S7 | Ep 30 | Leader of the Year, Di Mayze, Chief Data & AI Officer, Marks and Spencer

In Episode 30 of Season 7 of Driven by Data: The Podcast, Catherine Dowden-King is joined by Di Mayze, Chief Data and AI Officer at Marks & Spencer, where they discuss why the CDO role has to prove its value, why clean data foundations still come before AI, and how to build a data culture people want to be part of.

As featured in Driven by Data Magazine 2026.

As the first person in post at M&S, and previously the first global head of data and AI at WPP, Di shares what she learned at dunnhumby, Boots and WPP, why curiosity matters more than jargon, and how she is balancing innovation with strong data foundations.

They also discuss:

• What dunnhumby and Tesco taught Di about putting data at the heart of every decision.
• Why she is comfortable saying “I don’t know”, and how that curiosity shapes her teams.
• Why great data leaders make a subject accessible rather than hiding behind acronyms.
• How to build confidence in asking “what do you mean?” when vendors say a product is AI powered.
• How Di built a 3,000-strong data and AI community at WPP through influence rather than power.
• Why trust, credibility and championing your team’s work matter more than formal authority.
• Why Di believes there is a direct correlation between joy and productivity.
• Why the CDO role was in trouble, and why CEOs wanted proof of its value.
• Why Di wants her team to keep automating its own work and moving into new areas.
• How to avoid being distracted by shiny new tools while still tracking the market.
• What makes a data-and-AI mix of foundations and quick wins work when leaders need to show momentum.
• Why putting AI on bad data or bad processes is a disaster.
• How M&S is using Copilot licences and employee-built agents to lift individual productivity.
• How the Sparks loyalty scheme balances personalisation with trust.
• Why a data culture can’t just mean more reports, and how to motivate action instead.
• What responsible AI looks like in practice: explainability, and humans before, in and after the loop.
• Why asking the same question several ways can reveal whether a model is just trying to please you.
• What a story about an AI deleting people from a gym queue shows about unintended consequences.
• Why measuring impact is the career advice Di would give her younger self.

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