Paul Alexander is Group CEO of Beyond: Putting Data to Work, with a career rooted in the belief that insight only matters when it changes behaviour. He began in advertising, where early experience reinforced a simple truth: analysis without action has no value. Senior roles at Leo Burnett, M&C Saatchi, and TBWA provided a rigorous grounding in commercial thinking and the realities of influencing decision-making at scale.
Paul moved into data in earnest at dunnhumby, where he learned how to industrialise insight and embed it into everyday commercial decisions. This period shaped his conviction that data must be commercially accountable and judged by outcomes, not sophistication.
In 2007, he founded Beyond: Putting Data to Work, motivated by frustration at seeing organisations invest heavily in data platforms, dashboards and models without changing what they actually do. Since then, he has worked closely with boards and executive teams across retail, financial services, travel and the public sector. His work has ranged from driving hundreds of millions of pounds in incremental revenue to unlocking three million additional clinical working hours for the NHS in a single month.
These experiences have shaped a clear philosophy: data and AI are leadership disciplines, not technical ones. Paul’s focus has never been on “owning” data, but on making it unavoidable in decision-making by embedding insight where it influences choices, trade-offs and behaviour, even when that creates discomfort.
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?
“The most important trait in data and AI leadership isn’t technical brilliance, it’s courage. Courage to challenge senior leaders when intuition clashes with evidence. Courage to simplify when complexity is fashionable. Courage to stop initiatives that are impressive but pointless.
“Alongside that, commercial empathy is critical. Great data leaders understand how value is really created in their organisation, where money is made, lost or wasted, and they design intelligence around those pressure points. Without that, data teams become very busy and very irrelevant.
“Storytelling also matters more than we like to admit. Not ‘once upon a time’ storytelling, but the ability to connect data to consequences: if we do this, here’s what happens; if we don’t, here’s the cost. That skill has been disproportionately influential in my organisation because it turns insight into momentum.
“Finally, effective leaders build decision systems, not data estates. In Beyond, the most impactful skill has been aligning data, technology, incentives and governance around a small number of high-value decisions. That’s where AI delivers – not in labs, but in the messy reality of how organisations behave.
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?
“Stop trying to be liked. Aspiring data and AI leaders often confuse influence with popularity. They polish, soften and caveat their insights until everyone nods politely, and nothing changes. That’s a fast route to becoming an expensive reporting function.
“My advice is this: be useful before you’re agreeable. Tell leaders what they need to hear, not what fits the meeting narrative. If the data contradicts the HiPPO, say so. If an AI initiative has no route to value, kill it early.
“You won’t win every argument (and you shouldn’t try to) but you’ll build something far more valuable than consensus: trust.
“In the long run, organisations don’t remember the data leaders who made things sound clever. They remember the ones who helped them make better decisions, even when it was uncomfortable. That’s the job.”