Chris Gullick, Chief Data and Artificial Intelligence Officer at Ofgem, has followed a non-linear career path that has strongly shaped his approach to data and AI leadership. He began in technology risk and assurance, an early grounding that instilled a lasting scepticism of large-scale systems and a clear-eyed view of how good intentions can translate into operational complexity. That perspective has remained central as he moved into senior data roles across professional services, technology organisations and, more recently, public service.
Two specific experiences have defined his outlook. The first was operating in global, highly regulated environments where data quality and governance carried tangible consequences. At International SOS, inconsistent data was a reporting inconvenience that created real operational risk. That experience sharpened his focus on trust, accountability and decision-making, rather than pursuing technically elegant but fragile solutions.
The second has been leading data and AI at Ofgem during a period when enthusiasm for AI accelerated faster than organisational readiness. Faced with pressure to move quickly alongside limited confidence in data foundations and risk management, Gullick has spent much of his time navigating that tension. Enabling progress while strengthening the underlying basics has reinforced his belief that effective data leadership relies less on technical novelty and more on judgement, credibility and timing.
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 traits that matter most for data and AI leadership are judgement, credibility, and pragmatism.
“Judgement matters because most decisions in this space are made with incomplete information and competing pressures. Knowing when to move, when to pause and when to quietly change the order of work is more important than having the perfect answer.
“Credibility matters because influence isn’t automatic. In my experience, it’s earned by being clear about limitations, being honest about risk and delivering reliably on what you say you’ll do. Over time, that creates space to have more difficult conversations when it really matters.
“Pragmatism is what turns intent into impact. In my organisation, progress has come less from big architectural moves and more from removing friction, clarifying ownership and making it easier for people to do the right thing with data and AI. Together, these traits have helped position data and AI as part of how the organisation operates, rather than something experimental or separate.”
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?
“The most useful non-traditional advice I’d give is to learn how to say ‘yes’ without meaning ‘now, as asked’.”
“Senior leaders rarely respond well to flat refusals, especially when there’s pressure to move quickly. What has worked better for me is agreeing with the intent (speed, innovation, visible progress) while quietly changing the order of work underneath. You let the organisation move, but you make sure foundations, governance and ownership are being put in place at the same time.
“Earlier in my career, I thought influence came from arguing the case. I’ve since learned it often comes from absorbing pressure and redirecting it. If you can enable progress while protecting trust and avoiding future mess, you become the person leaders rely on when things are uncertain.
“That skill is rarely written down, but it’s often what separates data leaders who are invited into the C-suite from those who remain on the sidelines.”