Tech Talks Daily

How do you move beyond AI experimentation and start building systems that can genuinely reason, act, and create value across an enterprise? Recorded at Adobe Summit in Las Vegas, this episode features Daniel Sheinberg, who leads cross-portfolio product initiatives for Adobe's Customer Experience Orchestration business. Daniel is at the center of Adobe's AI and agentic strategy, helping shape how some of the world's largest organizations think about the next generation of customer experiences. During our conversation, Daniel cuts through the hype surrounding agentic AI and explains what actually separates an AI assistant from an AI agent. We explore how advances in reasoning, memory, context awareness, and tool usage are enabling systems that can move beyond generating content to actively helping organizations achieve business goals. Daniel shares practical examples of how enterprises are using these capabilities to personalize customer journeys at a level that would have been impossible with traditional workflows. We also discuss the rise of AI-powered brand concierges, including how are using agentic experiences to create more meaningful customer interactions. Daniel explains why context is becoming one of the most valuable assets in enterprise AI, how businesses can prepare their data and systems for agentic workflows, and why governance, trust, and brand intelligence will play such an important role in successful deployments. If you're trying to understand where AI is heading next, what customer experience orchestration really means, and how businesses can safely deploy agentic AI at scale, this conversation offers a valuable look at both the opportunities and the challenges ahead.

What does better analytics actually mean when your mission is protecting the planet? At SAS Innovate, I sat down with John Blackwell, Director of Strategic Analytics at The Nature Conservancy, to explore how data, AI, and marketing intelligence are helping one of the world's largest conservation organizations raise more money, operate more efficiently, and ultimately direct more resources toward protecting land, water, and ecosystems across more than 80 countries. In this episode, John explains how analytics has become a critical part of modern conservation strategy. With fundraising supporting everything from habitat protection to climate resilience projects, improving donor retention and increasing fundraising efficiency directly impacts how much work The Nature Conservancy can do around the world. John shares how the organization improved donor retention by 10 percent and increased year-over-year giving by 30 percent by moving away from siloed systems and toward a more connected, data-driven approach. We discuss how analytics helps identify long-term donor potential, personalize supporter journeys, optimize fundraising asks, and create a clearer 360-degree view of donor engagement across email, direct mail, telemarketing, and digital channels. John also explains why personalization in the nonprofit world requires a very different balance than in commercial marketing. Trust and authenticity matter just as much as performance metrics. The conversation also explores how AI is quietly changing the way nonprofit analytics teams operate. From speeding up model development to improving feature selection and identifying rare high-value donor opportunities through synthetic data generation, John shares where he sees AI creating immediate practical value without compromising the human voice of the organization. What stood out most to me is how this is ultimately a story about efficiency creating impact. The more effective The Nature Conservancy becomes at fundraising and donor engagement, the more money can go directly toward conservation rather than operational overhead. And in a world where every nonprofit is competing for attention, funding, and trust, analytics may quietly become one of the most important tools available for protecting the future of our planet. So, as organizations continue investing in AI and analytics, are they focusing enough on the real-world outcomes those technologies can help create?

What happens when one of the world's fastest-growing travel platforms decides the future of business travel will be built around AI from the ground up? In this episode of Tech Talks Daily, I sat down with Navan co-founder and CTO Ilan Twig to discuss how the company is reshaping travel, payments, and expense management through AI-native systems designed for the real world, not just polished demos. What immediately stood out during our conversation was Ilan's mix of technical obsession and relentless focus on user experience. This is someone who isolated himself for months to truly understand the mechanics of large language models before most companies had even worked out what ChatGPT meant for their business. That curiosity now powers Navan's AI strategy, where conversational interfaces are replacing what Ilan calls the old "forms and tables" model of software interaction. We explored how Navan's AI assistant, Ava, is already handling thousands of real-world travel support conversations every day, with customer satisfaction scores that rival those of human agents. During major disruption events like Storm Fern and the Heathrow airport fire, Ava scaled instantly, resolving huge volumes of customer requests without the delays and staffing nightmares that traditionally overwhelm travel providers. But this conversation goes much deeper than travel. Ilan shared his thoughts on why the software industry is moving toward conversational, context-aware interfaces, why most businesses still misunderstand what agentic AI actually means, and how Navan is building proprietary models trained on its own travel data to outperform larger, generic frontier models. We also discussed trust, hallucinations, AI supervision layers, and why companies must stop treating AI as a magic trick and start measuring it against hard business outcomes. There is also a fascinating human side to this episode. From building a company through market turbulence, investor skepticism, and geopolitical uncertainty, to challenging accepted thinking since his school days, Ilan's story reflects the mindset of someone who genuinely believes technology should solve real problems rather than create headlines. If you have been wondering where AI moves beyond hype and starts delivering measurable operational value, this conversation offers a rare look behind the curtain from someone building these systems at scale every single day. Useful Links Connect with Ilan Twig Learn more about Navan Check out blog posts by Navan Follow Navan on LinkedIn Visit our Sponsors Check out the Nordlayer Browser Learn more about Denodo Data Products  

What happens when AI systems stop acting like assistants and start acting like autonomous decision-makers inside your business? And if those systems are pulling information from fragmented, inconsistent, and poorly governed data environments, how much trust can organizations really place in the outcomes? In today's episode, I'm joined by Terry Dorsey for a fascinating conversation about the growing gap between AI ambition and enterprise reality. Terry brings decades of experience spanning enterprise architecture, business intelligence, operations, healthcare, utilities, manufacturing, and defense. Long before AI became the headline topic dominating every boardroom conversation, he was already working deeply in semantic modeling, natural language systems, and the architectural foundations that modern AI now depends on. At the center of our discussion is the new AI Trust Gap report from Denodo, which reveals why so many organizations are struggling to move AI projects from experimentation into reliable production environments. We explore why live data matters so much in an agentic AI world, why "more data" often creates more confusion instead of clarity, and how inconsistent business meaning across systems quietly undermines AI trust inside large organizations. Terry explains why many enterprises are still operating on architectures originally designed for historical reporting and analytics, while now expecting those same environments to support autonomous AI systems making real-time operational decisions. From semantic sprawl and duplicated business logic to governance failures and fragmented security models, we unpack the hidden technical debt that AI is now exposing at scale. The conversation also takes a deeper philosophical turn as we discuss why enterprise meaning itself may become the future control plane for AI. Terry shares why provenance, explainability, and semantic consistency are no longer optional concerns reserved for compliance teams, they are becoming foundational requirements for trustworthy AI systems capable of operating autonomously. We also discuss why governance cannot be bolted on after deployment, how logical data management helps organizations reduce duplication and maintain operational trust, and why the companies that succeed with agentic AI will not necessarily be the fastest movers, but the ones building stable and reusable architectural foundations beneath the surface. If your organization is rushing toward AI adoption while wrestling with siloed systems, disconnected data, and growing governance concerns, this episode offers a much-needed reality check. Because, as Terry explains, the future competitive advantage may have less to do with the AI model itself and far more to do with the architecture, meaning, and trust frameworks supporting it. Useful Links Terry Dorsey LinkedIn Denodo LinkedIn Denodo Website The AI Trust Gap Report — global survey of 850 executives that explores why organizations are investing heavily in AI, but many still can't fully trust the data behind it. O'Reilly's The Rise of Logical Data Management, by Christopher Gardner — explains what's necessary to enable true self-service data access and 24/7 AI-ready data. The Enterprise AI and Data Management Glossary  — glossary that helps ensure both technical and non-technical professionals can make informed decisions, optimize strategies, and align on best practices for digital transformation. The ROI of Using the Denodo Platform alongside the Modern Data Lakehouse — Drawing on interviews with numerous global enterprises and applying a comprehensive ROI methodology, this study, conducted by independent analyst Veqtor8, found that by using Denodo alongside their data lakehouse, they realized considerable benefits. Agentic AI Manifesto — a blueprint for credible autonomy at enterprise scale. Denodo's standard for the next era of trusted, autonomous enterprise AI.

