Tech Talks Daily

If every company is now a tech company and digital transformation is a journey rather than a destination, how do you keep up with the relentless pace of technological change? Every day, Tech Talks Daily brings you insights from the brightest minds in tech, business, and innovation, breaking down complex ideas into clear, actionable takeaways. Hosted by Neil C. Hughes, Tech Talks Daily explores how emerging technologies such as AI, cybersecurity, cloud computing, fintech, quantum computing, Web3, and more are shaping industries and solving real-world challenges in modern businesses. Through candid conversations with industry leaders, CEOs, Fortune 500 executives, startup founders, and even the occasional celebrity, Tech Talks Daily uncovers the trends driving digital transformation and the strategies behind successful tech adoption. But this isn't just about buzzwords. We go beyond the hype to demystify the biggest tech trends and determine their real-world impact. From cybersecurity and blockchain to AI sovereignty, robotics, and post-quantum cryptography, we explore the measurable difference these innovations can make. Whether improving security, enhancing customer experiences, or driving business growth, we also investigate the ROI of cutting-edge tech projects, asking the tough questions about what works, what doesn't, and how businesses can maximize their investments. Whether you're a business leader, IT professional, or simply curious about technology's role in our lives, you'll find engaging discussions that challenge perspectives, share diverse viewpoints, and spark new ideas. New episodes are released daily, 365 days a year, breaking down complex ideas into clear, actionable takeaways around technology and the future of business.

How Alison Kay Sees AWS Driving The Move From AI Adoption To Transformation 05/02/2026

How Alison Kay Sees AWS Driving The Move From AI Adoption To Transformation Are businesses really making progress with AI, or are many still stuck using it for the digital equivalent of making phone calls on a smartphone? In this episode, I sit down with Alison Kay, VP / Managing Director AWS UKI, to unpack what is actually happening behind the headlines of AI adoption across the UK. On paper, the numbers look strong. Around 64% of UK businesses are now using AI, a sharp rise from the previous year. But when you look closer, the story shifts. Only one in four organizations have moved into more advanced use cases, where real productivity gains, efficiency improvements, and innovation start to show up in meaningful ways. So what is holding everyone back? In our conversation, Alison shares insights from AWS research and her work with organizations ranging from major enterprises like Barclays and the BBC to fast-moving startups. We explore why skill shortages are slowing progress, why many companies struggle to move beyond basic use cases, and how governance and trust are becoming central to scaling AI responsibly. We also spend time breaking down the rise of agentic AI, a term that is starting to appear everywhere. Instead of simply generating answers, these systems are beginning to take action, writing code, testing software, and working alongside humans to dramatically accelerate delivery timelines. Alison shares a powerful example where a project that might have taken 40 engineers over two years was completed by six engineers in just 76 days with the support of AI agents. Along the way, we look at real-world examples from companies like Trainline and Evri, showing how AI is already reshaping customer experience and operational efficiency in ways that go far beyond theory. This episode is a must-listen for business leaders trying to understand where AI is delivering real value today, where the biggest gaps still exist, and how to move from experimentation to meaningful transformation. So if your organization is already using AI, the real question becomes this, are you using it to improve what you already do, or are you ready to rethink how your business operates entirely? Useful Links . Please check the partners of the Tech Tech Talks Network Learn more about the Visit /episode/index/show/techblogwriter/id/41079575

Inside AWS At 20: Werner Vogels On The Moment Everything Changed 04/30/2026

Inside AWS At 20: Werner Vogels On The Moment Everything Changed What if one of the most influential figures in modern technology had almost ignored the opportunity that would define his career? In this episode, I sit down with Werner Vogels, Chief Technology Officer at Amazon, to explore the story behind Amazon Web Services as it marks its 20th anniversary, and how a near-dismissed phone call turned into a front-row seat to one of the biggest shifts in computing history. Werner takes me back to the early days when Amazon was still seen as “just a bookstore,” and shares what he discovered when he first stepped inside what he calls Amazon’s “technology kitchen.” What he found was a company solving problems at a scale that commercial software simply could not handle, forcing them to build everything themselves. That mindset would go on to shape everything from Dynamo to the foundations of modern cloud infrastructure. We also unpack the thinking behind one of the most important shifts in enterprise technology, the move from upfront licensing to pay-as-you-go. It sounds obvious now, but at the time it challenged how the entire industry operated, giving businesses the ability to experiment, scale, and take control of their own costs in ways that were not possible before. Looking ahead, Werner offers a refreshing perspective on AI and what he describes as a developer renaissance. While many headlines focus on replacement, he sees AI as a tool that amplifies human capability, placing even greater importance on curiosity, ownership, and collaboration. It is a reminder that while tools will continue to evolve, responsibility and decision-making still sit firmly with the people using them. This episode is a must-listen for anyone building, leading, or investing in technology. It connects the dots between past, present, and what comes next, showing how today’s AI wave echoes the same patterns that shaped the cloud revolution. So as we look toward the next era of computing, the question is simple, are we ready to think at the scale required to build what comes next? Useful Links Please check the partners of the Tech Tech Talks Network Learn more about the Visit /episode/index/show/techblogwriter/id/41079135

SAS Innovate: Turning Messy Data Into Meaningful Decisions With AI In Healthcare 04/30/2026

SAS Innovate: Turning Messy Data Into Meaningful Decisions With AI In Healthcare Can faster access to real-world data actually change patient outcomes, or are we still too reliant on controlled clinical trials to see the full picture? In this episode, I sit down with Dr. Alex Asiimwe, Executive Director of Epidemiology at Gilead Sciences, to explore a topic that doesn’t get enough attention in the AI conversation, real-world evidence. While much of the industry focuses on AI in drug discovery or diagnostics, Alex brings a different perspective, one rooted in what happens after treatments reach real patients in the real world. As he explains, clinical trials may be the gold standard, but they are still controlled environments. Real-world evidence is where we begin to understand how treatments perform across diverse populations, healthcare systems, and everyday conditions. What stood out in our conversation is just how messy and fragmented that real-world data can be. Much of it is not collected for research purposes, which means it takes months, sometimes up to a year, to clean, structure, and analyze before it can inform decisions. Alex shares how AI is beginning to change that, not by replacing human expertise, but by automating the most time-consuming parts of the process. If that timeline can be cut in half, the impact is immediate. Faster evidence means faster decisions, and in healthcare, delays in evidence can directly affect patient outcomes. We also explore what Alex describes as the “analytics gap,” the disconnect between where data exists and where insights are actually generated. Today, much of the evidence used in drug development still comes from limited datasets, often from a single country or region. Yet the treatments themselves are global. That mismatch creates blind spots, particularly in low and middle-income countries where data is often unstructured, fragmented, or simply not accessible. AI has the potential to standardize and unlock that data, helping to create a more complete and representative view of patient populations worldwide. Of course, the challenges are not just technical. Trust, governance, and politics all play a role in whether data can be shared and used effectively. Alex is clear that the biggest barrier is not the science or the analytics, it is building trust between organizations, governments, and communities. Without that, even the most advanced AI models cannot deliver meaningful outcomes. This conversation also touches on the importance of collaboration, not just between healthcare organizations and technology providers like SAS, but across the global ecosystem. Alex highlights how partnerships, open standards, and shared frameworks can help close the analytics gap and accelerate progress in areas like HIV prevention, where understanding real-world patient behavior is critical. As we wrap up, one message comes through clearly. AI is not a miracle solution, and it will not transform healthcare overnight. But when applied to the right parts of the workflow, especially around data preparation and evidence generation, it can create measurable, meaningful change. So as healthcare leaders look to move beyond pilots and into real impact, the question becomes, are we focusing on the right problems, and are we ready to open up the data needed to solve them? Useful Links I – Observational Health Data Sciences and Informatics Please check our partners of Tech Tech Talks Network Learn more about the /episode/index/show/techblogwriter/id/41078475

