavior helps leaders decide what to close, reshape, or scale. It also changes floor plans. When people do come in, they want project rooms and collaboration zones, not endless rows of cubicles. Retrofit is the sleeper story. You don't need a skyline of brand-new towers to get smarter. Low-cost sensors and targeted integrations are making older buildings more responsive than most people expect. That opens the door for progress without nine-figure capex. Energy, sustainability, and proof Boards want less energy spend and real emissions progress. The quickest wins are often hiding in plain sight. Richard walked through HVAC control that follows people, sunlight, and weather rather than fixed schedules. Lights that turn off when a room is empty are yesterday's news. Cooling only where teams are actually working is today's play. He also flagged a coming wave on factory floors. Many legacy motors and line components quietly draw more power than they should. Clip-on sensors can spot out-of-tolerance behavior so maintenance can fix the energy hog instead of replacing an entire line. That is the kind of operational change that lowers bills and supports sustainability targets with data, not slogans. Richard's timeline is refreshingly near term. He believes a large slice of the built environment will show real autonomy in three to five years. Not theory. Not demos. Everyday operations that quietly handle themselves until a human is truly required. If this conversation sparks an idea for your sites, stores, labs, or campuses, I want to hear how you're approaching it. What feels possible this quarter, and what still feels out of reach?

What if the biggest weakness in cybersecurity isn't a missing tool, but a cultural blind spot? That's the perspective of Dan Jones, Senior Security Advisor at Tanium, who joined me on Tech Talks Daily to share why he believes cybersecurity is fundamentally a people problem dressed up as a technology problem. Dan brings nearly three decades of experience in cyber operations, including leading cyber defence strategy for the UK Ministry of Defence. His career has shown him that technology alone doesn't secure organisations—it's the people at the front line, their leadership, and their ability to make the right decisions under pressure. He argues that while new tools flood the market every year, the make-or-break factor remains the same: how teams are led, supported, and empowered. In our conversation, Dan explains why leadership is often the overlooked part of cybersecurity, how culture shapes security outcomes, and why automation should be embraced not as a threat to jobs but as a way to give people time back for higher-value decision making. He shares examples from both military and enterprise contexts, showing how organisations succeed or fail based not on what tools they buy, but on how well they bring their people along for the journey. We also dig into one of today's hottest debates: the role of AI in cybersecurity. While many fear AI will displace jobs, Dan insists those fears are rooted in culture, not reality. He draws parallels to past industrial shifts, making the case that automation and orchestration are stepping stones that prepare teams for an AI-powered future—one where human judgment still sits firmly at the centre. This is a timely reminder for every leader and practitioner that cybersecurity is about more than firewalls and code. It's about trust, training, and people working together with the right tools at the right time. And yes, it's also about taking five minutes to brew a proper cup of tea—a lesson Dan believes says a lot about leadership and reflection. If you've ever wondered whether your organisation is focusing too much on tools and not enough on culture, this episode will make you stop and think. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

Some interviews stick because they take a noisy topic and bring it back to reality. This was one of them. I spoke with Erin Gajdalo, CEO of Pluralsight, about what it actually takes to upskill a workforce in an AI era that seems to change by the week. We compared boardroom intent with day-to-day practice, and Erin was refreshingly clear about both. Pluralsight began more than twenty years ago in classrooms, moved online as the market shifted, and now supports Fortune 500 teams with expert-led courses, hands-on labs, and the admin tools leaders need to measure progress at scale. The thread running through the whole story is simple: people learn by doing, and companies get value when that learning maps to real work. We talked about AI in her own workflow first. Erin uses it to draft presentations, crunch data, and speed up research, then pushes that mindset across the company through focused sprints where every department experiments and reports back. That culture piece matters. Pluralsight's latest research found that 61 percent of respondents still think using generative AI is “lazy,” which drives employees to adopt tools in the shadows and exposes the business to avoidable risk. Her answer is clear guidance, safe environments to practice, and permission to test without fear of failure. The payoff shows up in real examples. One financial services firm raised prompt engineering efficiency by 20 percent and saved 1,600 hours in three months by pairing assessments with prescriptive learning paths and hands-on practice. We also explored the fear that keeps people quiet. Layoff headlines travel faster than case studies, and that skews the mood inside many teams. Erin makes a straightforward case. Treat AI as an assistant that improves standard and repetitive tasks, protect the business with clear policies, then invest in education for everyone, not only engineers. Close the confidence gap with data. Baseline skills, prescribe learning, measure proficiency, and tie improvements to actual tasks. When leaders show their own work and give teams room to try things, adoption follows. The conversation finished on the future. Technical skills will keep evolving, but the standout advantage will be a willingness to learn and the soft skills that carry ideas from prototype to production. Erin also shared a personal goal that resonated with me. She would love a private breakfast with Serena Williams to talk about Serena Ventures and backing founders from underrepresented groups. It fit the theme of the episode. Talent is everywhere. Opportunity appears when someone opens a door and stays long enough to help you through it. If you want the full story, including how Pluralsight is updating its platform for scale and how leaders can reduce “shadow AI” without slowing innovation, you can find their research and resources at Pluralsight.com.  ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA    

