Tech Talks Daily

What happens when we finally admit that stopping every cyberattack was never realistic in the first place? That is the thread running through this conversation, recorded at the start of the year when reflection tends to be more honest and the noise dial is turned down a little. I was joined by returning guest Raghu Nandakumara from Illumio, nearly three years after our last discussion, to pick up a question that has aged far too well. How do organizations talk about cybersecurity value when breaches keep happening anyway? This episode is less about shiny tools and more about uncomfortable truths. We spend time unpacking why security teams still struggle to show value, why prevention-only thinking keeps setting leaders up for disappointment, and why the conversation is slowly shifting toward resilience and containment. Raghu is refreshingly direct on why reducing cyber risk, rather than chasing impossible guarantees, is the only metric that really holds up under boardroom scrutiny. We also talk about the strange contradiction playing out across industries. Attackers are often using familiar paths like misconfigurations, excessive permissions, and missing patches, yet many organizations still fail to close those gaps. The issue, as Raghu explains, is rarely a lack of tools. It is usually fragmented coverage, outdated processes, and a talent pipeline that blocks capable people from entering the field while claiming there is a skills shortage. One of the most practical parts of this conversation centers on mindset. Instead of asking whether an attacker got in, Raghu argues that leaders should be asking how far they were able to go once inside. That shift alone changes how success is measured, how teams prepare for incidents, and how pressure-filled P1 moments are handled when boards want answers every fifteen minutes. We also touch on how legal action, public claims campaigns, and customer lawsuits are changing the stakes after a breach, forcing executives to rethink how they frame cyber investment. From there, Raghu shares how Illumio has been working with Microsoft to strengthen internal resilience at massive scale, and why visibility and segmentation are becoming harder to ignore. This is a conversation about realism, responsibility, and growing up as an industry. If cybersecurity is really about safety and not slogans, what would you want your organization to stop saying, and what would you rather hear instead? Please feel free to upload the podcast. Here are also the links we discussed on the call: Useful Links Connect with Raghu Nandakumara on LinkedIn and Twitter Learn more about Illumio Lateral Movement in Cyberattacks Illumio Podcast  Follow on Facebook, Twitter, LinkedIn, and YouTube   Thanks to our sponsors, Alcor, for supporting the show.

What really happens inside an organization when a cyber incident hits and the neat incident response plan starts to fall apart? That question sat at the heart of my return conversation with Max Vetter, VP of Cyber at Immersive. It has been a big year for breaches, public fallout, and eye-watering financial losses, and this episode goes beyond headlines to examine what cyber crisis management actually looks like when pressure, uncertainty, and human behavior collide. Max brings a rare perspective shaped by years in law enforcement, intelligence work, and hands-on cyber defense, and he is refreshingly honest about where most organizations are still unprepared. We talked about why written incident response plans tend to fail at the exact moment they are needed most. Cyber incidents are chaotic, emotional, and non-linear, yet many plans assume calm decision-making and perfect coordination. Max explains why success or failure is often defined by the response rather than the initial breach itself, and why leadership, communication, and judgment matter just as much as technical skill. Real-world examples from major incidents highlight how competing pressures quickly emerge, whether to contain or keep systems running, whether to pay a ransom or risk prolonged downtime, and how every option comes with consequences. One idea that really stood out is Max's belief that resilience is revealed, not documented. Compliance and audits may tick boxes, but they rarely expose how teams behave under stress. We explored why organizations that rely on annual tabletop exercises often develop a false sense of confidence, and how that confidence can become dangerous when decisions are made quickly and publicly. Max shared why the best-performing teams are often the ones that feel less certain in the moment, because they question assumptions and adapt faster. We also dug into the growing role of crisis simulations and micro-drills. Rather than rehearsing a single scenario once a year, Immersive focuses on repeated, realistic practice that builds muscle memory across technical teams, executives, legal, and communications. The goal is not to predict the exact attack, but to train people to think clearly, collaborate across functions, and make defensible decisions when there are no good options. That preparation becomes even more important as cyber incidents increasingly spill into supply chains, manufacturing, and the physical world. As public scrutiny rises and consumer-led legal action becomes more common after breaches, reputation and response speed now sit alongside forensics and recovery as business-critical concerns. This episode is a candid look at why cyber crisis readiness is a discipline, not a document, and why assuming you will cope when the moment arrives is a risky bet. So if resilience only truly shows itself when everything is on the line, how confident are you that your organization would perform when the pressure is real and the clock is ticking? Useful Links Connect with Max Vetter on Linkedin Learn more about Immersive Labs Follow on LinkedIn, Instagram, Twitter and Facebook Thanks to our sponsors, Alcor, for supporting the show.