Freshworks CEO On The SaaS-pocalypse And What Comes Next For Software 04/29/2026

Freshworks CEO On The SaaS-pocalypse And What Comes Next For Software In this episode of Tech Talks Daily, I welcome back Dennis Woodside, CEO of Freshworks, to unpack the growing conversation around the so-called SaaS-pocalypse and what it really means for the future of software businesses. There is no shortage of dramatic headlines suggesting SaaS is under threat, but Dennis offers a far more practical perspective. He explains that this is less about the collapse of software and more about a major reset in how software is judged, bought, and valued. As AI changes customer expectations, businesses are no longer willing to pay for incremental features or vague AI claims. They want clear outcomes, measurable ROI, and platforms that can prove they belong inside an AI-augmented tech stack. We discuss how the traditional seat-based pricing model is shifting toward consumption, outcomes, and usage-based models. Dennis shares why software companies without a strong AI strategy risk being squeezed out. At the same time, those with mission-critical systems of record and deep workflow intelligence are better positioned to thrive. He explains why deterministic software still matters in a world obsessed with generative AI and why the future belongs to platforms that combine trusted operational data with secure, embedded AI experiences. Dennis also shares how customers are changing the way they evaluate software, with many now using tools like ChatGPT and Google Gemini to compare vendors, analyze RFPs, and arrive at buying decisions far earlier in the sales process. This shift is forcing software vendors to rethink marketing, product design, and customer engagement from the ground up. We also explore the balance between governance and experimentation, why AI adoption must happen from both the top down and bottom up, and why speed, not just cost reduction, is becoming the real business driver. Dennis shares examples of how organizations are redesigning workflows, accelerating engineering output, and freeing up high-value talent from repetitive work. As he puts it, most companies are no longer asking if they need AI; they are asking how fast they can make it part of everything they do. If you have been wondering whether the SaaS model is broken or simply evolving into something smarter, this conversation offers a sharp and realistic look at what comes next. How is your business thinking about durability in an AI-first world, and are you building to last or simply building to grow? Useful Links Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/41049015

Google Cloud Next 2026: How Workspace Intelligence Is Redefining The Future Of Work 04/28/2026

Google Cloud Next 2026: How Workspace Intelligence Is Redefining The Future Of Work How much of your working day is actually spent doing meaningful work, and how much is lost chasing emails, searching for documents, sitting in meetings, and trying to remember where that one important conversation happened? At Google Cloud Next in Las Vegas, I sat down with Yulie Kwon Kim, Vice President of Product for Google Workspace at Google, to talk about how AI is changing the way billions of people work every day. Yulie leads the products many of us rely on constantly, Gmail, Google Calendar, Drive, Docs, Sheets, Slides, and newer tools like Google Vids. At this year’s event, she introduced Workspace Intelligence, a major step forward in how AI works inside those everyday tools. Instead of acting like a disconnected assistant, Workspace Intelligence understands your context across emails, meetings, files, and organizational knowledge to help create documents, prioritize inboxes, take meeting notes, and automate the repetitive work that quietly drains productivity. We explore what Workspace Intelligence actually is, how it differs from third-party AI tools, and why context matters just as much as model capability. Yulie explains why being a truly AI-first enterprise requires more than powerful models, it needs grounded context, governance, and security that people can trust. We also discuss one of the biggest concerns for business leaders: how to adopt AI without creating new risks around data security and access control. Yulie shares how Google approaches governance inside Workspace and why existing permissions and protections remain central to how AI operates. This conversation also touches on something bigger, the shift from individual productivity to shared organizational intelligence, where knowledge moves from living inside one person’s head to becoming something the entire company can benefit from. If AI could remove one frustrating task from your workday tomorrow, what would you choose first? Useful Links Connect with Vice President of Product for Google Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/41040800

Google Cloud Next 2026: How Agentic AI Is Transforming Financial Services 04/28/2026

Google Cloud Next 2026: How Agentic AI Is Transforming Financial Services What happens when one of the world’s most heavily regulated industries starts moving at AI speed? At Google Cloud Next in Las Vegas, I sat down with Sid Nadella, Director of Financial Services and Market Leader at Google Cloud, to talk about how AI is reshaping banking, wealth management, and capital markets from the inside out. With more than 20 years of financial services experience, including a long career at Goldman Sachs, Sid brings a rare perspective on how traditional institutions are balancing innovation with regulation, trust, and zero tolerance for error. We explore why the industry is moving beyond simple AI pilots and into what he calls the “doing era,” where agentic AI is helping firms move from static dashboards and fragmented workflows toward intelligent systems that can reason, anticipate, and act in real time. Sid shares where he sees the biggest business impact today, from fraud detection and risk management to operational efficiency and unlocking new growth. We also discuss real-world examples from firms like Citi Wealth, Citadel, Scotiabank, and Starling Bank, and why the real opportunity lies in building the right foundations first: governance, compliance, observability, and strong data access across increasingly complex environments. We also tackle one of the biggest concerns around AI adoption, the fear that it replaces people. Sid explains why the real story is augmentation, helping teams remove repetitive work and focus on better decisions, stronger customer relationships, and higher-value outcomes. If you work in financial services, enterprise technology, or simply want to understand what agentic AI looks like beyond the headlines, this is a conversation packed with practical insight. How close is your organization to becoming truly agentic? Useful Links Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/41038535

Tenable On Agentic AI, Exposure Gaps, And The Next Big Security Risk 04/27/2026

Tenable On Agentic AI, Exposure Gaps, And The Next Big Security Risk What happens when AI starts moving faster than the people meant to control it? In this episode, I’m joined by Bernard Montel, Field CTO EMEA at Tenable, for a timely conversation about the AI risks many organizations may be underestimating. Bernard believes we are heading toward a defining AI accident and that the first major incident may come through speed, scale, and unintended consequences rather than a malicious attack. We talk about why so many companies feel pressure to adopt AI at pace, while visibility, governance, and control struggle to keep up. Bernard describes this moment as “driving faster than we can steer,” and explains why shadow AI, overprivileged identities, cloud misconfigurations, and exposed AI projects are already creating real business risk. The conversation also looks at agentic AI and why giving systems the ability to take action changes the security equation. A chatbot giving a wrong answer is one problem. An AI agent making flawed decisions, leaking data, or interacting with industrial systems is something very different. Bernard also shares why AI can become a distraction from the security basics that still matter, including cloud security, identity, exposure management, and vulnerability remediation. Attackers may be using AI to move faster, but many of the weaknesses they exploit remain painfully familiar. We also discuss Tenable’s new agentic AI framework, announced during RSA, and how the company is using AI to help security teams respond at machine speed while reducing exposure across IT, cloud, OT, identity, and AI environments. For business and security leaders, this episode offers a clear warning and a practical takeaway. AI adoption is no longer a future conversation, but control, governance, and exposure management need to move with it. How prepared is your organization for an AI incident caused by accident rather than attack? Share your thoughts. Useful Links Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40869910

