Tech Talks Daily

Is 2026 the year AI finally has to prove it is worth the investment?
In this episode, I'm joined by Chris Riche-Webber, VP of Business Intelligence and Analytics at SmartRecruiters, to explore why so many AI and agentic AI initiatives stall after the pilot phase and what separates the projects that scale from the ones that quietly disappear. With Gartner predicting that more than 40 percent of agentic AI programs could be cancelled by 2027, Chris brings a pragmatic, data-led perspective on what is really happening inside organizations as the hype meets operational reality.
We talk about the fundamentals that have not changed despite the new technology. Influence, clearly defined problems, measurable impact, and adoption still determine success, yet they are often overlooked in the rush to deploy the latest tools. Chris explains why "good vibes" are no longer enough in front of a CFO, how to baseline outcomes properly, and why ownership of results is one of the most common missing pieces in enterprise AI programs.
A big part of the conversation focuses on what Chris calls the "agent washing" problem. Just as products are sometimes marketed with fashionable labels that do not reflect their real value, many solutions are being positioned as agentic without delivering true autonomy or business outcomes. We discuss how leaders can cut through the noise by asking better questions, aligning technology to specific use cases, and recognizing when simple automation is the right answer.
Trust, adoption, and measurable ROI emerge as the three signals that determine whether an AI initiative survives. Chris shares a clear framework for defining these signals in a way that is consistent, comparable over time, and meaningful to the executive team. We also explore how connecting talent decisions to revenue, productivity, and retention changes the conversation, especially in the context of SmartRecruiters' broader SAP ecosystem and the opportunity to link people data directly to business performance.
This is a conversation about moving from experimentation to accountability, from buying narratives to solving real problems, and from technology-first thinking to outcome-first leadership.
So as the window for easy wins closes and the demand for proof of value grows, will your AI strategy be remembered as a pilot that generated excitement or as an initiative that delivered measurable business impact?

What if the real AI race in 2026 isn't about building bigger models, but about where decisions are made, how fast they happen, and whether they deliver measurable value?
In this episode, I'm joined by John Bradshaw, Director of Cloud Computing Technology and Strategy at Akamai, to unpack his predictions for the next phase of cloud, AI inference, and the economics that will shape enterprise technology over the next 12 months. As organizations move beyond experimentation, John explains why the boardroom conversation has shifted from capability to return on investment, and how spiraling compute demands are forcing leaders to rethink the balance between performance, cost, and innovation.
We explore why this new financial scrutiny is not slowing AI adoption, but refining it. John shares how inefficient GPU workflows, centralized inference, and poorly aligned architectures are being challenged by a more disciplined approach that pushes intelligence closer to the edge. This shift is not only about latency and performance. It is about building scalable, value-driven platforms that can support real-time decision-making, agentic workloads, and global user experiences without breaking traditional IT budgets.
Trust is another major theme throughout our conversation. From the rise of everyday AI agents that quietly handle routine tasks to the growing importance of secure, resilient inference pipelines, John outlines how low-latency edge infrastructure, local processing, and hybrid cloud models will redefine reliability for both enterprises and consumers. We also discuss the smart home backlash following recent outages, and why the next generation of connected products will be designed to work even when the network does not.
The episode also looks at the future of streaming, where consolidation, intelligent content delivery, and AI-driven personalization are reshaping both the user experience and the economics behind the platforms. Behind the scenes, orchestration is emerging as a defining capability, with multiple models and services working together to validate outputs, reduce hallucinations, and create more dependable AI systems.
This is a conversation about moving from possibility to production, from experimentation to accountability, and from centralized architectures to distributed intelligence.
So as AI becomes embedded in every workflow and every customer interaction, will the winners be the companies with the biggest models, or the ones that know exactly where their AI should live, how it should be orchestrated, and how it proves its value every single day?