What happens when the web browser stops being a passive window to information and starts acting like an intelligent coworker, and why does that suddenly make security everyone's problem? At the start of 2026, I sat down with Michael Shieh from Mammoth Cyber to unpack a shift that is quietly redefining how work gets done. AI browsers are moving fast from consumer curiosity to enterprise reality, embedding agentic AI directly into the place where most work already happens, the browser. Search, research, comparison, analysis, and decision support are no longer separate steps. They are becoming one continuous workflow. In this conversation, we talk openly about why consumer adoption has surged while enterprise teams remain hesitant. Many employees already rely on AI-powered browsing at home because it removes ads, personalizes results, and saves time.  Inside organizations, however, the same tools raise difficult questions around data exposure, credential safety, and indirect prompt injection. Once an AI agent starts reading untrusted external content, the browser itself becomes a new attack surface. Michael explains why this risk is often misunderstood and why the real danger is not internal documents, but external websites designed to manipulate AI behavior. We dig into how Mammoth Cyber approaches this challenge differently, starting with a secure-first architecture that isolates trusted internal data from untrusted external sources. Every AI action, from memory to model connections to data access, is monitored and governed by policy. It is a practical response to a problem many security teams know is coming but feel unprepared to manage. We also explore how AI browsers change day-to-day work. A task like competitive analysis, which once took days of manual research and document comparison, can now be completed in minutes when an AI browser securely connects internal knowledge with external intelligence. That productivity gain is real, but only if enterprises trust the environment it runs in. We touch on Zero Trust principles, including work influenced by Chase Cunningham, and why 2026 looks like a tipping point for enterprise AI browsing. The technology is maturing, security controls are catching up, and businesses are starting to accept that blocking AI outright is no longer realistic. If you are curious to see how this works in practice, Mammoth Cyber offers a free Enterprise AI Browser that lets you experience what secure AI-powered browsing actually looks like, without putting your organization at risk. I have included the link so you can explore it yourself and decide whether this is where work is heading next. So, as AI browsers become the new workflow hub for knowledge workers everywhere, is your organization ready to secure the browser before it becomes your most exposed endpoint, and what would adopting one safely change about how your teams work? If you want to see what an enterprise-grade AI browser looks like when security is built in from day one, Mammoth Cyber is offering free access to its Enterprise AI Browser.  It gives you a hands-on way to experience how agentic AI can automate real work inside the browser while keeping internal data isolated from untrusted external sources. You can explore it yourself and decide whether this is how your organization should be approaching AI-powered browsing in 2026. Useful Links Learn more about the Mammoth Enterprise Browser and try it for free  Connect with Michael Shieh on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

What happens when engineering teams can finally see the business impact of every technical decision they make? In this episode of Tech Talks Daily, I sat down with Chris Cooney, Director of Advocacy at Coralogix, to unpack why observability is no longer just an engineering concern, but a strategic lever for the entire business. Chris joined me fresh from AWS re:Invent, where he had challenged a long-standing assumption that technical signals such as CPU usage, error rates, and logs belong only in engineering silos. Instead, he argues that these signals, when enriched and interpreted correctly, can tell a much more powerful story about revenue loss, customer experience, and competitive advantage. We explored Coralogix's Observability Maturity Model, a four-stage framework that guides organizations from basic telemetry collection to business-level decision-making. Chris shared that many teams stall on measuring engineering health without connecting that data to customer impact or financial outcomes. The conversation became especially tangible when he explained how a single failed checkout log can be enriched with product and pricing data to reveal a bug costing thousands of dollars per day. That shift, from "fix this tech debt" to "fix this issue draining revenue," fundamentally changes how priorities are set across teams. Chris also introduced Olly, Coralogix's AI observability agent, and explained why it is designed as an agent rather than a simple assistant. We discussed how Olly can autonomously investigate issues across logs, metrics, traces, alerts, and dashboards, enabling anyone in the organization to ask questions in plain English and receive actionable insights. From diagnosing a complex SQL injection attempt to surfacing downstream customer impact, Olly represents a move toward democratizing observability data far beyond engineering teams. Throughout our discussion, a clear theme emerged. When technical health is directly tied to business health, observability stops being a cost center and becomes a competitive advantage. By giving autonomous engineering teams visibility into real-world impact, organizations can make faster, better decisions, foster innovation, and avoid the blind spots that have cost even well-known brands millions. So if observability still feels like a necessary expense rather than a growth driver in your organization, what would change if every technical signal could be translated into a clear business impact, and who would make better decisions if they could finally see that connection? Useful LInks Connect with Chris Cooney Learn more about Coralogix Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

What does real AI transformation look like when leaders stop chasing prototypes and start demanding outcomes they can actually measure? That question sat at the center of my conversation with Alex Cross, Chief Technology Officer for EMEA at CI&T, alongside Melissa Smith, as we unpacked why so many organizations feel stuck between AI ambition and business reality. There is no shortage of excitement around AI, but there is growing skepticism too, especially from leadership teams who have seen pilots come and go without clear return. This episode focuses on how CI&T is addressing that gap head on. Alex shared how CI&T frames its work as AI-enabled transformation rather than simply layering AI tools onto existing processes. The distinction matters.  Instead of using AI to speed up broken workflows, CI&T reshapes how work gets done so AI becomes part of value creation itself. We explored a standout example from ITAU, the largest bank in Latin America, where deep modernization work helped deliver gains that most executives only ever see in strategy decks.  Productivity rose sharply, digital launch cycles collapsed from years to months, customer satisfaction jumped, and the commercial impact reached hundreds of millions in uplift. These are the kinds of results that change boardroom conversations. A big part of how CI&T gets there is its proprietary Flow platform. Alex explained how Flow gives clients a day-one AI environment, removing the heavy upfront cost and complexity that often slows momentum. Instead of spending months building platforms before any value appears, teams can move from proof of concept to production in as little as six to eight weeks. Flow also plays a second role that many AI programs miss, acting as a measurement layer so performance, efficiency, and ROI are visible rather than assumed. We also talked about why partnerships matter when execution is the goal. CI&T works closely with hyperscalers like AWS and Databricks, combining native tools with its own codified expertise. That combination has helped the company achieve an unusually high success rate in bringing AI initiatives to production, a challenge many organizations still struggle with. For Alex, the difference comes down to a relentless focus on production readiness and collaboration between business and technology teams from day one. Looking ahead, the conversation turned to CI&T's expansion across EMEA and what the company's 30th year represents. Rather than chasing every new trend, the focus is on productizing services around real client problems, whether that is legacy modernization, efficiency, or growth. The goal is to bridge strategy and execution in a way that feels practical, fast, and accountable. If you are leading AI initiatives and wondering why progress feels slower than the hype suggests, this episode offers a grounded perspective from the front lines. So, as organizations head into another year of bold AI plans, the real question becomes this. Are you building faster caterpillars, or are you ready to do the harder work required to turn ambition into something that can truly scale? Useful Links Connect with Alex Cross Connect With Melissa Smith Learn more about CI&T Follow CI&T on LinkedIn and YouTube Thanks to our sponsors, Alcor, for supporting the show.