Here's the thing. We have had brilliant ideas in Web3 for years, along with better tooling and plenty of enthusiasm, yet adoption still feels slower than it should be. In my conversation with Maciej Baj, founder of t3rn, we got under the skin of why that is and what it might take to change the pace. His starting point is simple to state and hard to deliver at scale: make cross-chain interactions feel seamless for users and predictable for developers. If you can do that, the door opens to practical products rather than experiments that only the bravest try. Maciej describes t3rn as a universal execution layer for cross-chain smart contracts, and the phrase matters because it changes how we think about interoperability. Instead of stitching together a mess of bridges and oracles, t3rn lets a contract access state and data across multiple chains from one place. Today it is mapped to the EVM for broad compatibility, but the design is chain agnostic by intent. That choice is less about tribal loyalties and more about meeting developers where they already build while keeping the door open to other ecosystems as the market evolves. Trust shows up in the details, and atomic execution is one of those details that changes behavior. If a multi-chain transaction cannot complete in full, it reverts. No half-finished transfers. No manual recovery adventures. This mirrors what smart contracts already offer on a single chain, which means developers can reason about outcomes without inventing fresh playbooks for every hop. It also reassures users, who care less about the plumbing and more about knowing that funds either arrive or return. Cost matters too. t3rn has been engineered for cost-efficient token movement across chains, which sounds mundane until you price a complex strategy that touches multiple venues. Lower friction makes new use cases economical. Maciej outlined a few that caught my eye. Trading algorithms that read and act on signals from multiple chains without duct tape. Simpler asset movement across ecosystems that do not share a wallet culture or UX conventions. Agent-driven executors that can watch for arbitrage or rebalance a portfolio without constant human oversight. The theme is the same throughout. Reduce the number of hoops and you increase the number of people willing to try something new. We also looked ahead. t3rn is preparing an integration with hyperliquid and rolling out a builder program to widen the ecosystem on top of its execution layer. An SDK is on the way so the community can help bring in new chains faster, rather than waiting for a core team to do all the heavy lifting. There is a governance track forming as well, aimed at giving the community more say in integrations and priorities. None of this guarantees success, but it signals a path from protocol to platform. I left the conversation with a clearer view of why interoperability still matters in 2025. The multi-chain world is not going away. Users move between ecosystems. Developers deploy to several environments at once. Liquidity, identity, and logic already live in many places. A universal execution layer that is reliable, cost aware, and easy to build on is the kind of boring-sounding foundation that ends up changing behavior. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