The Role Of Technology In Creating Healthier, Smarter Buildings 04/26/2026

The Role Of Technology In Creating Healthier, Smarter Buildings What if the smartest climate technology strategy isn’t about inventing something new, but rethinking the buildings we already spend 90% of our lives in? In this episode of Tech Talks Daily, I sit down with Ben Stapleton, Chief Executive Officer of US Green Building Council California, or USGBC California, to discuss why buildings sit at the center of sustainability, resilience, and community well-being. From energy use and air quality to wildfire resilience and climate justice, Ben makes a compelling case that the built environment may be one of the most practical places to create real change. Ben and his team launched the California Building Performance Hub, a platform designed to help building owners, operators, and policymakers understand how to improve building performance through policy guidance, technical resources, rebates, and even an AI-powered assistant trained on building codes and compliance pathways. We discuss how this platform is helping accelerate California’s move toward healthier, lower-energy, high-performance buildings and why AI is becoming a useful sidekick rather than a replacement for human expertise. Our conversation also moves beyond technology and into something far more human: community. Ben shares how sustainability only works when people feel they have both awareness and agency. From helping low-income communities understand electrification and indoor air quality, to taking a "BuildSMART Trailer” filled with real building materials into neighborhoods so people can touch and understand the future of their homes, this episode is a reminder that climate progress starts with education and trust. We also talk about wildfire resilience in California, where simple low-cost building decisions can dramatically reduce fire risk while also improving energy efficiency and health outcomes. Ben explains why many of the solutions already exist, and why the challenge is often less about invention and more about implementation, policy, and long-term thinking. For business leaders, public sector teams, and anyone thinking about the future of cities, this episode offers a fresh perspective on sustainability as both a financial and human opportunity. Healthier buildings create healthier people, and healthier people create stronger businesses. Is the future of climate action already built around us, and are we finally ready to look up and see it? I’d love to hear your thoughts. Useful Links Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40815630

Certinia And Spaulding Ridge On AI, ROI, And Services Teams 04/25/2026

How Nue Is Bringing Agentic AI To Revenue Operations 04/24/2026

How Nue Is Bringing Agentic AI To Revenue Operations How much revenue is lost because the systems behind pricing, quoting, billing, and finance still do not talk to each other properly? In today’s episode, I’m joined by Tina Kung, CTO and Co-Founder of Nue, the quote-to-revenue platform helping AI and SaaS companies rethink how they sell, bill, and grow. Tina brings more than two decades of experience across enterprise software, CPQ, billing, and revenue operations, with previous roles at Oracle, Zuora, SteelBrick, and Salesforce. Tina shares the story behind Nue and why she saw a growing gap between the systems that handle selling and the systems that manage revenue. As SaaS companies move from traditional subscriptions into usage-based pricing, credit burn-down models, product-led growth, partner channels, and enterprise sales, the old way of stitching together tools with manual work and spreadsheets starts to break down. We discuss how AI is changing go-to-market operations and why transaction-level intelligence matters. Tina explains how Nue connects quoting, billing, usage, and revenue data into a single system, then applies AI so teams can understand what is happening, spot opportunities, and take action faster. One of the standout stories is OpenAI, which rolled out Nue in just eight weeks to support the rapid growth of its ChatGPT Enterprise business. Tina shares what that process revealed about the speed of modern AI companies and why flexible revenue infrastructure is now a serious advantage. We also talk about the rise of agentic AI in revenue operations, from creating quotes and orders to handling subscription changes and surfacing upsell opportunities. As the SaaS model comes under pressure from AI, Tina offers a practical view of what needs to change behind the scenes for companies to stay competitive. If SaaS is entering a new chapter, are your revenue systems ready for how customers now buy, use, and pay for software? /episode/index/show/techblogwriter/id/40649005

Jack Fu Of Draco Evolution On The Future Of AI-Driven ETFs 04/23/2026

Jack Fu Of Draco Evolution On The Future Of AI-Driven ETFs Can AI really remove emotion from investing, or does human judgment still matter most when money is on the line? In today’s episode, I’m joined by Jack Fu, Founder and CEO of Draco Evolution, a company using AI, quantitative models, and decades of market experience to help investors make smarter and more disciplined decisions. Jack’s journey began during the 2008 financial crisis while working as a financial advisor at Union Bank of California, where watching investors lose life-changing amounts of money completely reshaped how he thought about risk, discipline, and long-term wealth creation. That experience led him to focus on one simple principle: avoiding big losses matters just as much as chasing returns. From managing assets for family offices and institutional clients to leading major investment operations across the Asia-Pacific region, Jack built his career around protecting capital first and helping investors stay in the market long enough to benefit from long-term growth. We explore how Draco Evolution is bringing institutional-level investment tools to everyday investors through AI-powered ETFs and a more dynamic approach to portfolio management. Jack explains how ETFs actually work, why they have become such a popular choice for investors, and the important difference between investing in AI companies and using AI itself to manage investment decisions. We also discuss the future of robo-advisors and why the next generation will move far beyond static questionnaires and occasional portfolio rebalancing. Jack shares why he believes the future lies in systems that adapt continuously to market conditions and investor behavior, creating something far more personal and responsive. From algorithmic trading and AlphaGo to today’s world of agentic AI, Jack offers a practical perspective on how technology is changing finance without replacing human oversight. He also shares why investors should treat AI as an enhancement tool rather than blindly trusting every recommendation. If you’ve ever wondered how AI is changing investing, what makes AI-driven ETFs different, or how to stay disciplined in unpredictable markets, this conversation offers plenty of insight. How much would you trust AI to help manage your financial future, and where would you still want a human in the loop? /episode/index/show/techblogwriter/id/40630060

Adobe Summit: Virgin Atlantic’s AI Concierge and the Future of Travel 04/22/2026

Adobe Summit: Virgin Atlantic’s AI Concierge and the Future of Travel What does it actually take to move from AI experiments and pilot projects to real business outcomes that customers can feel? At Adobe Summit in Las Vegas, I sat down with Neil Letchford, Vice President of Digital Engineering at Virgin Atlantic, to talk about how the airline is doing exactly that. While many organizations are still debating ROI, governance, and where agentic AI fits into the customer journey, Virgin Atlantic has already launched an AI concierge that is actively helping customers book holidays, find answers faster, and create a smoother travel experience from the first search to stepping onboard the aircraft. Neil shared how a proof of concept built in just two months evolved into a live multi-agent system that now helps customers plan trips, book holidays, and move seamlessly between digital channels and human support when needed. We talked about the importance of “knowledge over data,” why observability and model evaluation matter when deploying AI at scale, and how the team built trust internally by focusing on real customer pain points rather than chasing shiny technology trends. What stood out most was how Virgin Atlantic has kept its famously human customer experience at the center of every decision. This is not automation for the sake of efficiency. It is about using AI to strengthen relationships, preserve brand personality, and create better outcomes for both customers and the business. From personalized holiday planning to agent-to-agent interactions that may soon redefine travel booking, this conversation offers a practical look at what happens when AI moves beyond theory and starts delivering value today. If you want to understand what agentic AI looks like in the real world, and why the companies moving early may gain a serious advantage, this is an episode you do not want to miss. What would AI need to do in your business before you would trust it to take the lead? Useful LInks Learn more about Check out Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40954435