What happens when AI moves from a standalone tool to a teammate that works inside the flow of your organization?
In this episode, I'm joined by Mick Hodgins, General Manager for EMEA at Notion, to explore how the idea of a connected AI workspace is reshaping the way teams collaborate, make decisions, and measure productivity. With a career that includes more than a decade at Google scaling growth across multiple countries, Mick brings a unique perspective on what it takes to build technology businesses across diverse markets and why this moment in AI feels fundamentally different from previous waves of innovation.
We talk about Notion's journey from a flexible, block-based collaboration platform to an AI-native workspace where context is the real differentiator. Mick explains why AI performs better when it understands how work actually happens, and how embedding agents directly into shared workflows allows teams to move from prompting tools to orchestrating outcomes. From automated reporting and knowledge management to self-improving agent loops that learn from their own performance, the conversation brings to life how organizations are already using AI to remove the "work around the work" and focus on higher-value thinking.
A major theme throughout the discussion is return on investment. In a world where many companies are still stuck in pilot mode, Mick shares how leaders can reframe ROI around productivity, speed, and the elimination of repetitive tasks rather than treating AI as a single project with a fixed payback period. We also explore how roles, org structures, and hiring priorities are beginning to shift as agents become extensions of team capability rather than experimental add-ons.
Because Mick leads the EMEA region, we also dive into the differences in adoption between the US and Europe, from regulatory considerations and cultural attitudes to the growing strength of the European startup ecosystem. It's a balanced view that recognizes both the caution and the creativity emerging across the region.
This is ultimately a conversation about friction. What happens to an organization when coordination overhead disappears, when reporting builds itself, and when knowledge stays current without human intervention?
So as AI agents move from novelty to infrastructure, are businesses ready to redesign how work gets done, and what becomes possible when teams stop managing tasks and start compounding impact?

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Is your cloud foundation ready for the explosion of AI workloads, or are you about to scale technical debt at the speed of innovation?


In this episode, I'm joined by Apurva Kadakia, Global Head of Cloud and Partnerships at Hexaware, an AI-first transformation company helping enterprises modernize the core systems that will determine whether their AI strategies succeed or stall. With a front-row seat to large-scale cloud programs across industries, Apurva explains why so many organizations that "moved to the cloud" still find themselves unprepared for what comes next, and why modernization-led migration has become a business priority rather than a technology upgrade.
We unpack the real warning signs that cloud environments are not fit for AI, from monolithic architectures and spiraling compute costs to hidden integration complexity and security gaps that only surface at scale. Apurva introduces the idea of "clarity before cloud," a structured approach to understanding sprawling application estates, identifying what truly matters to the business, and matching each workload to the right modernization path using the five R's. It's a conversation that moves beyond theory into the practical decisions leaders need to make now if they want to avoid being locked out of future innovation.
The role of AI inside the transformation journey is another major theme. Rather than treating AI as a destination, Apurva shares how AI-led and human-perfected assessment models are already accelerating application discovery, classification, and migration planning, completing the majority of the heavy lifting while keeping human judgment firmly in control. We also explore why governance cannot be an afterthought, and how a dedicated Cloud Transformation Office can drive adoption, reskilling, stakeholder alignment, and data readiness without slowing delivery.
Looking ahead to a world of agentic systems and rapidly multiplying cloud workloads, this episode offers a clear message. The organizations that win will not be the ones that adopted cloud first, but the ones that modernized with intent.
So as AI moves from experimentation to enterprise scale, are your applications, your architecture, and your operating model truly ready to support it, or is now the moment to rethink your path before the next wave hits?

What if the biggest transformation in hospitality isn't happening in the dining room, but in the kitchen you never see?
In this episode, I'm joined by James Pool, Chief Technology and Operations Officer at Middleby, a company quietly powering more than a hundred brands across commercial foodservice and food processing. With more than three decades spent accelerating how food is cooked, prepared, and delivered at scale, James offers a rare look inside the technology, automation, and connected platforms reshaping how some of the world's most recognizable restaurant and retail brands operate.
We explore what the connected, IoT-enabled kitchen actually looks like in practice, and why James prefers to think of it as digital automation for the entire restaurant. From front-of-house energy optimization to automated food safety reporting and real-time equipment intelligence in the back, the conversation reveals how data is being used to reduce waste, improve uptime, simplify training, and ultimately increase profitability at the store level. This isn't about adding more screens or more complexity, it's about removing friction from every step of the operation.
James also shares how Middleby is bringing together a vast portfolio of technologies, from rapid-cook ovens and ventless kitchens to robotics and AI-driven service insights, into a single harmonized experience. That integration is opening the door to new formats such as ghost kitchens and non-traditional locations, where food can be prepared almost anywhere without the constraints that once defined a commercial kitchen. Along the way, we discuss how brands like Yum! Brands, Dunkin', Domino's, and Kroger are balancing speed, consistency, cost control, and customer experience in an environment where every investment must prove its return.
The episode also takes us inside Middleby's Innovation Kitchens around the world, where operators can experiment with layouts, workflows, and equipment in real conditions before committing capital in the field. It's a powerful reminder that the future of hospitality is being prototyped long before it reaches the high street.
So as automation, AI, and real-time analytics move from the factory floor into the heart of the restaurant, is the smart kitchen becoming the most important competitive advantage in foodservice, and are brands ready to rethink how their entire operation is designed around it?