When we think about what separates winning traders from those who struggle, we usually picture strategies, indicators, or a bit of insider know-how. But what if the biggest edge has been sitting on your desk all along? In this episode, I sit down with Eddie Z, also known as Russ Hazelcorn, the founder of EZ Trading Computers and EZBreakouts. With more than 37 years of experience as a trader, stockbroker, technologist, and educator, Eddie has built his career around one mission: helping traders cut through noise, avoid expensive mistakes, and get the tools they need to stay competitive in a fast-moving market. Eddie breaks down the specs that actually matter when building a trading setup, from RAM to CPUs to data feeds, and exposes which so-called “upgrades” are nothing more than overpriced fluff. We also dig into the rise of AI-powered trading platforms and bots, and what traders can do today to prepare their machines for the next wave. As Eddie points out, a lagging system or a missed feed isn't just an inconvenience—it can be the difference between a profitable trade and a costly loss. Beyond the hardware, we explore the broader picture. Rising tariffs and global supply chain disruptions are already reshaping the way traders access technology, and Eddie shares practical steps to avoid being caught short. He also explains why many experienced traders overlook their machines as a “secret weapon” and how quick, targeted fixes can transform reliability and performance in under an hour. This conversation goes deeper than specs and gadgets. Eddie opens up about the philosophy behind the EZ-Factor, his unique approach that blends decades of Wall Street expertise with cutting-edge technology to simplify trading and help people succeed. We talk about his ventures, including EZ Trading Computers, trusted by over 12,000 traders, and EZBreakouts, which delivers actionable daily and weekly picks backed by years of experience. For traders looking to level up—whether you're just starting out or managing multiple screens in a professional setting—this episode is packed with insights that can help you sharpen your edge. Eddie's perspective is clear: the right machine, the right mindset, and the right knowledge can make trading not only more profitable, but, as he likes to put it, as “EZ” as possible. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

Most conversations about AI are still caught up in the spectacle. We see demos, marvel at copilots, and argue about the latest big model. But what happens when you strip away the hype and focus on AI that simply works? That is exactly the perspective Olga Lagunova brings to this episode. As Chief Product and Technology Officer at GoTo, she has one goal in mind: make AI useful, practical, and almost invisible. Olga believes the real test of AI is whether it integrates seamlessly into workflows. In her view, the most powerful AI is the kind that feels almost boring because it is just part of how work gets done. During our conversation she explains how GoTo is embedding AI into its platform so that small and midsize businesses can benefit without needing data scientists on staff or large budgets to experiment. We explore the difference between AI for SMBs and AI for enterprises, and why simplicity and trust matter more than shiny features. Our discussion also goes deeper into agentic AI, where tools are no longer just assistants but are taking on tasks in the background. Olga highlights how GoTo balances this shift with guardrails, governance, and human-in-the-loop oversight to ensure that efficiency never comes at the cost of security. We also unpack the classic build versus buy dilemma, why shadow AI is becoming a real risk for companies, and how leaders can measure ROI in a way that proves value both immediately and over time. If you are tired of the hype and want to understand how AI is quietly reshaping the backbone of business operations, this episode with Olga Lagunova will give you a grounded and forward-looking perspective.

I wanted this conversation to do two things at once. First, ground the hype in real practice. Second, show how a small country can punch well above its weight by connecting industry, academia, and government with purpose. With Chantelle Kiernan from IDA Ireland and Stephen Flannagan from Eli Lilly and Company, we explored what digital transformation really looks like on the factory floor in Ireland, why talent is the engine behind it, and how cross-sector collaboration is turning ideas into measurable outcomes. Ireland's manufacturing base employs hundreds of thousands and fuels exports, yet what stands out is the shared mindset. The shift toward Industry 5.0 puts people at the center while using digital, disruptive, and sustainable technologies to rethink production. Eli Lilly's experience shows how a digital-first culture changes everything. New sites start paperless by default. Established plants raise their game through micro-learning, data-driven problem solving, and champions who model the behavior. The message is simple. Technology only sticks when people see clear value and have the skills to act on it. From pilots to site-wide change Here's the thing. The strongest wins come from a strategic, site-wide approach rather than isolated pilots. Maturity assessments across pharma sites in Ireland revealed common patterns, shared bottlenecks, and repeatable opportunities. That insight helps teams justify investment, sharpen ROI arguments, and accelerate adoption without slowing production. Reinvestment in legacy facilities becomes a long-term advantage when you connect equipment, data, and people with a clear plan. This is where Ireland's ecosystem shows its class. Purpose-built centers like Digital Manufacturing Ireland, NIBRT, IMR, and I-FORM give teams a place to test before they invest. Indigenous tech SMEs sit at the same table as global pharma leaders and large tech firms, which means collaboration moves faster. When 50 percent or more of new R&D projects cite academic partnerships, you know something healthy is happening. Skills, STEM, and the mindset shift Upskilling came through as the decisive enabler. IDA Ireland supports companies with skills needs analysis and access to training. Universities co-create relevant courses. Micro-credentials and immersive apprenticeships build confidence on the shop floor. Stephen's point about micro-learning hit home. People learn best when they can apply knowledge to a problem they care about, right now. That keeps momentum high and spreads digital competence across teams without waiting on giant projects. Barriers still exist. Defining ROI, coping with regulatory complexity, and balancing change with daily production are real challenges. Culture is the swing factor. Leaders who set the tone, create space for experiments, and reward progress see faster results. GenAI is already shifting attitudes by improving personal productivity, which naturally opens minds to operational use cases like predictive maintenance, knowledge capture, and quality improvements. What comes next If the last decade was about connecting machines, the next decade will be about connecting knowledge. Expect smarter, greener, and more multidisciplinary manufacturing. AI will sit alongside advanced materials and sustainable design. The most resilient sites will combine agile infrastructure with strong learning cultures, so they can absorb change rather than resist it. Ireland's model of collaboration gives a useful signal. When industry, government, and academia align around shared outcomes, the runway gets longer and the takeoff gets smoother. This episode is about the practical choices that make transformation real. Strategic assessments. Shared R&D spaces. Cohorts of digital champions. And a relentless commitment to skills. It is a story of steady progress that scales, and a reminder that the future belongs to teams who can learn faster together. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