What does AI-led transformation actually look like when it moves beyond pilots, hype, and slide decks and starts changing how work gets done every day? That question framed my conversation with Venk Korla, CEO of HGS, at a time when many organizations feel both excited and exhausted by AI. Boards want results, teams are buried in proofs of concept, and leaders are under pressure to show progress without breaking trust, budgets, or operations. This episode cuts through that tension and focuses on what it takes to turn ambition into outcomes. Venk shared how HGS thinks about what he calls intelligent experiences, where customer interactions are directly connected to operational follow-through. Instead of treating AI as a front-end layer or a chatbot add-on, HGS links context, data, and fulfillment so the experience continues after the conversation ends. We talked through practical examples, from airlines proactively rebooking stranded passengers before they queue at a desk, to healthcare providers guiding patients step by step before and after surgery with timely, relevant messages. In each case, the value comes from anticipation and execution, not novelty. A big part of our discussion centered on why so many AI initiatives stall. Venk described how organizations often chase technology first, launching pilots without redesigning the underlying process. HGS takes a different route through what they call Realized AI, embedding AI into specific workflows with clear ownership and measurable goals. The focus is on outcomes such as faster processing, higher compliance, and improved customer satisfaction, all proven within a ninety day proof of value. It is a disciplined approach that favors repeatability over experimentation theater. We also spent time on cloud strategy, an area where expectations and reality often collide. Venk was candid about why simple lift-and-shift migrations fail to deliver value. Without re-architecting applications to take advantage of elasticity and serverless compute, cloud spend can grow while performance stalls. He shared how a FinOps mindset, combined with application redesign, helped one client dramatically improve load speeds while reducing costs, reinforcing the idea that transformation requires structural change, not surface movement. Ethics and trust were another thread running through the conversation. Venk emphasized that AI systems are only as reliable as the data, governance, and oversight behind them. Human-in-the-loop design remains central at HGS, ensuring accountability, empathy, and confidence for both customers and employees working alongside AI. This balance between automation and human judgment came up again when we discussed their software-as-a-surface model, where AI and people work together in a carefully orchestrated way, with pricing tied to resolved outcomes rather than activity alone. As the pace of change continues to accelerate, this episode offers a grounded perspective on how to move forward without getting lost in noise. If you are leading transformation and feeling pressure to show progress, the real challenge may not be choosing the right tool, but deciding which outcomes truly matter and redesigning work around them. As AI, cloud, and customer experience continue to converge, are you building systems that look impressive in demos or that deliver predictable results when it counts? Useful Links Connect with Venk Korla Learn more about HGS Follow on LinkedIn  

What if the biggest breakthrough in weight management is not a new diet, but finally seeing how your body responds in real time? That question sat at the center of my conversation with Sharam Fouladgar-Mercer, CEO and co-founder of Signos, a continuous glucose monitoring (CGM) and AI-powered health platform built to help people manage weight by understanding their metabolism. January is when motivation is high and the wellness noise is loud, but it is also when a lot of people realize how hard it is to stick with generic advice that does not fit real life. This episode is about why personalization matters, how metabolic signals can change the way you think about food and exercise, and what happens when health technology shifts from reporting the past to guiding the next decision. Sharam explained how Signos pairs a CGM with an AI-driven experience that turns glucose data into practical actions. The point is not to force people into rigid rules or extreme restrictions. Instead, it is about learning how your body reacts to everyday choices, then using that feedback to reduce spikes, improve consistency, and build habits you can actually live with. We talked about simple interventions, like changing the order of foods in a meal, timing movement more intelligently, and spotting patterns that would otherwise stay invisible. Two personal stories brought the conversation to life. Sharam shared how he lost 25 pounds while increasing his calorie intake, which challenges a lot of assumptions people carry into weight loss. He also shared a story from his family life, where his wife's deep sleep increased from roughly 20 minutes a night to around 60 minutes after focusing on glucose stability, even while total sleep time remained limited during the intense period of raising young kids. It is the kind of detail that hits home for anyone who has ever tried to make healthier choices while exhausted and stretched thin. We also explored why FDA clearance matters for Signos and what that could mean for mainstream access. Over-the-counter availability reduces friction, can lower cost, and opens the door to broader adoption, including potential FSA and HSA eligibility. Looking ahead, Sharam shared a vision that goes beyond weight management, connecting metabolic health to the long arc of prevention and chronic conditions where insulin resistance plays a role. If you have ever felt like you are doing all the "right" things and still not seeing results, this episode will make you rethink what "right" even means. And if you could finally see your metabolism in real time, would it change how you approach food, sleep, exercise, and the habits you want to keep this year? Useful Links Connect with Sharam Fouladgar-Mercer Learn more about Signos Instagram, Facebook, X and YouTube Thanks to our sponsors, Alcor, for supporting the show.  

