Tech Talks Daily

What does it really take to move AI from experimentation into something enterprises can trust, scale, and rely on every day?
In this episode of Tech Talks Daily, I'm joined by Rob Lay, CTO and Solutions Engineering Director for Cisco UK and Ireland, recorded in the run-up to Cisco Live EMEA in Amsterdam. As agentic AI dominates conference agendas on both sides of the Atlantic, this conversation steps away from model hype. It focuses on the less glamorous, but far more decisive layer underneath it all: infrastructure.
Rob explains why the biggest constraint on scaling AI agents in production is no longer imagination or ambition, but the readiness of the environments those agents run on. We talk about how legacy technical debt, latency, fragmented networks, and disconnected security tools can quietly undermine AI investments long before leaders see any return. As organizations move out of pilot mode and into real execution, those cracks become impossible to ignore.
A big part of the discussion centers on why AI changes the relationship between network, compute, and security teams. Traditional silos struggle to keep up as autonomous systems make decisions at machine speed. Rob shares how Cisco is approaching this shift through tighter integration across the stack, with security designed directly into the network rather than bolted on later. When AI agents act independently, routing everything through centralized chokepoints does not hold up.
We also explore how operational complexity is evolving. Tool sprawl is already overwhelming many IT leaders, and agent sprawl is clearly coming next. Rob outlines Cisco's platform strategy, including how agent-driven operations, human oversight, and context-aware automation are shaping a new approach to day-to-day resilience. This leads into a wider conversation about digital resilience as a business issue, where visibility, assurance, and learning from incidents matter more than static continuity plans that only get tested once a year.
For European leaders in particular, data sovereignty and control remain at the forefront. Rob explains how Cisco is responding with flexible deployment models, local data residency options, and air-gapped environments that support AI innovation without forcing customers into a single rigid operating model.
We close by looking at where enterprises are actually seeing value today, where expectations are still running ahead of reality, and what leaders attending Cisco Live should really be listening to as announcements roll in. If you are responsible for infrastructure, security, or technology strategy in an AI-driven organization, this conversation offers a grounded view of what needs to be ready before agents can truly deliver on their promise.
As AI-powered systems start to move faster than most roadmaps anticipated, are you confident the foundations underneath them are ready to keep up, and what would you change if you were starting that journey today?
Useful Links
Connect with Rob Lay
Cisco Live
Follow Cisco on LinkedIn

What does it really take to move enterprise AI from impressive demos to decisions that show up in quarterly results?
One year into his role as Global Managing Partner at IBM Consulting, Neil Dhar sits at the intersection of strategy, capital allocation, and technology execution. Leading the firm's Americas business and a team of close to 100,000 consultants, he has a front-row view into how large organizations are reassessing their AI investments.
From global healthcare leaders like Medtronic to luxury retail brands such as Neiman Marcus, the conversation has shifted. Early proofs of concept helped executives understand what was possible. Now the focus is firmly on proof of value and on whether AI can drive growth, competitiveness, and measurable return.
In this episode, I speak with Neil Dhar about what has changed in the boardroom over the past year and why ROI has become the central question.
Drawing on more than three decades in finance and private equity, including senior leadership roles at PwC, Neil explains why AI is increasingly being treated as a capital allocation decision rather than a technology experiment.
Every dollar invested has to earn its place, whether through productivity gains, operational improvement, or new revenue opportunities. Vanity projects no longer survive scrutiny, especially when boards and investors expect results on a much shorter timeline.
We also explore how IBM is applying these same principles internally. Neil shares how the company has identified hundreds of workflows across the business, prioritized those with the strongest economic impact, and used AI and automation to drive large-scale productivity gains. The result is a potential $4.5 billion in annual run rate savings by 2025, with those gains being reinvested into innovation, people, and future growth.
It is a candid look at what happens when AI strategy, leadership accountability, and disciplined execution come together inside a global organization.
If you are a business leader trying to separate real value from hype, or someone wrestling with how to justify AI spend beyond experimentation, this conversation offers a grounded perspective on what enterprise AI looks like when it is treated as a business decision rather than a technology trend.
Are you ready to rethink how AI earns its place inside your organization, and what proof of value really means in 2026?
Useful Links
Connect With Neil Dhar
IBM Institute for Business Value, "The Enterprise in 2030" study
Learn More About IBM Consulting