What does AI transformation actually look like inside one of the world's largest engineering organizations? At Team '26 in Anaheim, I recently sat down with Jason Andrews to unpack how Cisco transformed decades of fragmented tooling, disconnected workflows, and spreadsheet-driven operations into a unified system of work built around Jira, Confluence, Jira Service Management, automation, and AI-ready workflows. And honestly, this conversation felt refreshingly practical. Jason oversees engineering operations across Cisco Networking, a business unit with around 22,000 engineers and product managers representing roughly $40 billion in annual revenue. So when he talks about transformation, this isn't theory. This is operational change happening at enterprise scale. We discuss how Cisco consolidated more than 85 Jira instances, reduced tooling spend by 54%, and accelerated reporting by 40x while creating a far more scalable engineering organization. But as Jason explains throughout the conversation, the real challenge was never the technology itself. It was getting teams to rethink how they wanted to work moving forward rather than simply migrating years of technical debt into modern systems. One of the strongest themes in this episode is the difference between transformation and migration. Jason explains why organizations often fail when they focus only on moving systems rather than changing workflows, behaviors, and operational culture at the same time. We also dive deep into AI adoption inside engineering organizations. Jason shares how Cisco is already seeing significant productivity gains from AI-assisted development, why organizational context matters so much for enterprise AI success, and why he believes the industry is still massively underestimating how much structured data and workflow consistency AI systems actually require. Along the way, we unpack scenario planning in the AI era, why annual planning cycles are becoming increasingly fragile, and how leaders can move from rigid long-term roadmaps toward more agile operational playbooks capable of adapting to constant disruption. There's also a fascinating discussion around the so-called "SaaS apocalypse," the limits of AI-generated software, and why Jason believes humans will remain central to enterprise operations for years to come, especially in organizations managing millions of lines of legacy code and decades of accumulated institutional knowledge. If your organization is currently navigating modernization, operational complexity, AI adoption, or large-scale systems transformation, this episode is packed with lessons learned from the front lines of enterprise change. And perhaps most importantly, Jason offers a reminder that AI alone is not the strategy. The real opportunity comes from reducing friction, improving context, and helping teams spend more time solving meaningful problems instead of manually stitching systems together.

What Happens When AI Starts Protecting the Power Grid Before Humans Even Spot the Problem? In this episode of Tech Talks Daily, I speak with Kaitlyn Albertoli, co-founder and CEO of Buzz Solutions, about how AI, drones, and computer vision are changing the way utilities inspect and maintain power infrastructure. As weather events become more frequent and energy demand continues to rise from EV adoption, renewable energy growth, and AI-driven data centers, utilities are under growing pressure to modernize systems that were built decades ago. Kaitlyn explains how utilities once relied on crews walking transmission lines with binoculars and handwritten notes before moving toward helicopter inspections and aerial imaging. Today, autonomous drones and aircraft can capture hundreds of thousands of inspection images every year. The real challenge now is turning that mountain of visual data into useful action before damaged equipment leads to outages, fires, or safety risks. We discuss how Buzz Solutions processes enormous image datasets in hours instead of weeks, helping utilities identify damaged insulators, corrosion, vegetation risks, and failing components before they become larger problems. We also talk about the people behind the infrastructure. Kaitlyn shares why AI should support frontline workers rather than replace them, especially as utilities face an estimated shortage of thousands of skilled linemen over the next several years. The conversation covers balancing false positives with missed detections, reducing operational data silos, and why partnerships with companies like Skydio and Esri are helping utilities connect inspection workflows more effectively. Kaitlyn also shares how Buzz Solutions is expanding into solar inspections, where AI can detect damaged or underperforming panels before warranties expire and energy production quietly drops over time. Alongside the technology discussion, she reflects on how competing in the 2012 U.S. Olympic Trials shaped the resilience and mindset she now brings to building a fast-growing AI company. From wildfire prevention and storm recovery to renewable energy operations and autonomous inspections, this episode looks at how AI is quietly becoming part of the infrastructure keeping modern society running. As utilities modernize aging systems under growing environmental and operational pressure, can AI help prevent the next major outage before it happens?

What happens when the cybersecurity industry stops debating whether agentic AI is a future problem and starts treating it as a present-day reality? In this episode of Tech Talks Daily, I sit down with Tim Freestone to unpack the biggest shift coming out of this year's RSA Conference. After attending RSA for more than two decades, Tim describes 2026 as the year the energy returned to the cybersecurity world, driven by one unavoidable topic: agentic AI. We explore why the conversation has rapidly evolved from curiosity to urgency, and why organizations are suddenly confronting an uncomfortable truth. AI agents are already operating inside businesses, often without visibility, governance, or control. Tim explains how shadow AI is spreading faster than many leadership teams realize, with employees experimenting with autonomous tools that connect directly to company data and external AI models. Our conversation also looks at the growing gap between visibility and control. Security teams may be discovering agents across their networks, but stopping risky behavior is an entirely different challenge. Tim argues that companies focusing purely on infrastructure are already falling behind, and that the real battleground is now the data layer itself. We discuss why data governance, audit trails, and access controls are becoming central to the future of cybersecurity strategy. Tim also shares his thoughts on state-sponsored AI threats, the rise of autonomous espionage operations, and why open-source AI models present a completely new level of risk for defenders. At the same time, he offers practical advice for IT and security leaders trying to figure out where to start amid the noise, complexity, and endless flood of new tools entering the market. If your organization is trying to understand how AI changes cybersecurity, governance, compliance, and risk management, this conversation offers a clear look at what security leaders are actually worried about right now, and why the next 12 months may redefine how companies think about protecting data altogether. Useful Links Connect with Tim Freestone Learn More About Kiteworks Data Security and Risk Report Kiteworks Substack Kiteworks LinkedIn Newsletter Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What if some of the most important applications of AI today have nothing to do with productivity, marketing, or enterprise automation, and everything to do with helping people survive crisis, displacement, and uncertainty? In this episode, recorded at, I sit down with André Heller Pérache to explore how technology originally designed for customer service has evolved into humanitarian infrastructure supporting refugees and displaced communities around the world. André shares the story behind Signpost, a global digital initiative from the International Rescue Committee that now operates across roughly 30 countries and 25 languages, helping register more than 20 million users while supporting over 500,000 digital social work consultations. But this conversation goes much deeper than technology. We discuss what happens when trusted information becomes as important as food, shelter, or medical support during times of crisis. André explains how Signpost was born from the realization that vulnerable communities were already living digitally through smartphones, WhatsApp, Facebook, and social platforms while much of the humanitarian sector still relied on traditional offline systems. We also explore the responsible use of AI in high-stakes environments where mistakes can have real-world consequences for refugees, families, and vulnerable populations. André shares why the IRC sees AI as one of the humanitarian sector's biggest bets at a time when armed conflict, climate disasters, and shrinking budgets are putting enormous pressure on aid organizations globally. From misinformation and trust to reducing cognitive burden and scaling empathy through technology, this episode offers a powerful reminder that behind every AI conversation are ultimately human beings searching for dignity, safety, clarity, and hope.

