In this episode of the Databricks Diaries, host Andy is joined by Gordon Logan, Director of Data Services at PRS for Music, for a deep dive into the unique challenges of preparing a century-old organisation for the AI era. Managing data for over 180,000 members—including composers, songwriters, and publishers—Gordon shares how his team navigates astronomical data volumes driven by the modern shift to streaming and digital consumption. He discusses the critical transition from isolated experiments to a structured, momentum-driven AI strategy backed by executive leadership.

Gordon explores the essential components of building an AI-ready culture, prioritising data literacy and "AI efficacy" to ensure the workforce uses powerful tools like Copilot ethically and responsibly. The conversation highlights a disciplined, business-led approach to use cases, moving away from "technology vanity exercises" and focusing instead on measurable outcomes like accurate royalty distribution and improved member services. Listeners will learn how PRS for Music employs a cross-functional AI team to stress-test hypotheses, ensuring that every technological advancement facilitates tangible business value while protecting the sensitive data of the UK’s creative community.

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

In this episode of the Databricks Diaries, Andy sits down with Mike Leverington, Director of Data Analytics at Skyscanner, to discuss the practical realities of AI readiness in a rapidly evolving tech landscape. Mike shares why successful AI integration begins with a cultural mindset rather than just technical infrastructure, urging leaders to "lean in" and embrace experimentation even amidst market volatility and uncertainty.

The conversation explores the threefold impact of Generative AI: driving internal productivity, revolutionising the customer experience, and disrupting entire industries. Mike provides actionable advice for data leaders on securing board buy-in, leveraging "small lab" innovation teams, and why the current hype cycle presents a unique opportunity to finally master self-serve analytics. From role-modelling AI use in daily admin tasks to "unlearning" how to Google, this episode is a masterclass in driving the last mile of the data value chain

AI isn’t failing because of the models. It’s failing because organisations can’t scale and adopt it.

Everyone’s talking about AI readiness. Very few are talking about what actually happens after you build something.

In this episode, I sit down with Magnus — a seasoned Data & AI leader who’s spent 20+ years helping global organisations (UEFA, Unilever, Worldpay, National Energy System Operator) move from experimentation to real impact.

We go beyond the usual “data, tools, roadmap” conversation and dig into what’s really holding organisations back.

This is a more honest look at where AI programmes succeed… and where they quietly stall.

If you’re investing in AI but not seeing meaningful ROI, this episode will likely hit home.

In this episode of the Databricks Diaries, we sit down with Tim, the Head of Data Science at The Citation Group, to explore his journey from a PhD in statistics at Lancaster University to leading AI innovation in a private equity-backed firm. Tim discusses the transition from the massive R&D landscapes of companies like Shell to the resource-constrained environment of an SME, where he now balances experimental projects with the need for immediate returns.

Throughout the conversation, he provides a masterclass on scaling data functions, from the importance of building a "proper" foundation to avoid technical debt to his "fire starter" strategy for managing and retaining high-performing talent. We also dive into his team’s National AI Award-winning project on call-back prioritization, which utilizes LLMs and sentiment analysis to optimize sales efficiency, and the "AI Champion" scheme - an initiative empowering over 200 employees across the business to experiment with the latest generative AI tools.

This episode offers a grounded look at how to maintain a "scientific method" mindset while navigating the rapid release cycles of platforms like Databricks.

In this episode of the Databricks Diaries, Sunando Das, Global head of Predictive Analytics at Unilever shows how they approach AI readiness at scale, balancing proven predictive models with emerging generative AI to drive measurable business growth. Drawing from years of applied AI across demand forecasting, marketing, innovation, and investment efficiency, Sunando estimates AI's potential to unlock 2–3% incremental growth when applied with discipline and focus.

The conversation tackles one of the biggest challenges in AI today: why only around 5% of generative AI pilots make it into production. Sunando explains why closing this gap requires more than better models, it demands cultural change, shared ownership, and a clear expectation that experiments must convert into real business outcomes.

We explore how cross-functional collaboration between business experts and technical teams is essential, why AI must be treated as a shared business asset rather than a data team initiative, and how conversational interfaces and agent-based workflows are reshaping how organisations interact with data. From continuous upskilling to test-and-learn experimentation, this episode offers practical guidance for organisations looking to move beyond pilots and turn AI into sustained impact.