What if your website could spot its own problems, fix them, and quietly make more money while you focus on building your business? That question sat at the heart of my conversation with Aviv Frenkel, co-founder and CEO of Moonshot AI, and it speaks to a frustration almost every founder and digital leader recognizes. Traffic is expensive, attention is fragile, and even small issues in design or flow can quietly drain revenue for months before anyone notices. Traditional optimization often means long cycles, internal debates, and teams juggling analytics, design tools, and testing platforms while hoping the next experiment moves the needle. Aviv's perspective is shaped by lived experience. Before building Moonshot AI, he ran an e-commerce company that had plenty of visitors but disappointing conversion. Like many founders, he watched teams guess at fixes, wait weeks for tests to run, then struggle to link effort to outcome. Moonshot AI was born from that frustration, with a simple ambition. Let the website diagnose what is broken, generate solutions, test them, and deploy the winner automatically, without the need for a dedicated growth team. In our discussion, Aviv explained how Moonshot focuses on front-end experience and site performance, spotting issues such as unclear value propositions, poorly placed calls to action, or confusing mobile navigation. The platform generates its own design, copy, and code variants, runs live tests, and then rolls out what actually works. The results are hard to ignore. Brands across beauty, fashion, jewelry, and consumer electronics are seeing revenue per visitor lift by thirty to fifty percent within months. One small change to a mobile navigation menu at Hugh Jewelry led to a fifty seven percent increase in revenue per visitor, which is the kind of outcome that gets leadership teams paying attention. We also talked about momentum behind the company itself. A recently announced ten million dollar seed round has given Moonshot AI the resources to scale engineering and go-to-market teams at a time when demand is accelerating fast. But beyond funding and growth charts, what stood out most was Aviv's longer-term view. As more people turn to AI assistants and agents instead of traditional search, websites need to be structured so machines can understand them as clearly as humans. Moonshot is already optimizing for that future, preparing sites for an agent-driven web where the customer might be an algorithm as much as a person. Aviv also shared his personal journey, moving from a successful career as a tech journalist and TV host into the far more humbling world of building companies. Rejection, uncertainty, and hard lessons came with the territory, but so did clarity. His guiding idea, inspired by Jeff Bezos, is a minimum regret mindset, choosing the harder path now to avoid looking back later and wondering what might have been. So as AI moves from tools that assist to systems that act, and as websites become active participants in growth rather than static assets, the big question becomes this. Are you still relying on slow, manual optimization cycles, or are you ready to let your website start improving itself, and what does that shift mean for how you build and scale in the years ahead? Useful Links Connect with Aviv Frenkel Learn More About Moonshot AI Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

What happens when decades of supply chain planning collide with AI, volatility, and a world that no longer moves at a predictable pace? That question sat at the heart of my conversation with Piet Buyck, a serial entrepreneur whose career spans early optimization engines, cloud-era planning systems, and now AI-driven decision environments. Speaking from Antwerp just days before the holidays, Piet brought a calm, grounded perspective shaped by years inside organizations operating under real commercial pressure. His journey includes building Garvis, an AI-native planning platform later acquired by Logility, which itself became part of Aptean. That arc alone tells a story about consolidation, scale, and where modern planning is heading. We spent time unpacking ideas from Piet's book, AI Compass for Supply Chain Leaders, particularly his view that planning drifted too far into abstract numbers and away from real-world context. Long before AI became a boardroom obsession, he saw how centralized models created distance between decisions and reality. When disruption arrives, whether through pandemics, tariffs, or geopolitical tension, that distance becomes costly. Piet shared vivid examples of how slow, spreadsheet-heavy processes fail precisely when speed and clarity matter most. One thread that kept resurfacing was data. Many leaders believe their data is "good enough" until volatility exposes blind spots. Piet pushed the conversation further, explaining that AI's value goes beyond crunching clean datasets. It can move understanding across silos, surface the reasons behind decisions, and make context visible without endless meetings. That idea of explainable, collaborative AI came up repeatedly, especially as a counterpoint to opaque automation that creates confidence without understanding. We also tackled the human side. There is anxiety around skills erosion and entry-level roles disappearing, but Piet's view was more nuanced. AI shifts where time and energy go, away from gathering information and toward judgment, fairness, and accountability. In his eyes, the real challenge for leaders is choosing the right scope. Projects that are too small fade into irrelevance, while those that are too big stall under their own weight. As we looked ahead, Piet reflected on how leadership itself may change as data becomes accessible to everyone. Authority based on instinct alone becomes harder to defend when assumptions are visible. The leaders who thrive will be those who can explain direction clearly, connect data to purpose, and bring people with them. So after hearing how planning, AI, and leadership are converging in real organizations today, how do you see the balance between human judgment and machine intelligence playing out in your own world, and are we truly ready for what that shift demands? Useful Links Connect with Piet Buyck The AI Compass for Supply Chain Leaders Book Logility Website Follow on LinkedIn  Thanks to our sponsors, Alcor, for supporting the show.  