What if the future of AI is not one all-knowing assistant, but an entire workforce of specialized agents working together behind the scenes? Recorded st Zendesk Relate, this episode features a fascinating conversation with Shashi Upadhyay about where enterprise AI is really heading, and why many businesses are still underestimating the scale of operational change required to make agentic AI work. Shashi explains why Zendesk views AI agents as a new form of digital labor rather than simply another software feature. Instead of building one giant general-purpose assistant, Zendesk is developing coordinated networks of specialized agents designed for specific business functions such as billing, collections, refunds, returns, employee service, and industry-specific workflows across sectors like healthcare, banking, and e-commerce. We also go behind the curtain inside Zendesk itself. Shashi shares how the company has transformed internally from a traditional seat-based SaaS business into an organization focused on measurable outcomes such as automation rates, customer satisfaction, and successful resolutions. He also discusses how AI is changing software development itself, enabling smaller engineering teams to move dramatically faster while reshaping how products are designed and built. The conversation explores some of the biggest themes emerging across the AI industry right now, including outcome-based pricing, AI trust and guardrails, resolution learning loops, embedded AI, and the growing shift toward agent-to-agent interactions where personal AI assistants may eventually negotiate directly with enterprise AI systems on behalf of consumers. We also discuss the fears many people have around jobs and automation. Rather than predicting catastrophic job loss, Shashi argues there is still enormous unmet demand for better service experiences, and that AI may ultimately allow businesses to finally deliver the level of customer experience people have wanted for years. If you're trying to understand where enterprise AI moves next after copilots and chatbots, this conversation offers a clear and thought-provoking look at the systems, workflows, and cultural shifts already reshaping the future of work.

What if the cybersecurity industry has spent decades fighting the wrong battle? In this episode of Tech Talks Daily, I sat down with Benny Czarny, founder and CEO of OPSWAT, to discuss why he believes the traditional "detect and respond" model is no longer enough in a world where AI is accelerating cyber threats faster than security teams can react. Benny joined me to discuss his new book, Cybersecurity Upside Down, which combines personal stories from building OPSWAT with a bold argument for rethinking how organizations approach cyber defense altogether. His central belief is simple but provocative: detection-based security has trapped the industry in a losing cycle in which attackers need to succeed only once, while defenders are forced into a constant state of reaction. During our conversation, Benny explained how his thinking evolved after realizing that even layering dozens of antivirus engines and sandboxing technologies still failed to stop malicious files reliably. That realization ultimately pushed him toward a prevention-first philosophy built around Deep Content Disarm and Reconstruction, or CDR. Rather than trying to determine whether a file is malicious, the approach assumes files may already be dangerous and regenerates clean, safe versions before they ever reach users or systems. We also explored how generative AI is changing the cybersecurity landscape in ways many organizations still underestimate. Benny shared why AI is dramatically reducing the time required to create malware, weaponize exploits, and scale attacks, effectively giving even inexperienced attackers capabilities once reserved for nation states or advanced cybercriminal groups. He also raised concerns that AI data lakes could become contaminated with malicious content, creating entirely new risks for organizations rushing to deploy large language models without securing the data feeding them. One of the most fascinating aspects of the discussion was the psychology and culture within cybersecurity teams. Benny argued that the industry often celebrates visible incident response activity while undervaluing quiet prevention. In a world dominated by alerts, dashboards, and SOC metrics, truly preventing attacks can almost appear invisible, despite potentially delivering far greater security outcomes. We also talked about the sectors Benny believes are most exposed today, including energy, manufacturing, and critical infrastructure operators that still rely heavily on reactive security models while facing growing operational and regulatory complexity. He explained why some industries are advancing faster than others and why compliance mandates could become a major catalyst for broader prevention-first adoption. Beyond cybersecurity itself, this episode also offered a fascinating look into Benny's entrepreneurial journey, what he learned building OPSWAT over two decades, how AI helped him research and structure his book, and why he is now even producing a cybersecurity-focused TV series called Into the Breach, designed to make complex security concepts easier for wider audiences to understand. This conversation challenges many of the assumptions the cybersecurity industry has normalized for years. Whether you work in security, IT leadership, compliance, or want to understand how AI is reshaping digital risk, this episode offers a very different perspective on what modern cyber resilience could look like in practice.

What happens when customer service stops being a department and starts becoming an autonomous operational system? Recorded live at, this conversation with Tom Eggemeier goes far beyond chatbots, copilots, and AI hype cycles. Instead, we explore why Zendesk believes the future of enterprise service will be built around what it calls an "autonomous service workforce," where AI agents, human experts, workflows, analytics, governance, and orchestration layers all work together as one continuously learning system. Tom shares how Zendesk transformed its own internal operations using AI, achieving more than 60% autonomous resolution rates while simultaneously increasing customer satisfaction. We also discuss why the company is shifting away from measuring ticket deflection and toward measuring actual resolutions, what the Forethought acquisition means for Zendesk's long-term AI strategy, and why governance, permissions, and operational trust may become more important than the AI models themselves. But this episode is about much more than software. Tom explains why he believes the next phase of enterprise AI will fundamentally reshape workflows, organizational structures, and even the role humans play inside modern businesses. We unpack the rise of specialized AI agents, why AI-to-AI interactions could soon outnumber human interactions, and why many organizations are underestimating the operational redesign required to make agentic AI work at scale. We also discuss the hidden risks of fragmented AI systems, why disconnected tools continue to drain businesses, and how companies can balance autonomy with human oversight and empathy. If you've been wondering where enterprise AI is really heading beyond the headlines, this conversation offers a fascinating look at how one of the biggest players in customer experience is attempting to redefine service itself.