Inside Brightcove: Filippo de Salazar On AI, Automation, And The New Streaming Economy 04/21/2026

Inside Brightcove: Filippo de Salazar On AI, Automation, And The New Streaming Economy How has streaming changed from simply delivering video to becoming one of the most important business engines behind sports, media, and customer engagement? In this episode of Tech Talks Daily, I sit down with Filippo de Salazar, who leads the Brightcove team following its acquisition by Bending Spoons, to talk about how the company is evolving and where the future of streaming is heading next. With more than 20 years in the industry and powering over a billion streams every week, Brightcove has become the invisible backbone behind many of the broadcasters, publishers, sports networks, and enterprise video experiences we all rely on without ever thinking about the technology behind them. Filippo shares how the past year has accelerated Brightcove’s product velocity, with major releases including AI capabilities, live 4K, live DRM, and automation tools that help customers move faster without compromising reliability. While the business has gained speed, he explains that Brightcove’s focus on stability and customer obsession remains unchanged, especially when customers depend on mission-critical video workflows that leave no room for failure. We also unpack how AI is moving beyond hype and creating measurable value for broadcasters today. From automatically detecting live sports highlights and clipping them for instant social sharing, to improving ad placement relevance, generating live captions, and translating content into more than 70 languages, AI is reshaping both operational efficiency and revenue generation. Filippo explains how tools like Brightcove’s Universal Translator and Metadata Optimizer are helping broadcasters unlock ROI that simply was not possible before. Our conversation also covers personalized streaming, fan engagement, cloud-native automation, and the rise of FAST channels. We discuss why sports audiences now expect low latency, instant highlights, and highly personalized viewing experiences, and how broadcasters must balance those expectations with the realities of infrastructure costs and monetization pressure. Filippo also shares why discoverability has become one of the biggest battlegrounds in streaming, with some viewers spending more time searching for content than actually watching it. Looking ahead, Filippo outlines the three trends he believes will define the next phase of streaming: intelligent automation, stronger monetization discipline, and managing fragmented viewing behaviors across live, subscription, ad-supported, and FAST environments. As media companies try to unify these experiences without adding complexity, platforms like Brightcove are becoming increasingly central to how modern video businesses operate. What does the future of streaming really look like when AI, automation, and personalization all collide, and are broadcasters ready for what comes next? Useful Links Connect with Learn more about following its acquisition by Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40697390

How HelloFresh Replaced 450 Spreadsheets With Real-Time Decisions 04/20/2026

How HelloFresh Replaced 450 Spreadsheets With Real-Time Decisions What happens when the biggest breakthrough in AI isn’t a flashy new tool, but finally getting rid of 450 spreadsheets? Recording live from Qlik Connect, I sat down with Ed Dunger from HelloFresh to talk about what operational transformation actually looks like inside one of the world’s most complex supply chain environments. Because when your business depends on forecasting demand, managing perishable food, coordinating deliveries, and making sure customers receive the right box at the right time, small inefficiencies quickly become expensive problems. Ed leads operational technology and analytics enablement across global teams at HelloFresh, covering everything from forecasting through to final-mile logistics. In this conversation, he shares how the company moved away from hundreds of disconnected Google Sheets and manual processes toward a near real-time, data-driven operating model that gives teams faster, clearer, and more reliable decision-making. We talk about the practical reality of replacing over 450 spreadsheets, building trust in the data, and creating systems that operational teams actually want to use. Ed explains why this was a two to three year journey rather than an overnight transformation, and how early wins, like predicting waste before it happened, helped build confidence across the business. We also explore how HelloFresh is using predictive AI to improve exception management when deliveries fail. From triggering recovery boxes faster to improving customer communication when something goes wrong, the focus is not on AI for the sake of AI, but on solving real problems that directly affect customer experience. There is also a valuable lesson here for any business trying to move from experimentation to operational reality. Start small, build trust gradually, and focus on solving one problem well before trying to transform everything at once. So as more organizations race to adopt AI, are we sometimes overlooking the simple operational fixes that create the biggest impact? And is real transformation less about the technology itself, and more about how people learn to trust it? Join me for a practical and honest conversation from Qlik Connect, and let me know your thoughts. Are you still managing around old processes, or are you building systems people can truly rely on? Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40920865

How the Reconomy Group and Valpak Went From Spreadsheets to Scalable AI-Powered Data Platforms 04/19/2026

How the Reconomy Group and Valpak Went From Spreadsheets to Scalable AI-Powered Data Platforms How do you turn complex regulatory data into something customers can actually use, trust, and act on? Recording live from Qlik Connect, I sat down with Robin Astle, Head of Qlik Analytics at Reconomy Group, to explore how data is becoming far more than an internal reporting tool. In Robin’s world, it has become a product in its own right, helping some of the world’s largest retailers manage compliance, reduce costs, and make smarter sustainability decisions. Robin works across Valpak, a business at the center of environmental compliance and packaging regulation, supporting over 100 enterprise customers across the UK, Europe, and the US. From packaging taxes and recycling targets to government submissions and sustainability reporting, the amount of data involved is enormous, and the stakes are high. In our conversation, Robin shares how the Valpak Insight Platform evolved from manual SQL extracts and spreadsheets into a fully scaled cloud-based analytics platform ingesting millions of rows of data every day. We discuss how that transformation helped reduce onboarding from weeks to days, created up to 90% time savings on CSR and analytics requests, and helped customers reduce compliance costs by up to 15%. We also explore the launch of PackChat, which uses natural language queries to help customers interact with compliance and packaging data without needing deep technical knowledge. Robin explains why context is everything when dealing with environmental regulations, and why building trust in the data model is essential before AI can deliver real value. There is also a bigger conversation here around how businesses can use data to serve customers directly, not just support internal teams. From OEM partnerships and cloud automation to scaling AI-powered services across global markets, Robin shares what it takes to turn data into a revenue-generating service. So as more organizations look to unlock value from the information they already hold, are we still thinking too narrowly about what data can do? And could your greatest untapped product actually be the data sitting inside your business today? Join me for a fascinating conversation from Qlik Connect, and let me know your thoughts. Are you still using data for reporting, or are you starting to think about it as a product? Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40920785

Qlik Connect: Mary Kern On Building AI People Will Actually Use 04/18/2026

Qlik Connect: Mary Kern On Building AI People Will Actually Use How do you turn powerful AI technology into something customers actually trust, adopt, and use? Recording live from Qlik Connect, I sat down with Mary Kern, Vice President of Analytics Product Go-To-Market at Qlik, to explore one of the most overlooked challenges in enterprise AI today. Not building the technology, but making it real for the people expected to use it every day. Because while AI innovation is moving at incredible speed, many organizations are still struggling with a much more practical question. How do you move from exciting product announcements and pilot projects to real adoption, measurable outcomes, and business value? In our conversation, Mary shares how Qlik is approaching that challenge by shifting the focus away from shiny features and toward outcomes that matter. We discuss why agentic AI is creating so much excitement, why customers are often much closer to operationalizing it than they realize, and how years of investment in data quality, governance, and analytics are now becoming the foundation for what comes next. We also talk about the growing importance of trusted data and context, especially as AI moves from generating insights to influencing decisions and actions. Mary explains why simply adding a large language model on top of existing systems rarely works, and why organizations need to think more carefully about how AI is trained, governed, and integrated into the environments where people already work. There is also a refreshingly honest conversation around cost, experimentation, and imperfection. Mary makes the case that organizations should start now, even if the data is not perfect, because using AI often reveals where the real gaps are and what needs to improve next. So as businesses look ahead to the next 12 months, what will separate those who successfully scale AI from those still stuck in pilot mode? And are we spending too much time talking about the technology, and not enough time understanding how people will actually use it? Join me for a candid conversation from the heart of Qlik Connect, and let me know your thoughts. Is your organization closing the gap between AI capability and real adoption, or is that still the biggest challenge? /episode/index/show/techblogwriter/id/40920510

