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.