Are businesses really making progress with AI, or are many still stuck using it for the digital equivalent of making phone calls on a smartphone?

In this episode, I sit down with Alison Kay, VP / Managing Director AWS UKI, to unpack what is actually happening behind the headlines of AI adoption across the UK.

On paper, the numbers look strong. Around 64% of UK businesses are now using AI, a sharp rise from the previous year. But when you look closer, the story shifts. Only one in four organizations have moved into more advanced use cases, where real productivity gains, efficiency improvements, and innovation start to show up in meaningful ways.

So what is holding everyone back?

In our conversation, Alison shares insights from AWS research and her work with organizations ranging from major enterprises like Barclays and the BBC to fast-moving startups. We explore why skill shortages are slowing progress, why many companies struggle to move beyond basic use cases, and how governance and trust are becoming central to scaling AI responsibly.

We also spend time breaking down the rise of agentic AI, a term that is starting to appear everywhere. Instead of simply generating answers, these systems are beginning to take action, writing code, testing software, and working alongside humans to dramatically accelerate delivery timelines. Alison shares a powerful example where a project that might have taken 40 engineers over two years was completed by six engineers in just 76 days with the support of AI agents.

Along the way, we look at real-world examples from companies like Trainline and Evri, showing how AI is already reshaping customer experience and operational efficiency in ways that go far beyond theory.

This episode is a must-listen for business leaders trying to understand where AI is delivering real value today, where the biggest gaps still exist, and how to move from experimentation to meaningful transformation.

So if your organization is already using AI, the real question becomes this, are you using it to improve what you already do, or are you ready to rethink how your business operates entirely?

Useful Links

• Connect with Alison Kay
• Unlocking the UK's AI Potential" report.

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What if one of the most influential figures in modern technology had almost ignored the opportunity that would define his career?

In this episode, I sit down with Werner Vogels, Chief Technology Officer at Amazon, to explore the story behind Amazon Web Services as it marks its 20th anniversary, and how a near-dismissed phone call turned into a front-row seat to one of the biggest shifts in computing history.

Werner takes me back to the early days when Amazon was still seen as "just a bookstore," and shares what he discovered when he first stepped inside what he calls Amazon's "technology kitchen." What he found was a company solving problems at a scale that commercial software simply could not handle, forcing them to build everything themselves. That mindset would go on to shape everything from Dynamo to the foundations of modern cloud infrastructure.

We also unpack the thinking behind one of the most important shifts in enterprise technology, the move from upfront licensing to pay-as-you-go. It sounds obvious now, but at the time it challenged how the entire industry operated, giving businesses the ability to experiment, scale, and take control of their own costs in ways that were not possible before.

Looking ahead, Werner offers a refreshing perspective on AI and what he describes as a developer renaissance. While many headlines focus on replacement, he sees AI as a tool that amplifies human capability, placing even greater importance on curiosity, ownership, and collaboration. It is a reminder that while tools will continue to evolve, responsibility and decision-making still sit firmly with the people using them.

This episode is a must-listen for anyone building, leading, or investing in technology. It connects the dots between past, present, and what comes next, showing how today's AI wave echoes the same patterns that shaped the cloud revolution.

So as we look toward the next era of computing, the question is simple, are we ready to think at the scale required to build what comes next?

Useful Links

• Connect with Werner Vogels

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Can faster access to real-world data actually change patient outcomes, or are we still too reliant on controlled clinical trials to see the full picture?

In this episode, I sit down with Dr. Alex Asiimwe, Executive Director of Epidemiology at Gilead Sciences, to explore a topic that doesn't get enough attention in the AI conversation, real-world evidence.

While much of the industry focuses on AI in drug discovery or diagnostics, Alex brings a different perspective, one rooted in what happens after treatments reach real patients in the real world. As he explains, clinical trials may be the gold standard, but they are still controlled environments. Real-world evidence is where we begin to understand how treatments perform across diverse populations, healthcare systems, and everyday conditions.

What stood out in our conversation is just how messy and fragmented that real-world data can be. Much of it is not collected for research purposes, which means it takes months, sometimes up to a year, to clean, structure, and analyze before it can inform decisions. Alex shares how AI is beginning to change that, not by replacing human expertise, but by automating the most time-consuming parts of the process. If that timeline can be cut in half, the impact is immediate.

Faster evidence means faster decisions, and in healthcare, delays in evidence can directly affect patient outcomes.

We also explore what Alex describes as the "analytics gap," the disconnect between where data exists and where insights are actually generated. Today, much of the evidence used in drug development still comes from limited datasets, often from a single country or region. Yet the treatments themselves are global. That mismatch creates blind spots, particularly in low and middle-income countries where data is often unstructured, fragmented, or simply not accessible. AI has the potential to standardize and unlock that data, helping to create a more complete and representative view of patient populations worldwide.

Of course, the challenges are not just technical. Trust, governance, and politics all play a role in whether data can be shared and used effectively. Alex is clear that the biggest barrier is not the science or the analytics, it is building trust between organizations, governments, and communities. Without that, even the most advanced AI models cannot deliver meaningful outcomes.

This conversation also touches on the importance of collaboration, not just between healthcare organizations and technology providers like SAS, but across the global ecosystem. Alex highlights how partnerships, open standards, and shared frameworks can help close the analytics gap and accelerate progress in areas like HIV prevention, where understanding real-world patient behavior is critical.

As we wrap up, one message comes through clearly. AI is not a miracle solution, and it will not transform healthcare overnight. But when applied to the right parts of the workflow, especially around data preparation and evidence generation, it can create measurable, meaningful change.

So as healthcare leaders look to move beyond pilots and into real impact, the question becomes, are we focusing on the right problems, and are we ready to open up the data needed to solve them?

Useful Links

• Connect with Dr. Alex Asiimwe
• OHDSI – Observational Health Data Sciences and Informatics

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