Qlik Connect: Nick Magnuson On Trusted Data and Agentic AI 04/18/2026

Qlik Connect: Nick Magnuson On Trusted Data and Agentic AI What if the reason most AI projects fail has less to do with the technology and more to do with how the work itself is designed? Recording live from Qlik Connect, I sat down with Nick Magnuson, Head of AI at Qlik, for a conversation about the gap between AI ambition and operational reality. Because while many organizations are still focused on models, tools, and the race to deploy new capabilities, the real challenge often sits somewhere much less glamorous. Workflow design, trusted data, and making sure AI fits the way a business actually runs. Nick brings more than two decades of experience in machine learning and predictive analytics, and in this conversation, he shares why so many AI initiatives fail before they ever create value. His view is refreshingly direct. Most failures are not technology failures at all. They are workflow failures, where teams try to force AI into the business without first understanding the outcomes they are trying to achieve. We also explore the rise of agentic AI and what it means when systems move from generating insights to taking action. Nick explains why governance becomes even more important in that world, how organizations can balance speed with control, and why trusted data has to move beyond being “good enough for reporting” to becoming reliable enough for decisions and automated execution. There is also a strong discussion around openness, portability, and the growing risk of vendor lock-in. As enterprises build more complex AI ecosystems, flexibility is becoming a strategic advantage, especially for organizations trying to scale without creating expensive dependencies they will regret later. For mid-market businesses with limited resources, Nick also shares a practical path to production. A reminder that operationalizing AI does not require massive teams or unlimited budgets, but it does require clarity, discipline, and a focus on the right problems first. So as the next wave of enterprise AI moves from experimentation to execution, what will separate the organizations that scale successfully from those still stuck in pilot mode? And are we asking the wrong questions by focusing on more AI, instead of better AI? Join me for a thoughtful conversation from the heart of Qlik Connect, and let me know your view. Is workflow design the missing piece in your AI strategy? /episode/index/show/techblogwriter/id/40920665

How American University’s Kogod School Of Business Is Redefining AI Education And Business Strategy 04/17/2026

How American University’s Kogod School Of Business Is Redefining AI Education And Business Strategy What does it really take to turn AI from a flashy experiment into something that creates measurable business value? In this episode of Tech Talks Daily, I sat down with Angela Virtu from American University’s Kogod School of Business to talk about what business leaders should actually be paying attention to as AI moves into a new phase in 2026. This conversation goes far beyond the usual headlines about bigger models and faster tools. Angela brings a rare mix of academic leadership and hands-on startup experience, which means she understands both the technical side of AI and the hard business questions around adoption, trust, and ROI. One of the most interesting parts of our discussion centered on how American University’s Kogod School of Business became one of the first AI-first business schools. Angela shared how that shift was never really about chasing hype. It was about recognizing a real change in the workplace and preparing students for jobs, workflows, and expectations that are already being shaped by AI. From faculty training to culture change, she explained how transformation only works when leadership is willing to support experimentation and accept that some ideas will fail before the right ones take hold. We also spent time unpacking where businesses stand right now in the AI adoption cycle. After years of pilots and proof-of-concept projects, many companies are under pressure to show results. Angela offered a refreshingly honest take on why so many AI projects stall and why adoption alone is a weak metric. Instead, she argued that companies need to tie AI initiatives to clear business problems and existing KPIs. Whether that means customer support resolution times, employee productivity, or operational efficiency, the point is simple. AI needs to earn its place. Another thread running through this episode is governance. As AI becomes more deeply embedded inside organizations, the conversation is shifting toward oversight, accountability, and trust. Angela explains why the strongest governance models are often shared across the company rather than locked inside one team. She also discusses the need for closed systems, stronger communication, and honest disclosure when businesses use AI in customer-facing environments. That part of the conversation feels especially timely as more brands try to balance innovation with customer expectations. We also looked ahead at what is coming next, from model orchestration and vertical AI to the rise of physical world models and even the possibility of AI agents becoming a customer audience in their own right. It is one of those episodes that will give business leaders, technologists, educators, and curious listeners plenty to think about. If you are trying to understand where AI strategy is headed in 2026, and how to separate real value from noise, this episode is for you. What did you make of Angela’s views on governance, ROI, and the next phase of AI adoption, and where do you think businesses are still getting it wrong? Share your thoughts with me. Useful Links: Visit the Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40649025

Qlik Connect: Ryan Welsh On Turning AI Into Business Outcomes 04/16/2026

Qlik Connect: Ryan Welsh On Turning AI Into Business Outcomes What actually separates AI that delivers real value from AI that never makes it past the demo stage? Recording live from Qlik Connect, I sat down with Ryan Welsh, Field CTO of Generative AI at Qlik, to get a grounded, practitioner-led view of what it really takes to make AI work inside a business. While the industry has spent the past few years racing to experiment, build, and deploy new capabilities, many organizations are still struggling to turn that progress into capabilities people use every day. In our conversation, Ryan cuts through the noise and explains why so many AI initiatives fail. Not because the models aren’t powerful enough, but because they’re not designed to fit into real workflows. He shares why context is far more than just a buzzword and how getting the right data, in the right place, at the right time, enables AI to deliver meaningful outcomes. We also explore the growing shift toward agentic AI and the responsibilities that come with it. From designing systems that can act autonomously while remaining under control to understanding where humans need to stay involved, Ryan offers a practical view of how organizations can move forward without introducing unnecessary risk. There’s also a refreshing honesty around where we are right now. After a wave of investment and expectation, many companies struggled to see immediate value from AI. But as Ryan explains, that period is changing, with more organizations finding ways to scale what works and move beyond isolated use cases. So, as businesses look ahead, what does it really take to move from experimentation to execution? And are we focusing too much on building more AI rather than the right AI for how our organizations actually operate? Join me for a candid conversation from the heart of Qlik Connect, and let me know your thoughts. Are you seeing AI deliver real outcomes in your business, or is it still stuck in the demo phase? Useful Links Connect with . , and Visit the May Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40898205