Today's episode is a conversation with Bret Kinsella, recorded while he was in Las Vegas for CES and preparing to step onto the AI stage. Bret brings a rare combination of long-term perspective and hands-on experience. As General Manager of Fuel iX at TELUS Digital, he operates generative AI systems at a scale most enterprises never see, processing trillions of tokens and delivering measurable business outcomes for global organizations. That vantage point gives him a clear view of both the promise of generative AI and the uncomfortable truths many teams are still avoiding. Together, we unpack why generative AI breaks so many of the assumptions security teams have relied on for decades. Bret explains why these systems are probabilistic rather than deterministic, and how that single shift creates what he calls an unbounded attack surface. Users are no longer limited to predefined buttons or workflows, and outputs are no longer constrained to a fixed database. The same prompt can succeed or fail depending on subtle changes, which makes single-pass testing and checkbox compliance dangerously misleading. If you have ever wondered why an AI system feels safe one day and unpredictable the next, this conversation offers a grounded explanation. We also explore why focusing on the model alone misses the real risk. Bret makes a strong case that the model is only one part of a much larger system shaped by system prompts, connected data sources, tools, and guardrails. Change any one of those elements and behavior shifts. This is why automated, continuous red teaming has become unavoidable. Bret shares how Telus Digital's Fortify AI attack model uncovered hundreds of vulnerabilities in hours, far beyond what human teams could realistically surface on their own. Yet automation is not the end of the story. The final decisions still depend on people who understand context, trade-offs, and business impact. Throughout the discussion, we return to a simple but uncomfortable idea. AI safety is not something you bolt on after deployment. It demands a different mindset, broader testing, repeated validation, and ongoing human judgment. For leaders moving from experimentation to real-world deployment, this episode is a clear-eyed look at what responsible progress actually requires. So, as more organizations rush to deploy agents and autonomous systems in 2026, are we truly prepared for software that learns, adapts, and occasionally surprises us, and what does that mean for how you test and trust AI inside your own business? Useful Links Connect with Bret Kinsella Telus Digital Website Fuel iX Thanks to our sponsors, Alcor, for supporting the show.

What does it actually take to move beyond AI pilots and turn enterprise ambition into real productivity gains? That question sat at the center of my conversation with Olivia Nottebohm, Chief Operating Officer at Box, and it is one that every boardroom seems to be wrestling with right now. AI conversations have matured quickly. The early excitement has given way to harder questions about return, trust, and what changes when software stops assisting work and starts acting inside it. Olivia brings a rare vantage point to that discussion, shaped by leadership roles at Google, Dropbox, Notion, and now Box, where she oversees global go to market, customer success, and partnerships at a time when AI is becoming embedded in everyday operations. We talked about why early adopters are already seeing productivity lifts of around thirty seven percent, while others remain stuck in experimentation. The difference, as Olivia explains, is rarely the model itself. Strategy matters more. Teams that treat AI as a chance to rethink how work flows through the organization are pulling away from those that simply layer automation on top of broken processes. This is where unstructured content, often described as dark data, becomes a competitive asset rather than a liability. When that information is curated, permissioned, and ready for agents to use, entire workflows start to look very different. A large part of our discussion focused on AI agents and why 2026 is shaping up to be the year they move from novelty to necessity. Agents are already joining the workforce, taking on tasks that used to require multiple handoffs between teams. That shift brings speed and autonomy, but it also raises new questions about trust.  Olivia shared why governance has become one of the biggest blind spots in enterprise AI, especially when agents act independently or interact across platforms. Her perspective was clear. Without strong security, permissioning, and oversight, the risks grow faster than the rewards. We also explored why companies using a mix of models and agents tend to see stronger returns, and how Box approaches this with a neutral, customer choice driven philosophy while maintaining consistent governance. From the five stages of enterprise AI maturity to the idea of a future agent manager role, this conversation offers a grounded look at what AI at scale actually demands from leadership, culture, and operating models. So as investment accelerates and AI becomes part of the fabric of work, the real question is this. Are organizations ready to redesign how they operate around agents, data, and trust, or will they keep experimenting while others pull ahead, and what do you think separates the two?   Useful Links Connect with Olivia Nottebohm The State of AI in the Enterprise Report Becoming an AI-First Company Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

What happens when the systems we rely on every day start producing more signals than humans can realistically process, and how do IT leaders decide what actually matters anymore? In this episode of Tech Talks Daily, I sit down with Garth Fort, Chief Product Officer at LogicMonitor, to unpack why traditional monitoring models are reaching their limits and why AI native observability is starting to feel less like a future idea and more like a present day requirement. Modern enterprise IT now spans legacy data centers, multiple public clouds, and thousands of services layered on top. That complexity has quietly broken many of the tools teams still depend on, leaving operators buried under alerts rather than empowered by insight. Garth brings a rare perspective shaped by senior roles at Microsoft, AWS, and Splunk, along with firsthand experience running observability at hyperscale. We talk about how alert fatigue has become one of the biggest hidden drains on IT teams, including real world examples where organizations were dealing with tens of thousands of alerts every week and still missing the root cause. This is where LogicMonitor's AI agent, Edwin AI, enters the picture, not as a replacement for human judgment, but as a way to correlate noise into something usable and give operators their time and confidence back. A big part of our conversation centers on trust. AI agents behave very differently from deterministic automation, and that difference matters when systems are responsible for critical services like healthcare supply chains, airline operations, or global hospitality platforms. Garth explains why governance, auditability, and role based controls will decide how quickly enterprises allow AI agents to move from advisory roles into more autonomous ones. We also explore why experimentation with AI has become one of the lowest risk moves leaders can make right now, and why the teams who treat learning as a daily habit tend to outperform the rest. We finish by zooming out to the bigger picture, where observability stops being a technical function and starts becoming a way to understand business health itself. From mapping infrastructure to real customer experiences, to reshaping how IT budgets are justified in boardrooms, this conversation offers a grounded look at where enterprise operations are heading next. So, as AI agents become more embedded in the systems that run our businesses, how comfortable are you with handing them the keys, and what would it take for you to truly trust them? Useful Links Connect with Garth Fort Learn more about LogicMonitor Check out the Logic Monitor blog Follow on LinkedIn, X, Facebook, and YouTube. Alcor is the Sponsor of Tech Talks Network

Are we asking ourselves an honest question about who really owns automation inside a business anymore? In my conversation with Darin Patterson, Vice President of Market Strategy at Make, we explore what happens when speed becomes the default requirement, but visibility and structure fail to keep up.  Make has become one of the breakout platforms for teams that want to build automated workflows without writing code, and now, with AI agents joining the mix, the stakes feel even higher. Darin talks candidly about the tension between empowerment and chaos, especially in organizations that embraced no-code tools fast and early, only to discover that automation can quietly turn into sprawl if left unchecked. What struck me most is how strongly Darin challenges the idea that documentation alone can save modern IT teams. He argues that traditional monitoring tools and workflow documentation are breaking down under the weight of constant iteration. That's where Make Grid comes in. Make Grid creates an auto-generated, real-time visual map of a company's automation ecosystem, something Darin describes as a turning point for governance. He explains why this matters now, not later. As companies deploy AI into processes that used to be owned by specialists, Grid provides a shared lens for understanding what is running, who built it, and where dependencies exist. It's an answer to a problem many IT leaders are reluctant to admit publicly, that automation systems often grow faster than oversight systems ever could. Darin also offers a refreshingly grounded take on the psychology of ambitious teams. He talks about the need to prevent "no-code anarchy," a phrase I've heard whispered at conferences, but rarely unpacked with clarity. His view is simple, trust teams to build, but give them shared maps, guardrails, and governance that don't slow them down. That balance between autonomy and oversight becomes even more meaningful when AI is introduced into workflows that touch security, IT performance, and cross-team accountability. Make Grid attempts to solve that balance by showing the automation architecture visually, even when internal documentation has gone stale. So here's the question I want to leave you with, if AI agents can now design, connect, and deploy workflows across an organization, what role will visual governance play in keeping businesses both fast and accountable? And what does good oversight look like when humans are no longer the only builders in the system? Useful Links Learn more about Make Connect with Darin Patterson Thanks to our sponsors, Alcor, for supporting the show.

Was 2025 the year the games industry finally stopped talking about direct-to-consumer and started treating it as the default way to do business? In this episode of Tech Talks Daily, I'm joined by Chris Hewish, President at Xsolla, for a wide-ranging conversation about how regulation, platform pressure, and shifting player expectations have pushed D2C from the margins into the mainstream. As court rulings, the Digital Markets Act, and high-profile battles like Epic versus Apple continue to reshape the industry, developers are gaining more leverage, but also more responsibility, over how they distribute, monetize, and support their games. Chris breaks down why D2C is no longer just about avoiding app store fees. It is about owning player relationships, controlling data, and building sustainable businesses in a more consolidated market. We explore how tools like Xsolla's Unity SDK are lowering the barrier for studios to sell directly across mobile, PC, and the web, while handling the operational complexity that often scares teams away from global payments, compliance, and fraud management. We also dig into what is changing inside live service games. From offer walls that help monetize the vast majority of players who never spend, to LiveOps tools that simplify campaigns and retention strategies, Chris shares real examples of how studios are seeing meaningful lifts in revenue and engagement. The conversation moves beyond technology into mindset, especially for indie and mid-sized teams learning that treating a game as a long-term business needs to start far earlier than launch day. Here in 2026, we talk about account-centric economies, hybrid monetization models running in parallel, and the growing role of community-driven commerce inspired by platforms like Roblox and Fortnite. There is optimism in these shifts, but also understandable anxiety as studios adjust to managing more of the stack themselves. Chris offers a grounded perspective on how that balance is likely to play out. So if games are becoming hobbies, platforms are opening up, and developers finally have the tools to meet players wherever they are, what does the next phase of direct-to-consumer really look like, and are studios ready to fully own that relationship? Useful Links Connect with Chris Hewish on LinkedIn Learn more about Xsolla Follow on LinkedIn, Twitter, and Facebook Thanks to our sponsors, Alcor, for supporting the show.

