In this episode of The Databricks Diaries, host Andy sits down with Steve Green, a senior data leader who has navigated the high-stakes intersection of data and AI from both the regulatory halls of the FCA and the front lines of private banking. Steve strips away the persistent AI hype to offer a masterclass on building a truly "AI-ready" business within the rigorous constraints of the financial sector.

Steve explains why the "garbage in, garbage out" reality remains the biggest hurdle to success and how a horizontal architectural view must prioritise business outcomes over technology for its own sake. The conversation reveals how to align legal, compliance, and IT through ethical control frameworks that maintain trust in generative AI outputs while ensuring foundational data hygiene remains a non-negotiable priority. By moving beyond the simple automation of annoying tasks, Steve challenges leaders to re-engineer end-to-end business processes to capture true value.

In this episode of The Databricks Diaries, I sit down with Tom Heath, a data and AI leader who has spent the last 20+ years working across data, semantic technologies, machine learning, and organisational transformation.

Tom shares a really thoughtful perspective on how the conversation has shifted — from having to convince people AI mattered, to helping organisations work out what to actually do with it.

We talk about what it takes to lead AI inside large, complex businesses, why so many projects still fail, and why technical capability on its own is never enough.

This is a grounded conversation on strategy, leadership, operating models, and the real work required to turn AI into something meaningful.

1. Why the public arrival of ChatGPT changed executive conversations overnight
2. The link between data structure, semantics, and the future of intelligent agents
3. Why AI success depends on more than just technical execution
4. The difference between efficiency, quality, and innovation as AI value drivers
5. Why some AI projects succeed technically but still fail commercially
6. The importance of problem definition before touching the technology
7. What leaders should focus on when building AI capability inside large organisations

In this episode of The Databricks Diaries, I sit down with Chris Hounslow, Head of Data Science & AI at Lebara, to talk about what it actually takes to turn AI from hype into real business impact.

Chris shares the journey behind Lebara’s transformation — from building solid data foundations to rolling out production-grade AI tools across a lean team… and winning industry recognition along the way.

We get into the realities of operating with limited resource, choosing the right use cases, and why most AI projects fail after the proof of concept stage.

If you’re trying to scale AI in a real business (not just experiment with it), this one’s worth your time.

1. Why the shift from “data science” to “AI” is often just rebranding, not reinvention
2. How to run a high-performing, lean AI team (5 people, 80% success rate)
3. Why being problem-led (not tech-led) is the key to choosing the right use cases
4. The importance of end-to-end ownership (not just building models)
5. Why most AI projects fail after the POC stage
6. How to build trust and adoption with non-technical stakeholders
7. The role of data foundations as the real competitive advantage
8. Why testing and validation are the biggest blockers to scaling AI