Qlik Connect: James Fisher On Turning AI Into a Business Strategy 04/16/2026

Qlik Connect: James Fisher On Turning AI Into a Business Strategy What does it really take to move beyond AI experimentation and build something a business can rely on? Recording live from Qlik Connect, I sat down with James Fisher, Chief Strategy Officer at Qlik, to unpack what’s actually changing as AI moves from hype into real-world execution. Because while many organizations have spent the past few years exploring use cases and running pilots, the harder challenge is now in front of them. Turning that early momentum into something scalable, governed, and aligned with business outcomes. In our conversation, James offers a candid view of where companies are getting this wrong. He describes a period of what he calls “AI madness,” where everything became a potential use case, but very little translated into measurable value. Now, he sees a shift toward more focused, outcome-driven thinking, where success depends on understanding the user, the data, and the specific problem being solved. One of the most thought-provoking moments comes when James challenges the idea of having an AI strategy at all. Instead, he argues that AI should be embedded directly into the broader business strategy, shaping how decisions are made, how processes operate, and how organizations compete. We also explore the realities that many businesses are only just beginning to face. The complexity of data access and governance, the growing pressure around cost and sustainability, and the risks of vendor lock-in in a rapidly evolving AI ecosystem. James shares why openness and flexibility are becoming critical, and why some of the same patterns seen in previous technology waves are starting to repeat themselves. So as organizations look ahead to the next 12 to 24 months, what will separate those that successfully operationalize AI from those that remain stuck in cycles of experimentation? And are we focusing too much on the technology, and not enough on the business problems it’s meant to solve? Join me for a grounded and strategic conversation from the heart of Qlik Connect, and let me know your thoughts. Are you still experimenting with AI, or are you starting to embed it into the core of how your business operates? Useful Links . , and Visit the May Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40897935

3483: How Glean Is Securing The Next Wave Of AI Agents In The Enterprise 04/15/2026

3483: How Glean Is Securing The Next Wave Of AI Agents In The Enterprise What happens when your AI agents start making decisions faster than your security team can even see them? In this episode, I sit down with Sunil Agrawal, Chief Information Security Officer at Glean, to unpack a shift already underway in enterprises. With predictions that 40 percent of enterprise applications will include autonomous AI agents by the end of 2026, we are moving from human-led workflows to machine-to-machine interactions at a scale most organizations are not fully prepared for. Sunil brings a rare perspective, blending more than 25 years of cybersecurity experience with an inventor's mindset shaped by over 40 patents. What stood out to me in our conversation is how quickly the traditional security model is becoming outdated. As he explained, "autonomous agents break those assumptions because they operate across tools, varying permissions and data sources with alarming speed and autonomy." This creates what he calls the "autonomy gap," in which the CIO's drive for speed collides with the CISO's need for visibility and control. We explore how that tension is playing out in real organizations today, and why so many are already falling behind. Nearly half of businesses still lack the AI-specific controls needed to prevent untraceable incidents, and the risks are not always what you might expect. Sunil argues that the first major rogue-agent incident is unlikely to be a malicious attack. Instead, it will come from confusion: a well-intentioned system taking the wrong action in the wrong context, with consequences that ripple across the business. The conversation then turns practical. Sunil breaks down his AWARE framework, a structured way to introduce real-time guardrails that evaluate intent, context, and risk before an agent takes action. Rather than relying on static policies, this approach focuses on continuous runtime enforcement, where systems are constantly assessed based on behavior rather than assumptions. What I found particularly valuable is how this moves beyond theory into something leaders can act on today. From starting with tightly scoped use cases to investing in full observability, this episode offers a clear roadmap for balancing innovation with accountability. As Sunil put it, organizations that succeed will not be the ones that move fastest, but the ones that prove trust at scale. So how do you embrace the productivity gains of autonomous AI without opening the door to invisible risk, and are your current security models ready for a world where the "user" is no longer human? Useful Links Visit the Tech Talks Network Sponsor /episode/index/show/techblogwriter/id/40885860

Qlik Connect: Mike Capone On Agentic AI and Turning Insight Into Action 04/14/2026

Qlik Connect: Mike Capone On Agentic AI and Turning Insight Into Action What does it actually take to move AI from experimentation into something a business can depend on every single day? Recording live from the show floor at Qlik Connect in Florida, I sat down with Qlik CEO Mike Capone to cut through the noise and get to the reality behind enterprise AI in 2026. Because while the headlines are still dominated by rapid innovation and new capabilities, many organizations are quietly facing a different challenge. They are struggling to turn AI ambition into measurable outcomes. In our conversation, Mike shares what he is hearing from customers around the world and why so many companies remain stuck in cycles of pilots and proof of concepts. We talk about the growing pressure from boards and leadership teams to move faster, and why that urgency is often leading to what he calls a “ready, fire, aim” approach that fails to deliver real business value. We also explore one of the biggest themes emerging at Qlik Connect this year. The shift toward agentic AI. But rather than focusing on the hype, Mike breaks down what this actually means inside a real enterprise workflow, where insights are not just generated but turned into decisions and actions. He also explains why getting the data foundation right is no longer optional, and how poor data quality can quickly turn AI from an opportunity into a risk. From data trust and governance to the challenges of operating across increasingly complex regulatory environments, this episode offers a clear view of what it takes to build AI systems that are reliable, scalable, and grounded in real business context. So as organizations look ahead to the next 12 to 24 months, what will separate those that successfully operationalize AI from those that remain stuck in pilot mode? And are we focusing too much on building more AI, rather than building better AI? Join me for a candid conversation from the heart of Qlik Connect, and let me know where you stand on this shift. Are you seeing real progress, or are the same challenges holding things back? /episode/index/show/techblogwriter/id/40868615

Twilio: Demystifying Model Context Protocol (MCP) And Real-World AI Deployment 04/14/2026

Twilio: Demystifying Model Context Protocol (MCP) And Real-World AI Deployment How are brands supposed to deliver AI-powered customer experiences when their data is scattered across systems that were never designed to work together? In this episode, I sit down with Peter Bell, VP EMEA Marketing at Twilio, to unpack one of the most important AI topics that still does not get enough attention outside technical circles, Model Context Protocol, or MCP. While many conversations about AI remain stuck on model hype, chatbots, and the latest product launch, Peter brings the discussion back to something far more practical. If businesses want AI to deliver real outcomes in customer service, marketing, and brand engagement, they first need a reliable way to connect large language models to the right data, in the right systems, with the right controls in place. That is why this conversation matters. Peter explains how MCP could become one of the biggest unlocks for enterprise AI by creating a standard way for LLMs to access information across fragmented tools like CRM platforms, marketing systems, and other business applications. Instead of forcing every company to build custom integrations from scratch, MCP creates a more consistent path for connecting models to the context they need. For me, that is where this episode really earns its place, because it moves the AI conversation away from vague ambition and toward the plumbing that actually makes useful AI possible. We also talk about why first-party data remains so important, especially as businesses try to create customer experiences that feel seamless, personal, and trustworthy. Peter makes the point that public models may be useful for general knowledge, but brands cannot rely on generic internet-trained systems to solve precise business problems. If you want AI to support travel bookings, customer service, or commerce journeys, you need specific data, strong governance, and a much clearer understanding of the problem you are trying to solve. That sounds obvious, but it is still where many AI projects fall apart. Another part of our conversation focuses on trust, which feels especially relevant right now. From scams and impersonation to consumer fatigue and poor automation, brands are under pressure to move faster without losing credibility. Peter shares how Twilio is thinking about branded calling, RCS, conversational AI, and voice experiences that feel modern without becoming intrusive or robotic. We also discuss why too many companies still automate too broadly, too quickly, without defining the actual use case first. What I enjoyed most here was Peter’s balanced view. He is optimistic about where AI is heading, but he is also realistic about the work still required to get there. This is not a conversation about AI magic. It is about data access, governance, trust, brand experience, and the standards that may quietly shape the next phase of AI adoption far more than the flashy headlines. So if you have been hearing more people mention MCP and wondering why it matters, or if you are trying to understand what needs to happen before enterprise AI can move from promise to practical value, this episode will give you plenty to think about. Is Model Context Protocol the missing layer that finally helps AI connect with the real world of business data? /episode/index/show/techblogwriter/id/40648930

