2 minute read

Building a Business Case for AI

Ofgem’s Chris Gullick challenges the fantasy of ready-made AI value, arguing that the business case is more of a political, cultural, and organisational project than a document to pitch.

At the DataIQ World Congress, Chris Gullick, Chief Data and Artificial Intelligence Officer, Ofgem, cut through the optimism that often surrounds AI adoption. His starting point was simple: everyone wants AI, but far fewer can explain what problem it should solve and almost no one wants to take responsibility for the benefits. As the person tasked with steering AI in a regulator facing public scrutiny and operational pressure, he offered a clear-sighted view of what it really means to build an AI business case that leadership can absorb and support.

“We can’t do everything for the business. These are your use cases, your benefits. We’re here to help, but the responsibility is shared.” – Chris Gullick, Chief Data and Artificial Intelligence Officer, Ofgem

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The reality behind the rhetoric

Chris has walked into meetings with AI use cases worth £8–14 million in savings, only to be told they fall short of the £50 million vision leaders have in mind. The enthusiasm is real; the grounding is not. For Chris, the issue is not ambition but the absence of focus. His approach is to anchor expectations with concrete examples: where Ofgem sits relative to peers, what comparable organisations have achieved, and where impact is genuinely possible.

“The answer is AI, what was the question?”

Like many data leaders, Chris faces a constant stream of AI-first solutions: ChatGPT for spreadsheet errors, generic consulting slides, and sweeping claims about AI’s maturity.

His countermeasure is discipline. In cross-division workshops, discussing solutions is banned and leaders must talk only about problems, outcomes, and pain points. The result was sixty-five use cases and, more importantly, the first real debate among executives about what matters most.

Outcomes beat technology

Many leaders expect generative AI to dominate the opportunity landscape, but Chris expects the opposite and that most benefits will come from automation, not AI. The Ofgem price-control process illustrates his point. Thousands of pages of regulatory submissions are now being analysed using an AI-supported process, cutting decision-making by months. The significance is not the model, but the industry-wide impact of faster regulatory clarity.

“These aren’t my use cases, they’re yours.”

Reinforcing business ownership has been one of Chris’s longest battles. Executives happily agree that benefits sit with them, then immediately ask how much he will save. Persistence has been key: repeating the message at board meetings, in one-to-ones, and in every forum available. Over time, non-executive directors have begun to echo it back to the organisation.

His response is consistent and deliberately uncomfortable: “I’m not going to save any money. You are. I’ll spend money helping you save it.”

Splitting the work

Chris recognised that leaders want two things: fast progress and big, strategic change. Treating them as one request leads to organisational whiplash, so his answer is a three-stream model: