3551: AI That Delivers at Scale: Inside HGS and Real Business Transformation

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 Thanks to our sponsors, Alcor, for supporting the show.

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