What happens when AI intelligence becomes commoditized? That is the question sitting at the heart of this episode recorded live at Team '26 in Anaheim, where I sat down with Sherif Mansour to unpack one of the biggest shifts happening in enterprise technology right now. For years, the AI conversation has focused on models, prompts, and raw capability. But according to Sherif, the real competitive advantage may no longer come from the intelligence itself. It comes from context. The workflows, relationships, decisions, knowledge, and operational history that exist inside an organization. In this conversation, Sherif takes me deep inside Atlassian's biggest AI announcements around Rovo, Teamwork Graph, AI-powered workflows, and the company's broader vision for what happens when AI moves beyond isolated copilots and starts operating across the flow of work itself. We explore why Atlassian believes organizational context is becoming the defining moat in enterprise AI, why the company is opening Teamwork Graph through MCP and external integrations, and how the industry is rapidly shifting from AI experimentation toward real operational execution. Sherif also myth busts some of the biggest misconceptions surrounding AI adoption today. We discuss the difference between automation and orchestration, why humans still remain central to decision-making, and how enterprises can avoid adding complexity while still moving quickly in the AI era. Along the way, we discuss real-world examples ranging from Formula One race strategy and procurement workflows through to AI-powered onboarding, engineering productivity, and the growing role of agentic systems inside large organizations. One of the most fascinating parts of the discussion centers around the evolution of enterprise software itself. Atlassian no longer sees AI as a standalone assistant sitting in a chat window. Instead, the vision is for AI to become deeply embedded into workflows, helping teams coordinate work, surface insights, and accelerate decision-making in real time. Sherif also shares why he believes the next major platform battle will not be over who owns the smartest AI model, but over who owns the operational context surrounding that intelligence. If you're trying to separate real enterprise AI progress from the hype cycle, this episode offers a thoughtful and refreshingly honest look at where things may actually be heading next. As always, I'd love to hear your thoughts. Is organizational context becoming the real competitive advantage in AI? And how prepared is your business for a future where humans and AI agents increasingly work side by side? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What if the biggest limitation holding AI back isn't the model, the data center, or the algorithm, but the fact that most physical objects in the world still cannot communicate digitally? In this episode of Tech Talks Daily, I sat down with Richard Price, CTO and co-founder of Pragmatic Semiconductor, to explore why AI systems remain "half blind" to the physical world and what happens when everyday objects finally become intelligent, connected, and verifiable data sources. Richard shared how Pragmatic Semiconductor is taking a radically different approach to chip design by creating flexible, ultra-thin semiconductors built specifically for item-level intelligence. Rather than competing directly with traditional silicon, Pragmatic is designing lightweight, low-cost electronics that can integrate directly into packaging, labels, healthcare patches, wearable devices, and products that conventional chips cannot support economically or physically. During our conversation, we unpacked why the long-promised "Internet of Everything" has remained frustratingly out of reach for so many years. Richard explained that while silicon has powered decades of incredible innovation, scaling connectivity to billions or even trillions of everyday objects introduces major cost, energy, and sustainability challenges. Pragmatic's flexible semiconductor technology aims to solve that by reducing manufacturing complexity, lowering environmental impact, and enabling intelligence directly at the edge. We also discussed how embedding intelligence at the item level could reshape supply chains, sustainability initiatives, healthcare systems, and even consumer trust. From reducing food waste through smarter logistics to enabling wearable healthcare sensors with entirely new form factors, Richard painted a picture of a future where physical products can actively communicate their identity, condition, and history in real time. One of the most fascinating parts of the conversation centered on how businesses should prepare for this shift. As edge intelligence grows, organizations may need to rethink traditional cloud-heavy architectures and start designing systems in which decisions occur closer to the object itself. Richard explained how this could reduce latency, lower energy usage, and unlock entirely new categories of connected products. We also explored the sustainability side of semiconductor manufacturing at a time when AI infrastructure and hyperscale data centers are drawing increasing scrutiny for their energy and environmental impact. Richard shared how Pragmatic's thin-film manufacturing approach uses fewer chemicals, less water, and lower-temperature processes, while opening the door to more environmentally conscious digital infrastructure. Toward the end of the episode, Richard offered insight into some of the most exciting real-world applications already emerging, including healthcare patches, wearable sensing technologies, AR and VR devices, and electronics that could eventually conform to the human body itself. It is the kind of conversation that makes you rethink what a semiconductor can actually be. If you've ever wondered what comes after smartphones and smart devices, this episode offers a fascinating look at how flexible electronics could quietly become the foundation for the next generation of connected intelligence. Useful Links Connect with Richard Price Learn More About Pragmatic Semiconductor Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What happens when an Air Force engineer with experience in intelligence, venture capital, and deep tech startups starts applying quantum-inspired computing to some of the hardest problems in aerospace and defense? In this episode of Tech Talks Daily, I sat down with Nathan Mason, VP of Strategic Growth at BQP, to unpack how quantum-inspired software is already helping organizations solve massive computational challenges without waiting years for fully mature quantum hardware. Nathan shared his fascinating career journey from military service after 9/11 through the intelligence community, business school, venture investing, and ultimately into the world of advanced simulation and optimization. He emphasized how data-driven thinking shaped his approach to high-stakes decision making and why gut instinct alone no longer suffices in an era driven by AI, complex systems, and operational risk. His insights provide valuable guidance for those interested in careers at the intersection of tech and aerospace. We also explored a question many business leaders are asking right now: what does "quantum in practice" actually look like today? Nathan explained how BQP is applying quantum-inspired approaches on existing CPUs and GPUs to improve simulation accuracy, accelerate modeling workloads, and help aerospace organizations make faster, smarter engineering decisions without simply throwing more hardware at the problem. This shows the tangible progress already happening, inspiring the audience with real-world impact. The discussion also tackled the commercial realities behind deep tech innovation. Nathan spoke candidly about the funding challenges facing startups working in quantum and defense technologies, emphasizing that moving beyond theory into operational deployment is difficult but achievable. This perspective encourages the audience to see obstacles as opportunities for innovation and persistence. Toward the end of the episode, Nathan shared thoughtful advice for students, engineers, and professionals looking to build careers in AI, aerospace, quantum, and defense. His message was simple but powerful: stay curious, keep learning, and never underestimate how a single conversation can completely change your career trajectory. If you've ever wondered how quantum computing moves from science fiction headlines into real-world business value, this episode offers a practical and honest perspective on how quantum-inspired software is already making a difference in aerospace and defense industries today. Useful Links Connect with Nathan Mason on LinkedIn Learn More about BQP Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What happens when AI stops being a feature and starts reshaping the very craft of design itself? Live from, I sat down with Charlie Sutton for a conversation that went far beyond product interfaces and pixels. As Atlassian unveiled its latest AI ambitions around agents, context, and the Teamwork Graph, Charlie offered a fascinating look at the human side of that transformation and why design may become even more important as AI becomes embedded into the way we work. Charlie shared how Atlassian approaches design at scale across products like Jira, Confluence, Loom, and Rovo, explaining why every interaction should feel intentional and cohesive, even when built by hundreds of people across dozens of teams. But this conversation quickly moved into much bigger territory. We explored how AI is changing the relationship between designers, developers, and business teams, and why the traditional barriers between idea and execution are rapidly disappearing. One of the most thought-provoking parts of the discussion centered around democratization. Charlie argued that while AI tools have dramatically lowered the floor for creativity, they have also raised the ceiling for what users now expect from software experiences. Anyone can prototype an app today, but expectations around quality, coherence, trust, and usability are climbing just as quickly. We also unpacked the growing shift from prompting AI to delegating work to AI agents. Charlie explained why assigning work to agents increasingly resembles managing human teammates, from defining goals and success criteria to understanding strengths, limitations, and context. That naturally led us into a deeper conversation about trust, transparency, and why users must always feel they can "pop the bonnet" and understand what AI systems are doing on their behalf. Another major theme throughout the episode was context. Charlie shared why Atlassian sees organizational context as one of the defining challenges of the AI era and how the Teamwork Graph is helping connect people, projects, conversations, and knowledge across the company. He compared this moment to the first time many of us used Google search and suddenly realized the scale of what was possible. We also discussed how AI adoption is unfolding differently from previous technology waves. Instead of adoption trickling down from hardcore technical users, Charlie is seeing rapid experimentation from marketing, HR, and design teams looking to reduce repetitive work and communicate ideas more effectively. Even his own mother, he joked, has become an AI power user before he has. From AltaVista nostalgia and Ask Jeeves memories to serious conversations about the future of human creativity, this episode captures a rare and honest perspective on where design, collaboration, and AI may be heading next. How will organizations balance personalization with shared experiences as AI becomes embedded into every workflow, and what role will human creativity play when everyone suddenly has access to the same powerful tools? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What happens when one of the most iconic teams in Formula One decides to rethink how work gets done behind the scenes completely? Last year, Atlassian Williams Racing made headlines when Atlassian entered Formula One as both title partner and technology partner. At the time, many people saw the partnership as another high-profile sponsorship deal. But over the last twelve months, something much bigger has been unfolding inside the Williams organization. At Team '26 in Anaheim, I sat down with Andrew Boyagi and Matt Harman to unpack how AI, data, workflows, and organizational transformation are reshaping life both at the factory and on the grid. This conversation goes far beyond racing. Matt explains how Williams is reducing the time between "idea to track," compressing development cycles so upgrades arrive at race weekends weeks earlier than before. One striking example involves reducing front wing lead times by a factor of three through parallel workflows and better collaboration, allowing performance gains to reach the circuit three race weekends sooner. Andrew shares how Atlassian's system-of-work philosophy is being applied in one of the most data-intensive environments on earth. We explore how tools like Jira, Confluence, Loom, Rovo, and Teamwork Graph are helping engineers, strategists, operations teams, and factory staff make faster decisions with less operational friction. We also discuss how AI is changing engineers' roles, why organizational context matters more than raw intelligence, and how Formula One teams balance human instinct with AI-driven precision in race strategy decisions. Matt offers fascinating insight into how AI helps teams process decades of historical race data in real time while still relying on human judgment in critical moments. Along the way, we explore the cultural transformation underway at Williams, including the shift away from endless meetings toward faster, outcome-focused collaboration. Matt explains how tools like Loom and Confluence are helping teams make decisions more efficiently while spreading knowledge more effectively across specialist departments. Andrew also reveals some eye-opening metrics from the partnership so far. Since rolling out Atlassian's Teamwork Collection, teams have reportedly increased throughput by 83%, while low-value meetings have been reduced by 863 hours in a single month across 200 people. Perhaps the biggest takeaway from this episode is that Formula One may actually be a perfect reflection of the challenges facing every modern business. As Andrew puts it during our conversation, Formula One is ultimately "an enterprise performance problem," just operating at 300 kilometers an hour with millions of people watching every weekend. If you've ever wondered what enterprise transformation looks like when milliseconds matter, this episode offers a fascinating look inside one of the most ambitious AI and workflow transformation journeys happening anywhere in business today   Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What happens when the biggest innovation in housing isn't a luxury tower or another short-term rental app, but a platform built specifically for everyone caught in between? In this episode of Tech Talks Daily, I sat down with Ezra Gershanok, co-founder of Ohana, to unpack how his team is quietly reshaping the overlooked middle-term housing market. For years, people relocating for internships, new jobs, temporary projects, or extended travel have faced two bad choices. Either pay eye-watering hotel and Airbnb rates for months at a time or lock themselves into inflexible long-term leases they never really wanted. Ezra experienced this firsthand while relocating during his time at McKinsey, while his co-founder faced similar frustrations at Apple. Instead of accepting the problem as unavoidable, they built a marketplace around trust, flexibility, and human connection. What struck me throughout our conversation was how Ohana sits at the crossroads of technology, real-world problem solving, and changing work culture. The company has already processed more than $37 million in payments over the past year, with average booking values around $8,000 and average stays approaching 80 nights. Those numbers completely change the economics and psychology of online marketplaces. These are no longer casual weekend bookings. These are high-trust decisions involving real money, real relocation stress, and real human relationships. We explored how Ohana uses AI behind the scenes while deliberately keeping the customer experience deeply human. Hosts and guests are introduced on live match calls. Security deposits are held in escrow. Support teams actively facilitate trust between both sides. Ezra shared how the company uses AI to scale communication and operational workflows without replacing human interaction, something that feels increasingly rare in today's race toward automation. The conversation also touched on how employer partnerships with companies like OpenAI, Palantir Technologies, and Oracle are creating predictable housing demand for interns and new hires moving into expensive cities like New York City and London. Ezra explained why the platform initially gained traction among Chinese international students and how those same network effects are now accelerating growth in London. We also discussed the practical side of building a startup with no-code tools like Bubble, scaling globally with a tiny core team, balancing community standards with rapid growth, and why execution still matters more than ideas. Ezra offered refreshingly honest insights about persistence, operational discipline, and why solving an underserved problem often matters far more than building flashy technology. This episode is a fascinating look at how AI can actually support more meaningful human experiences instead of replacing them. It is also a conversation about trust, housing, modern mobility, and the growing realization that the way we live and work no longer fits neatly into old systems. So how will platforms like Ohana shape the future of temporary living as work becomes increasingly global, flexible, and distributed?   Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