In this episode of Tech Talks Daily, I'm joined by Kiren Sekar, Chief Product Officer at Samsara, to unpack how AI is finally showing up where it matters most, in the frontline operations that keep the global economy moving. From logistics and construction to manufacturing and field services, these industries represent a huge share of global GDP, yet for years they have been left behind by modern software. Kiren explains why that gap existed, and why the timing is finally right to close it. We talk about Samsara's full-stack approach that blends hardware, software, and AI to turn trillions of real-world data points into decisions people can actually act on. Kiren shares how customers are using this intelligence to prevent accidents, cut fuel waste, digitize paper-based workflows, and scale expert judgment across thousands of vehicles and job sites. The conversation goes deep into real examples, including how large enterprises like Home Depot have dramatically reduced accident rates and improved asset utilization by making safety and efficiency part of everyday operations rather than afterthoughts. A big part of our discussion focuses on trust. When AI enters physical operations, concerns around monitoring and surveillance surface quickly. Kiren walks through how adoption succeeds only when technology is introduced with care, transparency, and a clear focus on protecting workers. From proving driver innocence during incidents to rewarding positive behavior and using AI as a virtual safety coach, we explore why change management matters just as much as the technology itself. We also look at the limits of automation and why human judgment still plays a central role. Kiren explains how Samsara's AI acts as a force multiplier for experienced frontline experts, capturing their hard-won knowledge and scaling it across an entire workforce rather than trying to replace it. As AI moves from pilots into daily decision-making at scale, this episode offers a grounded view of what responsible, high-impact deployment actually looks like. As AI continues to reshape frontline work, making jobs safer, easier, and more engaging, how should product leaders balance innovation with responsibility when their systems start influencing real-world safety and productivity every single day? Useful Links Connect with Kiren Sekar Learn more about Samsara Tech Talks Daily is Sponsored by Denodo

What if airlines stopped thinking in terms of seats and schedules and started designing for the entire journey instead? In this episode of Tech Talks Daily, I'm joined by Somit Goyal, CEO of IBS Software, to talk about how travel technology is being rebuilt at its foundations. Since we last spoke, AI has moved from experimentation into everyday operations, and that shift is forcing airlines to rethink everything from retailing and loyalty to disruption management and customer trust. Somit shares why AI can no longer sit on the edge of systems as a feature, and why it now has to be embedded directly into how decisions are made across the business. We discuss the growing gap between legacy airline technology and rapidly rising traveler expectations, and why this tension has become a defining moment for the industry. For Somit, travel tech is no longer back office infrastructure. It is becoming the operating system for customer experience and revenue. That shift changes how airlines think about retailing, moving away from selling flights toward curating outcomes across a multi day journey that includes partners, servicing, and real time operational awareness. The conversation also explores why agility now matters more than scale, and how airlines are approaching this transformation without breaking what already works. A major part of this episode focuses on IBS Software's deep co-innovation partnership with Amazon Web Services. Somit explains why this is far more than a cloud hosting arrangement, covering joint R&D, shared roadmaps, and AI labs designed to help airlines build modern retailing capabilities faster. We also unpack what "AI first" really means in practice, how intelligence is reshaping offer creation, pricing, order management, and disruption handling, and why responsible AI must be treated as a product rather than a legal safeguard. We also spend time on loyalty, one of the industry's most stubborn challenges. Somit outlines why converging reservations and loyalty systems is such a powerful unlock, how it enables real time personalization instead of generic segmentation, and why loyalty should evolve from a points ledger into an experience engine that delivers value before, during, and after a trip. As airlines race toward 2026, the big question is no longer whether transformation will happen, but who will move with enough clarity and trust to earn long-term loyalty. In a world where AI knows more about travelers than ever before, how do airlines use that intelligence to create better outcomes without crossing the line, and are they ready to rethink the journey from end to end? Useful Links Connect with Somit Goyal Learn more about IBS Software Tech Talks Daily is Sponsored by Denodo  

What happens when a podcast stops being something you listen to and becomes something you physically show up for? In this episode of Tech Talks Daily, I wanted to explore a different kind of tech story, one rooted in community, endurance, and real human connection. I was joined by Sam Huntington, a Business Development Officer at Wells Fargo, who has quietly built something special at the intersection of technology, entrepreneurship, and cycling through his podcast and community project, Hill Climbers. Sam's story starts far from a studio. It begins on a bike, moving through Philadelphia, Los Angeles, and eventually Austin, where chance conversations on group rides turned into friendships, business relationships, and eventually a podcast. We talk about why endurance sports and startups share the same mental terrain, the moments when you want to quit, and how those moments often define the outcome. Sam explains how Hill Climbers evolved from recorded conversations into weekly rides, live podcast tapings, and in person events that bring founders, investors, and operators together without name badges or pitch decks. We also dig into what makes Austin such a magnetic place for founders right now, and why community building outside Silicon Valley feels different when it is built around shared effort rather than curated networks. Sam shares lessons learned from taking a podcast offline, including the early weeks when hardly anyone showed up, the temptation to stop, and the persistence required to build momentum. There is a refreshing honesty in how he describes growing something slowly, resisting shortcuts, and letting trust compound over time. This conversation is also a reminder that meaningful networks are rarely built through algorithms. They are built through shared experiences, discomfort, friendly competition, and showing up consistently when no one is watching. Whether you are a founder, an investor, or someone trying to build a community of your own, there is something grounding in hearing how relationships form when work is not the opening line. As more of our professional lives move online, are we losing the spaces where real connection happens, and what would it look like for you to build community around a shared passion rather than a job title? Userful Links Connect with Sam Huntington Hill Climbers Website Instagram Tech Talks Daily is Sponsored by Denodo

