Tom Spencer is Director of Customer Data Sciences at Aviva, where he leads teams spanning analytics, data science, engineering, and data product to deliver customer-focused insight and AI at scale.
He began his career in retail and FMCG at dunnhumby, working close to the commercial frontline. That experience shaped a lasting belief that data is only valuable when it changes a decision for real customers. Early roles taught him both how easy it is to produce impressive analysis that fails to land, and how powerful data becomes when it earns trust by solving tangible problems.
A key turning point in Tom’s career came with the move from specialist roles into leading multi-disciplinary teams. His focus shifted from individual models to the broader system around them, including product thinking, engineering reliability, governance, and adoption. Through this transition, he developed a pragmatic view that better execution almost always delivers more impact than marginal improvements in algorithms.
At Aviva, working in a highly regulated environment has further shaped his perspective on responsible AI. He sees it not as a compliance exercise, but as a core leadership discipline that must be embedded into how teams operate and make decisions.
Tom’s approach to data leadership is grounded in a simple set of principles: start with outcomes, design systems that scale, and invest in people and partnerships as seriously as technology. He consistently emphasises that technology is rarely the right starting point; impact comes from aligning data capability to real business needs and customer outcomes.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
In my opinion, really key traits for data and AI leadership are creating clarity, and credibility for delivering results.
“We work in a complex, rapidly changing field. It is easy to over-complicate. Those that create clarity amid that have a massive advantage. Simplify the complexity into practical choices: what problem we are solving, how we will measure success, and what we will not do. This protects focus and ensures teams work on what really matters.
“However, I don’t think clarity alone is enough. You also must have built credibility; a track record of delivery and results. Sometimes there still needs to be a leap of faith, but that’s an easier sell if you’ve built up the credit in the bank with your stakeholders. When leaders trust you, they give you more freedom to move quickly.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“I don’t know if it’s non-traditional, but one really useful piece of development advice I still use and share with my teams is ‘think a level up’.
“It’s all too easy to get caught up in your own role, thinking about your own problems, stuck in your own perspective. However, if you can snap out of that and try to think like your manager (or whichever senior stakeholder is relevant in the situation) it can make a real difference to how you think about problems.
“That zoomed-out perspective can really help your development and makes you so much more valuable to your manager and your organisation. In my opinion, it’s universal; it can be useful in your career from entry-level to CEO.”
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