At Google Cloud Next in Las Vegas, I sat down with Granville Valentine to talk about one of the biggest shifts happening in business technology right now, the move from isolated AI experiments to orchestrated, production-scale agentic systems. Granville leads Google Cloud's AI Go-to-Market organization across North America, working directly with major enterprises on adopting Gemini, customer experience AI, and multi-agent workflows. That puts him right at the center of how businesses are actually deploying AI in the real world, and where many are still getting stuck. In this conversation, we explore why so many companies discovered in 2025 that standalone chatbots were failing to deliver measurable ROI, and how orchestration-based AI systems are changing that. Granville explains why the future belongs to multi-agent workflows built around business outcomes rather than technology demos, with different agents collaborating around customer experience, commerce, upselling, support, and personalization. We also discuss the rise of proactive "digital concierges" that unify search, commerce, maps, personalization, and customer service into a single intelligent journey rather than the fragmented app experiences consumers are used to today. Granville shares practical examples from companies like The Home Depot and explains how businesses are using Gemini Enterprise for Customer Experience to create more natural and effective customer interactions. Another major theme in this episode is data. We explore how cross-cloud connectivity and universal context engines are helping organizations query data across multiple cloud environments without moving everything into a single platform first, dramatically reducing friction for companies trying to build agentic workforces. The conversation also touches on generative media, from video and image creation to interactive shopping experiences, and how businesses are using these tools to drive real engagement, customer retention, and revenue growth rather than simply producing flashy content. Most importantly, this episode cuts through the hype and focuses on execution. Granville explains why businesses need to stop thinking about AI as a standalone feature and start thinking about it as an operating model built around outcomes, experimentation, and continuous learning. Are businesses finally ready to move from AI experimentation to the agentic enterprise? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What happens when AI growth collides with the physical limits of power, materials, and global supply chains? In this episode of Tech Talks Daily, I speak with Matt Kelly, CTO and Vice President of Technology and Standards at the Global Electronics Association, about the growing pressure on AI infrastructure and the supply chains that support it. Drawing on insights from thousands of member organizations across manufacturing, automotive, and electronics, Matt offers a practical look at what business and technology leaders should really be preparing for in 2026 and beyond. Our conversation begins with the shift from cost optimization to resilience and system-level performance. Matt explains why the old procurement mindset of chasing the lowest-cost supplier is rapidly being replaced by what he calls confidence-based sourcing. In a world shaped by geopolitical disruption, pandemic aftershocks, and surging demand for AI, organizations are discovering that cheap sourcing means little if critical components fail to arrive on time. We also discuss why dual sourcing has evolved from a procurement strategy into a business continuity requirement. Matt shares real-world examples of how something as small as a missing capacitor can prevent the delivery of million-dollar AI infrastructure systems. That single point of failure has pushed resilience metrics such as recovery time, geographic diversity, and validated backup suppliers into boardroom discussions. Another major focus of the episode centers on AI infrastructure itself.  While many conversations around AI focus on software models and automation, Matt argues that the true bottleneck may soon become power availability. From server cooling and energy consumption to sustainable hardware design and material shortages, the industry now faces challenges that stretch far beyond compute performance alone. Matt also explains why fully localized supply chains remain unrealistic for the electronics industry. Instead, he advocates for a balanced model that combines trusted global partnerships with strategic regional sourcing for critical components and security-sensitive technologies. One of the strongest takeaways from this conversation is that AI infrastructure must now be approached as a system problem. Silicon design, packaging, thermal management, power delivery, sustainability, and supply chain strategy cannot be treated as separate conversations. As organizations race to scale AI capabilities over the next few years, are business leaders truly prepared for the infrastructure realities sitting behind the AI boom, or are we about to discover that resilience and energy matter just as much as innovation itself? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What happens when the pace of AI innovation collides with the realities of semiconductor development? In this episode of Tech Talks Daily, I speak with Faraj Aalaei, CEO of Cognichip and a semiconductor industry veteran with more than 25 years of experience spanning engineering, venture capital, and two successful IPOs. Faraj joins me to discuss why the future of artificial intelligence may depend on radically rethinking how chips are designed, manufactured, and scaled. Cognichip is developing the world's first Artificial Chip Intelligence (ACI®) to reimagine chip design. Founded by experts from Amazon, Google, Apple, Aquantia, Synopsys, and KLA, the company tackles high cost and inaccessibility in chip development, enabling hardware to evolve as quickly as software innovation. Backed by $93 million in total funding from Seligman Ventures, SBI Investment, Mayfield, Lux Capital, and Candou Ventures, Cognichip's ACI® reduces design cycles by 50%, cuts development costs by 75%, and optimizes power, performance, and efficiency. ACI® accelerates innovation and broadens access to semiconductor technology by making it easier, more affordable, and accessible to a broader range of innovators Faraj explains how the semiconductor industry now faces a growing bottleneck. While AI software can evolve at remarkable speed, chip development often still takes between three and five years and costs more than $100 million. That mismatch is becoming increasingly difficult to sustain as demand grows for specialized AI hardware, edge computing systems, and next-generation infrastructure. Our conversation also explores the geopolitical and economic shifts reshaping the semiconductor industry. Faraj shares his perspective on the emerging concept of "Pax Silica," the growing effort by governments to restructure global chip supply chains and reduce reliance on China. While many policymakers see this as a matter of national security and resilience, Faraj warns there may also be unintended consequences, including rising AI infrastructure costs, engineering shortages, and slower innovation cycles. One of the most interesting parts of our discussion centers on the idea that AI itself may become the missing scaling factor for semiconductor development. Instead of relying solely on larger engineering teams and longer development cycles, Cognichip believes AI-designed chips could dramatically accelerate innovation and make advanced hardware development accessible to far more companies and researchers. Faraj also reflects on his career journey from entrepreneur to investor and back again, sharing lessons from decades spent helping build the modern semiconductor ecosystem. From supply chain realities to the growing pressure on engineering talent, this episode offers a rare insider perspective on the technologies quietly powering the entire AI economy. As AI systems continue to demand faster, more specialized hardware, are we reaching the limits of traditional chip development, and could AI itself become the tool that reshapes the future of semiconductors?