Invisible Technologies CEO On Building AI Around Real Workflows, Not Hype 04/13/2026

Invisible Technologies CEO On Building AI Around Real Workflows, Not Hype What does it actually take to make AI work inside a real business, where messy data, human judgment, and operational risk all collide? In this episode, I sit down with Matt Fitzpatrick, CEO of Invisible Technologies, to talk about why the biggest barrier to enterprise AI is not model quality, it is everything that comes before the model ever gets to work. Since stepping into the CEO role in January 2025, Matt has moved quickly, raising $100 million and expanding Invisible’s footprint across major cities including New York, San Francisco, DC, Austin, London, and Poland. But this conversation is far less about headlines and far more about what happens in the trenches of AI adoption, where companies are trying to move from pilots and PowerPoint promises to systems that actually deliver results. A huge theme throughout our discussion is data readiness. Matt makes a compelling case that most businesses are still dealing with fragmented systems, inconsistent records, and information spread across disconnected tools. That reality makes it incredibly hard to deploy AI in a way that creates trust and value. We talk about SwissGear, where Invisible used its Neuron platform to clean and structure 750 scattered tables in just one week, a task that could have taken a large engineering team months or longer. We also discuss why that kind of work matters so much, because once the data foundation is fixed, companies can start making better decisions on forecasting, operations, and planning with a level of confidence that simply was not there before. We also spend time on Invisible’s human-in-the-loop approach, which I think will resonate with a lot of listeners trying to cut through the noise around job displacement and agentic AI. Matt argues that the real opportunity is not replacing people, but giving them better tools to handle repetitive work while preserving room for human expertise, judgment, and oversight. He shares examples from commercial credit workflows, healthcare, and sports analytics, including a fascinating story about the Charlotte Hornets using AI to turn broadcast footage into detailed tracking data. What stood out to me was how practical his perspective felt. This was not theory. It was about building systems around how organizations actually work, rather than expecting businesses to reshape themselves around a generic AI product. Another part of the conversation that deserves attention is governance. As boards rush to understand agentic AI, Matt explains why trust, standards, and responsible deployment are now driving buying decisions just as much as raw capability. We talk about privacy in healthcare, the risks of scaling autonomous systems without mature governance, and why enterprise adoption still trails consumer AI by a wide margin. That gap between excitement and execution may be one of the most important stories in AI right now. If you are wondering why so many AI projects never make it into production, or what it will take for enterprise AI to finally deliver on its promise, this episode is packed with insight. It is a conversation about data, deployment, governance, and the role humans will continue to play as AI becomes part of everyday business operations. After listening, I would love to know where you stand, is the future of AI really about bigger models, or is it about making AI fit the messy reality of how work gets done? /episode/index/show/techblogwriter/id/40812860

Willow On How AI Is Changing The Way Buildings Operate 04/12/2026

Willow On How AI Is Changing The Way Buildings Operate In this episode, I speak with Bert Van Hoof, CEO of Willow, about how AI is starting to reshape the built world in ways that go far beyond smart dashboards and efficiency reports. Bert brings decades of experience from the front lines of digital infrastructure, including his time at Microsoft, where he helped create Azure Digital Twins and Smart Places. Today at Willow, he is focused on a much bigger idea, using AI to help buildings, campuses, hospitals, airports, and other complex environments operate with greater intelligence, lower waste, and better outcomes for the people who rely on them every day. One of the most interesting parts of our conversation is how Bert explains the shift from passive building software to active management systems. For years, many digital twin and smart building tools were good at showing what had already happened. But operators do not need another screen full of charts. They need systems that can connect live data, static records, spatial context, and operational history to help them make better decisions in real time. That is where Willow comes in, creating a digital foundation where AI can reason across everything from HVAC and air quality to occupancy, refrigeration, maintenance history, and even energy usage patterns. We also unpack why this matters right now. Energy costs remain under pressure, sustainability goals are getting harder to ignore, and many organizations are still stuck with fragmented systems that do not talk to each other. Bert shares how AI can help move building teams from reactive maintenance to predictive performance, spotting issues earlier, cutting downtime, reducing waste, and extending the life of expensive assets. He also explains why the future of building operations will depend on a stronger data foundation, operational AI copilots, and systems that can support an aging workforce while making these roles more appealing to the next generation. What stood out for me was how practical this all became once we moved past the buzzwords. This was not a conversation about futuristic hype. It was about real examples, from occupancy-based HVAC control in offices and campuses to leak detection in schools, vaccine refrigeration monitoring, and hospital environments where downtime can carry enormous consequences. Bert makes a strong case that buildings are no longer just static structures. They are living operational environments filled with signals, systems, and opportunities that have been hiding in plain sight. We also touch on the wider picture, including what Bert learned from smart cities and energy grid modernization, and how those lessons now apply to commercial real estate, airports, research labs, and higher education campuses. There is a real sense that the physical world is entering a new chapter, one where AI starts to bridge the gap between digital intelligence and real-world action. If you have ever wondered what AI looks like when it leaves the screen and starts improving the places where people work, heal, travel, learn, and live, this episode will give you plenty to think about. As always, I would love to know what you think, are buildings finally ready to become truly responsive, and what opportunities or risks do you see ahead? /episode/index/show/techblogwriter/id/40624985

Blumberg Capital On What Investors Really Want From AI Founders Now 04/11/2026

Blumberg Capital On What Investors Really Want From AI Founders Now What does it really take to build the next generation of AI companies when the hype around scale begins to fade and real-world impact takes center stage? In this episode, I sit down with David Blumberg, founder and managing partner at Blumberg Capital, to unpack what he believes will define the next wave of AI startups. With a track record that includes being the first investor in companies like Nutanix, Braze, and DoubleVerify, David brings a perspective shaped by decades of identifying breakout innovation early. But what stood out most in our conversation was his belief that 2026 marks a turning point where intelligence moves beyond experimentation and becomes operational. We explore what that shift actually means in practice. David explains how AI is evolving from systems that generate insights into systems that take action, and why that distinction matters for founders, investors, and enterprise leaders alike. He shares how the most compelling startups today are not simply layering AI onto existing products, but embedding it deeply into workflows across industries like finance, security, and supply chain. These are companies built on proprietary data and real operational context, designed to make decisions with precision rather than simply process information. Our conversation also challenges some widely held assumptions about success in the AI space. David makes it clear that scale alone will not separate winners from the rest. Instead, the focus is shifting toward accuracy, reliability, and domain expertise. Founders who have lived the problems they are solving, rather than approaching them from the outside, are far more likely to build something defensible and lasting. It is a subtle shift, but one that could redefine how value is created in the years ahead. There is also a broader discussion about where investment is flowing and why. With the vast majority of companies Blumberg Capital now evaluates being rooted in AI, the bar for differentiation is rising fast. David offers insight into what his team is really looking for in founders entering this next cycle, and how startups can stand out in an increasingly crowded field. So as AI moves from promise to execution, and from experimentation to real-world outcomes, the question becomes harder to ignore. Are we ready to rethink how we measure success in the AI era, and what kind of companies will truly earn their place at the top? /episode/index/show/techblogwriter/id/40570530

