Why AI Success Depends on Boring Governance and Data Foundations

Most people in a shiny new AI job want to talk about the shiny new thing. Damian Leach, recently made chief AI and digital officer at the corporate services firm Vistra, did the opposite. Asked how the company is building its big new AI-powered platform for around 10,000 staff across 65 locations, he said it all starts with the boring stuff: designing policies and guardrails, setting standards, doing the architecture, mapping the roadmap, and addressing regulatory, trust and privacy concerns. He called this work of "upmost importance".

That is a refreshing thing for a senior leader to admit in public, and here is why he is right.

When a company decides to "do AI", there is a strong pull towards the visible parts. The chatbot. The demo. The slide that makes the board nod. Vendors are happy to feed that appetite, because a polished tool is easy to sell and easy to buy. What they cannot sell you is the part Leach is describing. Your guardrails have to match your regulator. Your data standards have to fit your actual, messy data. Your roadmap has to survive contact with your real people and how they work. None of that comes in a box.

This is the irony of the whole AI market. The off-the-shelf stuff is, by definition, the same for everyone. The big providers will happily give you the same large language model they give your competitor. The thing that decides whether it works for you or against you is the unglamorous groundwork underneath it. That groundwork is where the value actually lives, precisely because it cannot be standardised and shipped.

Leach is honest about why this affects his customers. He describes data as one of the largest problems they face, with information sitting in silos and multiple sources giving multiple answers to the same question. If you have ever asked two departments for the same number and got two different figures, you already understand the issue. Pointing a clever AI tool at that mess does not clean it up. It just produces fast, confident versions of the confusion. Poor foundations plus powerful tools equals quicker mistakes.

There is a deeper point hiding in here about what AI is actually good for. Leach pushes back on the common assumption that AI is mainly about automating processes. He says the real benefit is improving the client experience, connecting people to their own data so they can ask "what if" questions and get useful answers. That is a meaningful shift in framing. Automation asks "how do we do the same thing with fewer people?" The better question asks "what could people do now that they could not do before?" The first squeezes value out. The second creates it.

Machines machine better than people ever could. They sort, match and process at a scale no human can touch. But deciding which questions are worth asking, which answers can be trusted, and which risks are acceptable in a regulated financial business, that remains human work. The boring stuff is really just the human stuff written down: the agreements about what good looks like, who is accountable when something goes wrong, and where a person has to stay in the loop. Skip it, and you have just hidden the judgement inside a system nobody fully understands.

For leaders watching their own organisations rush at AI, there is a simple test buried in Leach's approach. Before the demo, before the procurement, ask whether anyone has done the unglamorous work. Are the standards set? Is the data trustworthy? Do people know who owns the decision when the model is wrong? If those answers are vague, start with the foundations before the tool.

The temptation will always be to treat governance, data quality and clear accountability as the slow tax you pay before the fun part. Leach has it the right way round. The slow part is the strategy. The fun part is what you get to build once the slow part holds. A new Chief AI Officer just said so on the record, and most organisations would do well to listen.