What happens when a country moves from cash-only transactions to instant digital payments that work for everyone? In this episode of Tech Talks Daily, I sit down with Steve Haley, Director of Market Development at The Mojaloop Foundation, to discuss how open and interoperable payment systems are helping reshape financial inclusion across Africa and other emerging markets. For many listeners in Europe or North America, instant payments and digital banking are often taken for granted. But Steve explains how millions of people around the world still live in economies where cash dominates daily life, and where even those with mobile money accounts remain disconnected from the wider financial system. In some countries, people have even been forced to carry two phones because competing mobile payment providers could not communicate with each other. Our conversation focuses heavily on Liberia, where the Liberian Inclusive Instant Payments System was deployed in just 73 business days. Built using Mojaloop technology in partnership with the Central Bank of Liberia, ThitsaWorks, and AfricaNenda, the system now allows interoperable mobile money transfers between major operators, including MTN and Orange Liberia. Steve shares why this matters far beyond convenience. Removing barriers between providers means people no longer need money trapped across separate accounts, merchants can accept digital payments more easily, and governments can distribute payroll and public payments through faster and more transparent systems. We also discuss how mobile wallets are helping expand account ownership across Liberia, which now exceeds 50 percent according to World Bank data, and why interoperability may become the missing piece that transforms access into meaningful financial participation. Another fascinating part of our discussion centers on the future of cross-border payments in Africa. Steve explains how many transactions between neighboring African countries still route through systems in the United States, increasing both cost and complexity. He believes interoperable instant payment systems across the continent could dramatically lower those barriers and unlock new levels of regional trade. This episode offers a thoughtful reminder that digital transformation is not always about the latest AI model or enterprise software platform. Sometimes it is about giving people the ability to send money, pay merchants, receive salaries, and participate in the economy with the same ease many of us already expect every day.  So how different would life feel if digital payments finally became accessible to everyone, regardless of where they live or who they bank with? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