What does it really mean to future-proof financial data? That's the question at the heart of my conversation with George Rosenberger, General Manager of NYFIX at Broadridge. George has spent his career moving through every corner of the capital markets, from trading desks to broker-dealers, and now into the software side where he oversees order routing, post-trade matching, and the adoption of new AI tools. His perspective is uniquely positioned between the history of financial markets and their rapidly accelerating future. This discussion takes inspiration from Broadridge's fifth annual Digital Transformation and Next Gen Technology study, which collected insights from more than five hundred technology and operations leaders across financial services. The survey highlights both the progress and the pressure points facing the industry. Forty-one percent of leaders still cite data security as a major hurdle, and while cloud, AI, and cybersecurity dominate the technology stack, a third of firms still lack security built into their core systems. George explains why this gap persists, how legacy platforms complicate modernization, and what steps firms can take to extract value from old infrastructure while preparing for what's next. We also explore the irony that many organizations overestimate their digital maturity. Generative AI adoption has surged from forty to seventy-two percent in a year, but governance, compliance, and data quality concerns remain. George stresses the importance of measuring outcomes, not just intentions, and shares how Broadridge is approaching AI responsibly through initiatives like its Algo Copilot, which helps traders make sharper decisions. If you're curious about how financial services can strengthen cybersecurity, reduce technical debt, and rethink data strategy as a true engine of innovation, this episode offers both a candid reality check and a roadmap. The speed of change is staggering, but with the right strategy, leaders can build resilience and stay ahead in a digital-first world.

What does it take to deliver personalized financial guidance to more than 140 million people every single day? That is the question I put to Wan Agus, Head of Engineering at Intuit Credit Karma, in this episode of Tech Talks Daily. Most of us open the Credit Karma app to check our credit score, look at a loan option, or browse for a better credit card. What we rarely consider is the technology running behind the curtain. Wan revealed that his teams are powering more than 60 billion daily AI predictions to understand members' needs, protect their privacy, and guide them toward the right financial choices. He explained why accuracy is everything in fintech. A misplaced recommendation can mean more than a poor customer experience; it can damage someone's credit score and hold back their progress. Our conversation also looked at what happened after Intuit acquired Credit Karma. Two very different tech stacks had to be brought together, and identity systems had to be unified so members could move seamlessly between Credit Karma and products like TurboTax. Wan compared the process to playing two complex board games at once, where success depends on strategy and collaboration. We also explored how Credit Karma is blending traditional AI with generative AI. From early chatbot experiments to today's Wallet Analyzer and Tax Advisor, Wan shared how his teams decide when to push forward with new tools and when to slow down to ensure safety and trust. He also gave us a glimpse into the future, where agent-to-agent technology could bring open banking-style transparency to the U.S. So how do you scale personalization without losing trust? And what can every business leader learn from Credit Karma's balance between speed, culture, and responsibility? I would love to hear your thoughts after listening.