AI Psychosis Explained With Dr. Ragy Girgis From Columbia University 04/10/2026

AI Psychosis Explained With Dr. Ragy Girgis From Columbia University How do we talk about artificial intelligence without ignoring the very human consequences it can have on our mental health? In this episode, I sit down with Dr. Ragy Girgis, Professor of Clinical Psychiatry at Columbia University, to unpack a topic that has quietly moved from the fringes of academic discussion into mainstream headlines. You have probably seen the term “AI psychosis” appearing more frequently, often surrounded by speculation, fear, or misunderstanding. But what does it actually mean, and how should we be thinking about it as these technologies become part of everyday life? Ragy brings a clinical and deeply considered perspective to the conversation. He explains that what we are seeing is not AI creating entirely new delusions out of thin air, but something more subtle and arguably more concerning. Large language models can reflect and reinforce ideas that already exist within a person’s mind. For someone already vulnerable, that reinforcement can push a belief from uncertainty into absolute conviction. That shift, even if small, can have life-altering consequences. It raises uncomfortable questions about how persuasive technology interacts with fragile mental states. We also explore the comparison many people make with older internet rabbit holes, and why this new generation of AI tools feels different. There is something about conversational systems that mimic human interaction so convincingly that they can blur the line between reflection and validation. Ragy introduces a powerful analogy rooted in the story of Narcissus, which reframes the issue in a way that feels both timeless and unsettling. It is not about an external voice planting ideas, but about a mirror that becomes impossible to look away from. But this conversation is not about fear. It is about responsibility and awareness. We discuss practical steps that could help reduce risk, from how AI systems communicate their limitations, to the role of families and clinicians, and even the responsibility of tech companies to invest in research around early warning signs. There is a sense that we are only at the beginning of understanding this phenomenon, and that the decisions made now will shape how safely these tools evolve. So as AI continues to move closer to us, speaking in our language and responding in real time, how do we make sure it supports human wellbeing rather than quietly amplifying our most vulnerable moments? Useful Links Visit the May Sponsors of Tech Talks Network and learn more about the /episode/index/show/techblogwriter/id/40486735

Flexera: Why 2026 Is AI’s ‘Back to Basics’ Moment 04/09/2026

The Lucid Software Playbook For Aligning People, Process, And AI 04/08/2026

The Lucid Software Playbook For Aligning People, Process, And AI How do you bring people together to do better work when everything around them feels increasingly complex, distributed, and uncertain? In today’s episode, I sat down with Jessica Guistolise from Lucid Software, and what struck me straight away was her belief that work has always been a group project, even if many organizations still behave as though it is not. Jessica shared how much of the friction we experience at work comes from misalignment, unclear expectations, and a lack of shared understanding. When teams are spread across time zones, systems, and now AI-powered workflows, those gaps only widen. Her perspective is simple but powerful. When people can actually see the work, rather than interpret it through documents, meetings, or assumptions, something shifts. Conversations become clearer, decisions become faster, and collaboration starts to feel human again. We also explored how visual collaboration platforms like those from Lucid Software are helping teams move away from scattered tools and disconnected workflows toward a more unified way of working. Jessica described it as having everything on one workbench, where teams can brainstorm, plan, and execute without constantly switching context. What really stayed with me was her focus on inclusivity in collaboration. Not everyone contributes in the same way, and visual environments can create space for different thinking styles, whether someone is outspoken, reflective, or somewhere in between. That idea of creating a shared language across teams, roles, and even personalities feels increasingly relevant in a world where communication often breaks down. Of course, no conversation right now would be complete without talking about AI. Jessica offered a refreshingly honest view. There is uncertainty, and there should be. But rather than avoiding it, she believes leaders need to make AI visible, map how it is used, define where human judgment matters, and encourage teams to experiment openly. One of the most interesting ideas she shared was reframing mistakes as early learnings. When teams feel safe to test, fail, and share what they discover, progress accelerates. When fear or blame enters the picture, everything slows down. We also touched on AI literacy and what it really means in practice. For Jessica, it comes down to clarity. Clear workflows, clear guardrails, and clear expectations about accountability. AI might assist, but humans remain responsible for outcomes. That mindset, combined with leadership that actively participates in experimentation, creates an environment where people feel confident stepping forward rather than holding back. This conversation left me thinking about how many organizations are still trying to layer AI onto unclear processes and expecting better results. Jessica’s message is that clarity comes first, then technology can amplify it. So if work really is a group project, are we giving our teams the visibility and confidence they need to succeed, or are we still asking them to figure it out in the dark? /episode/index/show/techblogwriter/id/40486270

EvoluteIQ On Rethinking ROI In The Age Of Enterprise AI 04/07/2026

EvoluteIQ On Rethinking ROI In The Age Of Enterprise AI What happens when the very pricing model meant to speed up AI adoption ends up slowing it down? In this episode of Tech Talks Daily, I sit down with Sameet Gupte, CEO and co-founder of EvoluteIQ, to discuss a part of the enterprise AI story that still doesn't get enough attention. While so much of the conversation around AI focuses on models, copilots, and the latest agentic promises, Sameet brings the discussion back to a business reality that every enterprise leader understands. If the economics do not work, adoption stalls. And if success in a pilot makes the final rollout even more expensive, something has gone wrong long before the board signs off on scale. Sameet argues that many organizations are still trapped by legacy pricing structures built for an earlier generation of automation. Per-user and per-bot pricing may look manageable at the pilot stage. Once a company tries to expand automation across departments, processes, and geographies, the numbers can quickly stop making sense. That creates what many now call pilot purgatory, where a company proves something can work, but cannot justify taking it any further. It is a problem rooted in incentives, procurement, and fragmented technology stacks, and it is one that CFOs are watching very closely. What I found especially interesting in this conversation is how Sameet frames the issue. He believes most enterprises do not actually have an automation problem. They have an orchestration problem. In other words, the challenge is rarely a lack of tools. It is getting all the systems, workflows, approvals, data flows, and legacy infrastructure to work together to produce a clean business outcome. That idea changes the conversation from buying isolated features to rethinking the process as a whole. We also discuss why outcomes-based pricing is increasingly resonating with enterprise buyers. Sameet explains why predictable costs, transparent commercial models, and shared accountability are helping move automation conversations out of innovation teams and into the CFO’s office. For public companies and large global enterprises, that matters. Leaders want fewer surprises, fewer overlapping vendors, and a much clearer line between spend and return. There is also a broader theme running through this episode about where the market is heading next. Sameet sees real urgency around vendor consolidation, enterprise simplification, and the need to rethink how AI is introduced into the business. His view is that companies need to pause, define what they actually want AI to do, and then choose tools that fit the business, rather than reshaping the business around the latest platform pitch. If you are trying to make sense of AI adoption beyond the hype, this conversation offers a practical and timely perspective on pricing, scale, and what real transformation could look like inside the enterprise. After listening, do you think the future of enterprise AI will be shaped as much by commercial models as by the technology itself, and what are you seeing in your own organization? Useful Links /episode/index/show/techblogwriter/id/40697285

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