Tech Talks Daily

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 Flanagan 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. ********* 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 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. ********* 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

AI is quickly moving from boardroom buzzword to boardroom headache. Enterprises are waking up to the fact that bringing large language models in-house is not just about performance or cost, but about control, accountability, and trust. In this episode of Tech Talks Daily, I sit down with Octavian Tanase, Chief Product Officer at Hitachi Vantara, to unpack what this shift really means for business and technology leaders. Octavian explains why governance has become the defining challenge of the AI era. Companies are under pressure not only to innovate but also to meet new regulatory demands and maintain trust with customers. That requires more than patching together tools or hoping for transparency from public AI providers. It means creating governance frameworks that deliver traceability, auditability, and explainability as standard practice, not as afterthoughts. We explore why vector databases may need something like a time-machine capability to document when and how information is added, giving enterprises a provable audit trail. This level of accountability supports both internal oversight and external compliance, turning abstract AI ethics debates into real operational requirements. Our conversation also turns to the role of infrastructure. Hitachi Vantara’s VSP One, with its tagline “One Data Platform, No Limits,” has been built to simplify data complexity across block, file, and object storage while providing a unified foundation for AI workloads. Octavian shares how this unified approach helps enterprises run compliant, explainable, and efficient AI across hybrid environments that span both on-premises and the cloud. This isn’t just a story about technology, but about the future of trust in digital business. If AI remains a black box, its value will always be limited. If it becomes explainable, traceable, and accountable, it can transform not only efficiency but also relationships with customers, regulators, and partners. So, how can leaders strike the right balance between governance and innovation without slowing down progress? Octavian leaves listeners with a forward-looking perspective on what the next few years of enterprise AI will demand, and why those who build on strong governance today may end up with the most resilient advantage tomorrow.   ********* 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 if the way we store data is shaping the planet’s future? That thought has been on my mind ever since attending the IT Press Tour in Amsterdam, where I first connected with today’s guest. With global data creation forecast to hit 510 zettabytes by 2030, and data centers already consuming staggering amounts of power, the conversation is no longer about whether change is needed but about how we approach it. Joining me on the podcast is Nicholas Stavrinou, co-founder of CompressionX, a company rethinking lossless compression. Nicholas shares how a mathematical paradox in a university notebook grew into a technology that promises faster, cheaper, and more sustainable data storage. His story takes us from leather-bound journals and napkin sketches to a working product that is already helping users cut their digital footprints by more than 90 percent. In our discussion, Nicholas explains why compression deserves a seat at the sustainability table, especially as AI and enterprise workloads generate unprecedented volumes of cold data that simply sit idle in storage. We talk about the real costs of data growth, from spiraling cloud bills to the hidden environmental toll of cooling data centers, and we explore whether smarter compression could give businesses an edge while also reducing emissions. Nicholas and his team are also taking this message beyond theory. After the IT Press Tour, they are heading to Big Data LDN at Olympia London, where Compression X will be presenting in the Data for Good theatre at 2:40pm on Wednesday, September 24, and welcoming visitors at stand G58. It’s a reminder that sustainable infrastructure isn’t just about grand new facilities or green energy projects; sometimes it starts with rethinking something as humble as a file format. As you listen, ask yourself: could compression be one of the simplest yet most overlooked ways to make digital life more efficient, affordable, and sustainable? ********* 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

I recorded this episode at Barracuda TechSummit25 in Alpbach, Austria, a mountain village that looks like a postcard and hosts some of the most grounded security conversations you will hear all year. My guest is Richard Flanders, Commercial Director at Aura Technology, a managed service provider on the south coast of England that supports public sector organisations and tightly regulated commercial clients. Richard arrived as part of Barracuda’s Partner Advisory Board, which means he spends as much time feeding customer reality back into product teams as he does comparing notes with peers in the hallway. We talk through his first TechSummit experience and why the event’s focus on hands-on engineering matters for MSPs who live in the weeds of configuration, policy, and response. Richard shares early thoughts on Barracuda’s secure edge service and the continued maturation of XDR, but the heart of our chat is the pressure he sees on customers. Compliance is no longer a side quest. ISO 27001, Cyber Essentials Plus, supply chain reporting, and new European rules are shaping budgets and expectations. Boards want proof. Auditors want evidence. Buyers want to know a supplier chose fit-for-purpose tools. That makes documentation, contracts, and the ability to show your working as important as the tech itself. We also get into the human side. In a world that loves point solutions, many teams are tired of alert noise and tool sprawl. Richard explains why a single, coherent view helps his engineers move faster and train better, and why MSPs are leaning into prevention-focused workflows rather than waiting for the next fire. He is candid about the conversations no one enjoys, like end-of-life systems that keep a legacy app alive, and the need for tougher stances when risk sits outside an acceptable boundary. AI comes up too, without the hype. Aura is hiring a Head of AI and Automation, standing up a private AI platform, and committing to ship a handful of small, useful apps for customers in the year ahead. The lens is productivity and safety, with an emphasis on teaching teams how to question outputs and rethink everyday tasks. Add in security awareness training, phishing simulations, and tabletop exercises, and you start to see a culture shift from annual tick-boxes to regular, lived practice. There is a lovely moment of serendipity in here as well. Richard’s first conversation on day one was with another partner from Pune, the same city where Aura runs its network operations. They swapped ideas on automation and integration that might never have surfaced on a video call. That is the value of getting people in a room together, especially when the room happens to be carved into the side of a mountain. If you work with an MSP, this episode will help you ask better questions. If you are an MSP, you will recognise the balance Richard describes. Pick the right controls for the risks you actually face. Prove what you do. Keep training. And give your teams a single place to see what matters, so the next incident stays small. ********* 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

I recorded this conversation at Barracuda TechSummit25 in Alpbach, Austria, where the mountains feel close enough to touch and the discussions get very real very quickly. My guests are Adam Khan, VP of Global Security Operations at Barracuda XDR, and Eric Russo, Director of SOC Defensive Security. Together they run the teams that watch, interpret, and act when attacks move across email, identity, network, cloud, and endpoints. Their keynote used the language of sport to make sense of modern defense, and it worked. You will hear why football tactics map cleanly to security, how roles and formations translate to controls and playbooks, and why a strong back line matters when the opposition moves the ball quickly. Here is the thing that stood out for me. Integrated defense is not a slogan. When Adam and Eric talk about Extended Detection and Response, they are describing a practical way to join signals, add context, and trigger action without waiting for a human to click through ten consoles. XDR gives analysts one source of truth, connects events that would otherwise sit in separate tools, and shortens the time between a suspicious signal and an action that contains it. That is how you turn alert fatigue into something manageable, and it is how small teams hold their own against fast, multi-step attacks. The analogies make it easier to picture. In football, a defense tracks runners, closes passing lanes, and communicates constantly. In security, that means correlating identity with network flows and endpoint behavior, then deciding who picks up the threat and how to press. The Home Alone reference takes it further. Imagine Kevin’s improvised defenses as point tools scattered around a house. Now add a single screen that shows every door, every window, and which trap fires next. That is the plain-English version of XDR that anyone can understand. We also unpack real incidents that their teams have faced, without naming names. You will hear how attackers chain steps across layers, and how automated responses isolate systems, lock accounts, and cut off command and control before damage spreads. The lesson is simple. Visibility gives you options. Automation buys you time. People make the right calls when they can see the whole pitch. If you work in security, this episode gives you a clear view of what good looks like. If you are a business leader, it offers a way to measure progress that goes beyond tool counts and budget lines. And if you enjoy a metaphor that lands, football and Home Alone might be the clearest explanation of XDR you will hear all year. ********* 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