What happens to patient care when hospital systems suddenly go dark and clinicians are forced back to pen and paper in the middle of a crisis? In this episode of the Tech Talks Daily Podcast, I speak with Chao Cheng-Shorland, Co-founder and CEO of ShelterZoom, about a problem that many healthcare leaders still underestimate until it is too late. As ransomware attacks, cloud outages, and system failures become more frequent, electronic health record downtime has shifted from a rare incident to a recurring operational risk with real consequences for patient safety, staff wellbeing, and hospital finances. Chao explains why traditional disaster recovery plans fall short in live clinical environments and why returning to paper workflows is no longer viable for modern healthcare teams. We discuss how EHR downtime can stretch from hours into weeks, how reimbursement delays and cash flow pressure compound the damage, and why younger clinicians are often unprepared for manual processes they were never trained to use. The conversation also explores the mindset shift now taking place among CIOs and CISOs, as resilience moves from a compliance checkbox to a survival requirement. At the heart of the discussion is ShelterZoom's SpareTire platform and the thinking behind treating uninterrupted access to clinical data as a baseline rather than a backup. Chao shares how the idea emerged directly from hospital conversations, why an external, always-available system is essential during cyber incidents, and how ShelterZoom's tokenization roots shaped a design focused on security without disruption. We also look at how rising AI adoption is changing the threat landscape and why many healthcare organizations are reordering priorities to secure continuity before rolling out new AI initiatives. As we look toward 2026, this episode offers a grounded view of how healthcare organizations must rethink downtime tolerance, data governance, and operational readiness in a world where digital outages can quickly become clinical emergencies. If downtime is now inevitable rather than hypothetical, what does real resilience look like for hospitals, and are healthcare leaders moving fast enough to protect patients when systems fail? Useful Links Connect with Chao Cheng-Shorland Learn more about ShelterZoom Tech Talks Daily is Sponsored by Denodo

Is your website still the front door to your business, or has AI already quietly changed where customers first meet your brand? In this episode of the Tech Talks Daily Podcast, I sit down with Dominik Angerer, Co-founder and CEO of Storyblok, to unpack how content, search, and discovery are shifting in an AI-first world. As search behavior moves away from blue links toward direct answers inside tools like ChatGPT and Google summaries, Dominik explains why many businesses are seeing traffic decline even while signups and conversions continue to grow. We explore how AI is reshaping the role of content management systems, from automation and orchestration to personalization at scale. Dominik shares why consistency now matters more than volume, how outdated content can actively harm brand visibility inside AI answers, and why the technical foundations built for SEO still play a major role as generative search takes hold. This conversation also dives into headless CMS architecture, why separating content from presentation has become even more valuable, and how structured, well maintained content gives AI systems something reliable to work with. Dominik also introduces the idea of joyful content, a belief that better tools lead to better work and ultimately better experiences for audiences. From AI-powered support workflows to personalized retail and loyalty experiences, he shares real examples of how forward-looking teams are already using content as an active system rather than a static archive. As businesses look toward 2026 and rethink how they show up across websites, apps, agents, and answer engines, this episode offers a grounded look at what needs to change and where to start. As AI becomes the place people go for answers rather than search results, how are you rethinking your content strategy, and what will you do differently after hearing this conversation? Connect with Dominik Angerer Learn more Storyblok Tech Talks Daily is Sponsored by Denodo

What happens when the push for smarter crypto wallets runs headfirst into the reality that everything on a public blockchain can be seen by anyone? In this episode of Tech Talks Daily, I wanted to take listeners who may not live and breathe Web3 every day and introduce them to a problem that is becoming harder to ignore. As Ethereum evolves and smart accounts unlock new wallet features, the surface area for risk grows at the same time. That is where privacy-first Layer 2 solutions enter the conversation, not as an abstract idea, but as a practical response to very real security and usability concerns. My guest is Joe Andrews, Co-founder and President at Aztec Labs. Joe brings an engineering mindset shaped by years of building consumer-facing applications and deep privacy infrastructure. Together, we unpack why privacy and security can no longer be treated as separate topics, especially as Ethereum rolls out more advanced account features. Joe explains how privacy-first Layer 2 networks act as an added line of defense, reducing exposure to threats that come from fully transparent balances, identities, and transaction histories. We also talk about what Aztec actually is, often described as the Private World Computer, and why that framing matters. Joe shares learnings from Aztec's public testnet launch earlier this year, what surprised the team once thousands of nodes were running in the wild, and how the community has stepped up in ways the company itself could not have planned for. There is also an honest discussion about the UK crypto scene, the missed opportunities, and the quiet resilience of builders who continue to ship despite regulatory uncertainty. As we look ahead, Joe outlines what comes next as Aztec moves closer to enabling private transactions on a decentralized network, and why the next phase is less about theory and more about real people using privacy in everyday interactions. If you are curious about how privacy-first Layer 2 solutions fit into Ethereum's roadmap, or why privacy might be the missing piece that finally makes smart wallets usable at scale, does this conversation change how you think about the future of crypto, and where would you like to see this technology go next? Useful Links Connect with Joe Andrews Learn more about Aztec Labs Tech Talks Daily is Sponsored by Denodo