How do AI agents safely access the data they need without exposing the business to risk? In this episode, I speak with Alex Gallego, CEO and founder of Redpanda, about why streaming data is becoming such an important foundation for enterprise AI. Redpanda began as a high-performance streaming data platform, but the company is now building what it calls the Agentic Data Plane, a governed access layer designed to connect AI agents with enterprise data and systems. Alex shares the story behind Redpanda's journey, from solving a personal engineering frustration to powering mission-grade systems for some of the world's largest organizations. We discuss why many enterprises are racing toward agentic AI while still lacking the permissions, controls, context, and observability needed to make agents safe in production. One of the standout moments in our conversation is Alex's comparison between hiring AI agents and forgetting to onboard them. Businesses are deploying accounting agents, coding agents, customer success agents, and security agents, yet many still lack a reliable way to decide what those agents can access, what actions they can take, and how to prove what happened when something goes wrong. We also talk about explainability, agent transcripts, and why enterprises need a full record of agent behavior across complex chains of activity. Alex explains how this matters in regulated sectors such as banking, where organizations may need to prove that an AI agent is acting helpfully and responsibly, and in manufacturing, where a faulty agent action could affect months of production. Alex also shares Redpanda's work with NVIDIA Vera, where benchmarks showed 5.5 times lower latency and 73% higher throughput. For business leaders, that means faster systems, lower costs, better customer experiences, and the ability to monitor agent behavior in real time. This conversation is a practical look at what enterprise AI needs next.  Speed matters, but governance, trust, and control may decide which companies can move AI agents from experiments into real operations. So, are we ready to give AI agents access to the enterprise, or do we first need to learn how to manage them like part of the workforce? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What happens when AI moves beyond experimentation and becomes part of the creative process itself? At Google Cloud Next in Las Vegas, I sat down with Albert Lai to explore how AI is transforming the media and entertainment industry from content creation and production to personalization, localization, and audience engagement. Albert works across Google Cloud, Google, and the wider Alphabet ecosystem to help media organizations rethink how they create and distribute content using cloud infrastructure, multimodal AI, and agentic workflows. And one thing became very clear in this conversation, the industry has moved beyond asking "What if?" and is now firmly focused on production-scale execution and measurable business outcomes. We discuss why media companies are fighting a growing battle for audience attention, and how AI is helping them create content more efficiently while also unlocking the value hidden inside vast archives of existing material. Albert explains why the conversation has shifted from simply producing more content to maximizing what already exists, and how AI is helping organizations rediscover and reimagine decades of footage, audio, and intellectual property. The conversation also explores one of the biggest themes emerging at both Google Cloud Next and NAB Show, the rise of agentic AI workflows. Albert shares how media companies are using orchestrated AI systems to streamline complex production processes, support editors and creators inside existing workflows, and improve everything from localization and dubbing to monetization and personalization. We also dive into real-world examples, including how companies like Avid Technology are integrating Google AI directly into production environments, and how Indonesian media company MTech used Google Cloud AI tools to create and distribute a 26-episode animated series with measurable improvements in production speed, cost efficiency, and audience engagement. This is not a conversation about replacing creativity. It is about augmenting it. If you work in media, content, streaming, sports, publishing, or simply want to understand how AI is changing storytelling itself, this episode is packed with practical insight and real-world examples. How will AI change the stories we create, and the way audiences experience them? Useful Links Connect with Albert, Lai Google Cloud Next 26 Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What does real ROI from AI and analytics actually look like in the fast-food industry? At SAS Innovate, I sat down with David Gardner, Senior Director of Analytics at Boddie-Noell Enterprises, the largest franchise operator of Hardee's in the United States, to explore how a 60-year-old family business is transforming itself through data, forecasting, and AI. This is a company processing around 40 million transactional records every single day across more than 300 restaurants, where even shaving a few seconds off a drive-thru experience can have a measurable impact on customer satisfaction and revenue. What makes this conversation so interesting is how grounded it is in operational reality. David shares how the company moved from relying on spreadsheets, summarized reports, and gut instinct toward real-time analytics powered by SAS. One of the standout stories involves extending breakfast hours. Operational teams initially resisted the idea, convinced it would create chaos in the kitchen. But once David dug into the transactional data, the numbers told a very different story. Breakfast sales during the extended hours were growing dramatically, proving the demand was real and helping the business make a decision based on evidence rather than instinct. We also discuss how analytics is helping optimize labor scheduling, forecasting, payroll, inventory planning, and customer throughput at scale. David explains how his team can now analyze profitability hour by hour for every restaurant in the business, helping local managers make faster and more informed decisions. With forecasting accuracy improving to within fractions of a percentage point, the business can plan more effectively in an industry facing inflation, labor pressures, delivery app disruption, and shifting customer habits. Another major theme is accessibility. David talks about the importance of data democratization and making analytics understandable for non-technical teams. Restaurant managers are not data scientists, and they should not need to be. The goal is to put insights directly into their hands in a way that is simple, actionable, and easy to understand. AI is now becoming part of that journey too, acting as what David describes as a mentor for newer managers, helping them identify opportunities, improve operations, and get up to speed faster. We also explore how customer behavior has changed dramatically with the rise of delivery platforms like DoorDash and Uber Eats, creating entirely different purchasing patterns compared to traditional in-store diners. Through analytics, the company can better understand those differences and optimize everything from promotions to staffing and menu strategy. What stood out most to me is that this is not a story about flashy AI demos or abstract transformation projects. It is about using analytics to solve practical business problems in real time while quietly improving the customer experience behind the scenes. Because at the end of the day, customers do not care about dashboards or machine learning models. They care about getting good food quickly, accurately, and consistently. The technology only matters if it helps deliver that outcome. So as businesses continue chasing AI opportunities, are they focusing on the use cases that actually move the needle, or getting distracted by the hype? Useful Links Connect with David Gardner Learn More About Boddie-Noell Ent. Catchup With What You Missed at Google Cloud Next Please check the partners of the Tech Tech Talks Network Denodo Learn more about the NordLayer Browser

What happens when the excitement around AI collides with the reality of deploying it inside a business? At SAS Innovate, that question came up repeatedly, and in this episode, I sit down with Manisha Khanna, global product marketing lead for AI at SAS, to unpack why so many organizations are still struggling to move from AI pilots to meaningful business outcomes. While headlines continue to celebrate the rapid rise of generative AI and agentic systems, Manisha brings a far more practical perspective shaped by working directly with enterprises trying to operationalize AI at scale. One of the most striking parts of our conversation centers on why AI projects continue to stall. According to Manisha, the biggest problems are not weak models or lack of ambition. Instead, organizations are running into unpredictable inference costs, operational complexity, governance challenges, and internal resistance to change. She explains why many companies still approach AI as a technology purchase rather than a transformation strategy, and why governance built in from the beginning can actually accelerate adoption rather than slow it down. We also spend time exploring what agentic AI really means beyond the hype. Manisha shares why SAS chose supply chain as the launch point for its first industry-packaged agent and how agentic systems differ from copilots by acting more like coworkers than assistants. Rather than simply providing recommendations, these systems can actively participate in business workflows, helping organizations move from monthly optimization cycles to near real-time decision-making. Another major theme is the growing importance of governance and accountability. As organizations deploy AI into regulated industries and customer-facing environments, the focus is shifting away from "whose model is best" toward "who is deploying the best use cases responsibly." Manisha explains why governing the use case itself matters more than obsessing over model benchmarks, and why companies that bolt governance on afterward create friction for themselves later. The conversation also touches on where AI is already delivering measurable value today. From customer complaint management in banking to aircraft maintenance support systems powered by retrieval-augmented generation, we discuss how organizations are seeing success when AI augments existing workflows rather than attempting wholesale disruption overnight. What stood out most for me is how often the human side of AI came back into focus. Manisha repeatedly emphasized that leadership communication, employee trust, and organizational readiness are just as important as the technology itself. If leaders position AI purely as a cost-cutting tool, fear and resistance follow. But when AI is framed as a way to empower people and improve outcomes, adoption becomes much easier. As organizations continue to implement AI and agentic systems, the biggest question is no longer whether the technology works, but whether businesses are ready to lay the foundations needed to make it succeed. Useful Links Connect with Manisha Khanna SAS Blog SAS Innovate Please check the partners of the Tech Tech Talks Network Denodo Learn more about the NordLayer Browser

