Tech Talks Daily

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.

Agentic AI is only interesting when it leaves the lab and takes responsibility for real outcomes. In this episode, I reconnect with Raj Koneru, Founder and CEO of Kore.ai, to talk about what that shift looks like inside large enterprises. Raj has been building conversational systems long before chatbots became dinner table conversation, and he is clear about where the action is now. Understanding intent is table stakes. The next frontier is planning, reasoning, and executing tasks through agents that can work across departments, respect policies, and prove their value in minutes, not months. We walk through how Kore.ai frames the stack. There is a layer below the line, where chips, data centers, clouds, and model providers keep advancing at speed. Above the line is where business value shows up. That is where companies design, deploy, and manage agents for customer service, employee productivity, and process automation. Raj describes Kore.ai as the operating system for that upper layer. The goal is simple to say and hard to deliver. Let teams build agents without writing code, plug into whichever models and clouds they prefer, and keep control through governance, security, and measurement. Interoperability runs through this conversation. Kore.ai partners with Microsoft, AWS, and G42’s Inception so customers can pick the environment that suits them. Vendor lock-in is a real anxiety for leaders. Raj’s answer is to be model agnostic, data agnostic, and cloud agnostic, while conforming to open standards. That way, enterprises spend their energy on the agents themselves rather than on maintaining a platform. It is a pragmatic view that reflects what I keep hearing on conference floors. Scale brings its own lessons. At Kore.ai’s volume, latency, accuracy, and security are non-negotiable, and governance is a daily practice rather than a slide in a deck. Raj talks candidly about no-code democratization, where a business user can assemble an agent for a focused task, then graduate to more complex workflows when ready. We also touch on the rise of on-device models in smartphones and where the boundary sits between quick local tasks and heavier actions that still rely on hosted models. If you care about agentic AI that does real work, this one is worth your time. Raj shares a thoughtful take on collaboration, standards, and why leaders should read widely, question everything, and start building with what is available today. Links are in the show notes.

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.