How Do Marketplaces Turn AI Ambition Into Scalable, Trusted Enterprise Reality? That is the question I explore in this episode with Julie Teigland, Global Vice Chair for Alliances and Ecosystems at EY, someone who sits right at the intersection of enterprise demand, technology platforms, and the ecosystems that increasingly power modern AI adoption.
As organizations race to deploy AI at scale, many are discovering that the real challenge is not a lack of tools, but the complexity of choosing, integrating, governing, and standing behind those decisions with confidence.
Julie explains why marketplaces are becoming a powerful mechanism for reducing friction in this process, helping enterprises move beyond experimentation toward AI solutions that are trusted, scalable, and aligned with real business outcomes. We talk about how marketplaces can collapse complexity, curate choice, and bring much needed clarity to leaders who are overwhelmed by the sheer volume of AI options available today.
Julie also shares how EY approaches this challenge through its "client zero" mindset, turning the lens inward and treating EY itself as the first marketplace customer.
By doing so, EY stress tests governance, security, and integration at real enterprise scale, serving tens of thousands of clients, running hundreds of thousands of servers, and processing hundreds of millions of transactions every day. That internal experience shapes how EY helps clients navigate trust, accountability, and cross-vendor integration risks, particularly as AI becomes more embedded into workflows and decision-making.
We also explore how strong alliances with cloud leaders like Microsoft and SAP are shaping how AI solutions are vetted, standardized, and deployed across industries, as well as how regulation, particularly in Europe, is influencing a shift toward responsibility by design.
This conversation goes beyond technology to focus on orchestration, trust, and outcomes, and why marketplaces are evolving from simple app stores into something far more strategic for enterprise AI.
If you are trying to understand how ecosystems, governance, and marketplaces can help turn AI from isolated projects into sustained business value, this episode offers a thoughtful and grounded perspective.
 I would love to know what resonated with you most. How do you see marketplaces shaping the future of AI adoption inside your organization?
Useful LInks
Connect With Julie Teigland
Learn More About EY

As someone who spends a lot of time covering AI announcements, product launches, and conference stages, it is easy to forget that most AI today is still built for desks, screens, and digital workflows.
Yet the reality is that the vast majority of the global workforce operates in the physical world, on roads, construction sites, depots, and job sites where mistakes are measured in injuries, collisions, and lives lost. That gap between where AI innovation happens and where real risk exists is exactly why I wanted to sit down with Amish Babu, CTO at Motive.
In this episode, I speak with Amish about what it truly means to build AI for the physical economy. We unpack why designing AI for vehicles, fleets, and safety-critical environments is fundamentally different from building AI for emails, documents, or dashboards.
Amish explains why latency, trust, and reliability are non-negotiable when AI is embedded directly into vehicles, and why edge AI, multimodal sensing, and on-device compute are essential when milliseconds matter. This is a conversation about AI that has to work perfectly in messy, unpredictable, real-world conditions.
We also explore how Motive approaches AI as a full system, combining hardware, software, and models into a single platform built specifically for life on the road. Amish shares how AI can help prevent collisions, support drivers in the moment, and create measurable safety and operational outcomes for fleets operating across transportation, construction, energy, and public sector environments. Along the way, we challenge common misconceptions around AI in vehicles, including the idea that it is about surveillance rather than protection, or that all AI systems are created equal when lives are on the line.
If you are interested in how AI moves beyond productivity tools and into high-stakes environments where safety, accountability, and trust matter most, this episode offers a grounded and practical perspective from someone building these systems every day. I would love to hear your thoughts on this one. How do you see the role of AI evolving as it moves deeper into the physical world?
Useful Links
Connect with Amish Babu
Learn More About Motive
How Motive's AI works: Real-time edge intelligence, humans-in-the-loop, and continuous improvement.

*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "54141b02-3c0e-46be-b764-c57b8d9d7ccc" data-testid= "conversation-turn-28" data-scroll-anchor="true" data-turn= "assistant"> In this episode of Tech Talks Daily, I sat down with Jinsook Han, Chief Agentic AI Officer at Genpact, to unpack one of the most misunderstood shifts in enterprise AI right now.
Many organizations feel confident about the value AI can deliver, yet only a small fraction are able to move beyond pilots and into autonomous operations that actually scale. Genpact's Autonomy By Design research puts hard data behind that gap, and Jinsook explains why optimism often races ahead of readiness.
We explore why agentic AI changes the rules entirely. When AI systems begin to act, decide, and adapt on behalf of the business, familiar operating models start to strain.
Jinsook makes a compelling case that agentic AI cannot be treated like another software rollout. It demands a rethink of data, governance, roles, and even how teams define work itself. The shift from tools to teammates alters expectations for people across the organization, from frontline operators to the C-suite, and exposes just how unprepared many companies still are.
Governance is a major theme throughout the conversation, but not in the way most leaders expect. Rather than slowing progress, Jinsook argues that governance must become part of how work happens every day.
She shares how Genpact approaches agent certification, maturity, and oversight, using vivid analogies to explain why quality and alignment matter more than simply deploying large numbers of agents. We also dig into why many governance models fail, especially when they rely on committees instead of lived understanding.
Upskilling sits at the heart of this transformation. Jinsook walks through how Genpact is training more than 130,000 employees for an agentic future, starting with executives themselves. The focus is not on abstract learning, but on proving that today's work looks different from yesterday's. Observability, explainability, and responsible AI are woven into this approach, with command centers designed to monitor both agent performance and health, turning early signals into opportunities rather than panic.
This conversation goes well beyond hype. It is about readiness, responsibility, and the reality of building autonomous systems that still depend on human judgment. As organizations rush toward agentic AI, are they truly prepared to change how decisions are made, how people work, and how accountability is defined, or are they still treating AI as a faster hammer rather than a new kind of teammate?




Useful Links
Connect with Jinsook Han
Learn More about Genpact