What happens when cybercrime becomes as easy to access as a subscription service, and what does that mean for every business connected to the internet today? In this episode, I sit down with SentinelOne AI and Cloud Security Evangelist Chris Hosking to unpack a shift that feels both inevitable and deeply unsettling. The rise of what Chris describes as an AI threat market is changing the rules of engagement. Cybercrime is no longer limited to highly skilled operators working in isolation. Instead, it has evolved into a thriving ecosystem where tools, services, and even AI-powered attack kits are bought and sold with alarming ease. As Chris explains during our conversation, "cyber crime is quite an ecosystem… the dark web has always been a place for cyber criminals to meet and to sell their wares." We explore how AI has accelerated this shift, lowering the barrier to entry to the point where attacks can be launched for as little as £35. That democratization of cybercrime is already having real-world consequences. Chris shares how individuals without deep technical expertise are now able to orchestrate sophisticated attacks using AI assistance, and why that surge in accessibility is driving both the volume and impact of cyber incidents. It also reframes a common misconception. Smaller businesses are not flying under the radar.  In fact, many are being targeted precisely because of weaker defenses, with attacks increasingly automated and opportunistic. The conversation also moves into more complex territory, where organized cybercrime and nation-state activity begin to overlap. Chris highlights how governments and criminal groups are drawing from the same AI marketplaces, blurring the lines between financial motivation and geopolitical intent. The implications stretch far beyond corporate risk, touching on critical infrastructure and everyday services that people rely on. It raises a difficult question about preparedness in a world where attacks are faster, more frequent, and harder to predict. At the same time, there is a practical thread running through this discussion. Chris challenges the instinct to immediately invest in more tools and instead encourages leaders to look inward first.  From improving basic security hygiene to using AI to reduce manual workload and noise, there are tangible steps organizations can take right now. The goal is not perfection, but resilience in an environment where, as Chris points out, incidents are becoming a regular occurrence rather than a rare event. This episode offers a clear-eyed look at where cybersecurity is heading, without the hype or fear-driven narratives. It is a conversation about scale, speed, and the uncomfortable reality that the threat landscape has changed in ways many organizations are still catching up with. So as AI continues to reshape both innovation and risk, how prepared is your organization for a world where anyone can launch an attack with a few prompts and a subscription? Useful Links SentinalOne Blog Connect with Chris Hosking Please check the partners of the Tech Tech Talks Network Denodo Learn more about the NordLayer Browser

What happens when the race to deploy AI starts to outpace the ability to control it? In this episode of Tech Talks Daily, I sit down with Ken Englund from EY to unpack findings from the latest 2026 Technology Pulse Poll, and the conversation quickly moves beyond theory into something many leaders will recognize from their own organizations. There is a growing tension between speed and oversight, a "velocity paradox" Ken describes, in which businesses are accelerating AI adoption while governance struggles to keep up. The numbers behind that story are hard to ignore. A large majority of tech leaders are prioritizing speed to market over careful vetting, while more than half of AI initiatives are happening outside formal IT oversight. For anyone responsible for security, compliance, or risk, that gap raises immediate concerns. But as Ken explains, it is not as simple as labeling this as reckless behavior. Much of this activity is driven by real innovation happening closer to the business, where teams are experimenting, solving problems, and creating value quickly. We spend time breaking down what that looks like in practice. From the rise of shadow AI tools to the growing risk of sensitive data exposure, there is already evidence that the consequences are beginning to show. At the same time, nearly every executive surveyed sees autonomous AI as central to future competitiveness, which means slowing down is not really an option either. One of the most useful parts of the conversation focuses on what organizations can actually do about it. Ken shares practical insight into why architecture matters more than ambition, how companies should think about optionality in a fast-moving AI ecosystem, and why observability is becoming a missing layer in many deployments. We also get into the reality of measuring AI value, where the conversation is shifting from promised returns to the often-overlooked cost side, including token usage and uncontrolled spending across departments. There is also a broader discussion around leadership and culture. Governance frameworks may exist on paper, but the real challenge lies in operationalizing them across a business that is already moving at speed. Add in geopolitical pressures, evolving regulations, and the complexity of deploying AI globally, and it becomes clear why many organizations feel overwhelmed. This episode is not about slowing innovation down. It is about understanding where things are breaking, what leaders are getting wrong, and how to build a path forward that balances progress with accountability. So, as AI budgets continue to rise and autonomous systems become part of everyday operations, how will your organization close the gap between ambition and control, and are you already further along that path than you realize?  Useful Links Ernst & Young Technology Pulse Poll Connect with Ken Englund on LinkedIn Follow on LinkedIn Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

What happens when your financial advisor is no longer limited by time, availability, or even geography, but is always there when you need them, ready to listen, respond, and guide you in real time? At Citi's announcement at Google Cloud Next 2026, I sat down with Joe Bonanno, Head of Wealth Intelligence, and Karolina Belwal, Global Head of Data Intelligence and Automation for Citi Wealth, to unpack what could become a defining shift in how wealth management is delivered. The launch of Citi Sky, built in partnership with Google Cloud and powered by Google DeepMind, is not another digital feature layered onto an existing app. It signals a move toward an always-on, conversational, and highly personalized experience that blends human expertise with AI-driven intelligence. What stood out in our conversation was how grounded this initiative is in real-world client behavior. Joe explained how traditional engagement models, whether phone calls, emails, or app notifications, often feel disconnected from what clients actually need in the moment. Life events, changing market conditions, and personal priorities rarely align with scheduled interactions. Citi Sky attempts to close that gap by being present at the exact moment a client has a question, whether that is late at night, between meetings, or during a moment of financial uncertainty. Karolina brought that point to life with a simple but relatable example. As a working parent, she highlighted how difficult it can be to connect with an advisor during the day. Citi Sky allows clients to engage on their own terms, asking questions when it suits them, in a way that feels natural and responsive. That shift from scheduled interaction to on-demand conversation could change how people think about financial guidance altogether. Under the hood, the technology is just as ambitious. Built on Gemini models through Google's enterprise agent platform, Citi Sky combines real-time voice, video, and multilingual capabilities into a single experience. But what makes it interesting is how it moves beyond reacting to questions. The system can anticipate needs, surface insights, and even guide advisors by identifying which clients may require attention during market events. In Joe's words, it becomes a teammate, one that can scale expertise across hundreds of clients while maintaining a sense of personalization. There is also a broader implication here for the industry. Wealth management has long relied on relationships built over time, supported by human intuition and experience. Citi is not replacing that model, but it is extending it. Advisors are still central, yet their reach is amplified by AI that handles routine interactions, summarizes conversations, and provides context before the next client meeting even begins. Of course, this raises familiar questions around trust, governance, and the role of AI in financial decision-making. Citi is clearly aware of that tension, emphasizing secure data foundations, regulatory compliance, and the importance of embedding its Chief Investment Office's institutional knowledge directly into the system. This is not positioned as a generic AI assistant, but as a reflection of Citi's own expertise, delivered through a new interface. What I found most compelling, though, was how both Joe and Karolina kept returning to the human side of the story. Yes, this is about agentic AI and advanced models. Still, it is also about reducing friction, improving access, and helping people answer a simple but powerful question: Am I financially okay? As Citi Sky rolls out to Citigold clients in the U.S., it will be fascinating to see how customers respond and how competitors react. If this model gains traction, it could reshape expectations far beyond wealth management and into every corner of financial services. As we move into the next phase of AI-driven client engagement, are we ready to trust a system that listens, understands, and acts on our financial lives in real time, and how much of that responsibility are we willing to share? Useful Links Learn More About Citi Sky, the AI-Powered Member of the Citi Wealth Team. Connect with Joseph V. Bonanno Jr. Connect with Karolina Belwal Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

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 Connect with Alison Kay Unlocking the UK's AI Potential" report. Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com  

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 Connect with Werner Vogels Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

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 Connect with Dr. Alex Asiimwe OHDSI – Observational Health Data Sciences and Informatics Please check our partners of Tech Tech Talks Network Learn more about the NordLayer Browser  

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 Connect with Dennis Woodside on LinkedIn Learn more about  Freshworks  Refresh 2026 Event Follow on LinkedIn Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.  

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 Yulie Kwon Kim, Vice President of Product for Google Google Cloud Next 26 Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.