Right now, almost every public sector leader I talk to is being told they need an AI strategy. There’s a lot of buzz among ministers and the top of the shop in Whitehall on the potential for AI to drastically change how the civil service operates. But enthusiasm isn’t a strategy. And in my recent work looking at how organisations across government are adopting AI, the space between the two is where most of the value quietly slips away.
The pattern repeats itself almost everywhere. Leaders see the promise, move quickly to put something in place — usually a copilot licence for everyone — and wait for the productivity gains to arrive. A year on, the tools are installed, a handful of people use them well, and the organisation can do no more than it could before. The technology turned up. The value didn’t.
Here’s what I keep coming back to: rolling out generative AI is the easy part. The organisations getting real, lasting value from it aren’t the ones that deployed fastest. They’re the ones that started with the problem they were trying to solve, and then invested in everything around the AI.
It’s the same lesson I’ve written about with data strategy. A generic “let’s adopt AI” fails for the same reason a generic “let’s treat data as an asset” does: it isn’t tied to anything anyone actually cares about. The organisations that get it right don’t buy a strategy off the shelf. They make a few clear choices about where AI will earn its keep, and then they resource the things that make those choices real.
So what does a good AI strategy actually put its money and attention into? In my experience, four things. Only one of them is the technology.
Clarity of intent
Start with the problem, not the product. Search for the most value-adding automations you can go for. Make clear choices about what you’ll build and what you’ll buy — being clear eyed about the true cost of each, because those are genuinely different decisions with different risks. Off-the-shelf tools are quick and comparatively cheap to begin; they speed up what you already do, but costs can come later if inefficient token use racks up sky high. Only AI built on your own data gives you a capability you didn’t have before. Both, done the right way, can be the right call. What doesn’t work is reaching for the quick win while expecting the transformational one — and I see that constantly: leaders overestimating what a fast rollout delivers, and underestimating the structural work sitting underneath it.
Data that’s AI-ready
This is the one that decides everything else. Government usually holds the data it needs; it rarely holds it in a state AI can read or learn from. It sits in ageing systems, unclassified, scattered across teams, and not machine-readable. The unglamorous work — cleaning it, linking it, labelling it — is what turns raw data into something AI can actually use, and it has to come first. I’ve lost count of the organisations buying clever tools while the data underneath them isn’t ready to feed them. There’s no shortcut here, and no amount of budget on the tool makes up for skipping it. This is where an AI ambition and a data strategy become the same conversation.
The right specialists
Getting the best out of AI is a bit like competing in Formula 1. The driver, the aerodynamicist and the race engineer each master a completely different craft. None can do another’s job but the team needs to come together to win. Building AI, integrating it into how you work, and buying it well call for different, scarce specialists too, and they’re rarely already sitting in the building. The organisations that pull ahead treat this as capability-building, not a one-off software rollout. They grow and hold on to the people who can do the work, rather than assuming a licence and a lunchtime webinar will get them there. In government these specialists are especially scarce, with the public sector competing for them against employers who can pay a good deal more. That makes it all the more important to spend the ones you have wisely: pointed at the problems only they can solve, and kept off the work an off-the-shelf tool would have handled just as well.
Guardrails, not hurdles
Good governance isn’t red tape; done well, it’s the thing that lets you move quickly and safely. But a lot of organisations are running AI through security and impact assessments built for a pre-AI world: slow, exhaustive, and designed to say no. Add a cautious culture and low AI literacy on top, and you get governance far more restrictive than the risk warrants, quietly smothering experiments before they can prove themselves. The answer isn’t less oversight. It’s proportionate guardrails, clear ownership, and a real mechanism for backing the use cases worth pursuing. Too little governance invites risk; too much invites paralysis. The organisations that move fastest are usually the ones that gave their people a safe space to experiment and a clear route from “this works” to “let’s scale it”.
Buying Gen AI is the easy part
Look again at those four, and notice what’s missing: the AI itself.
That’s the whole point. None of the things that make AI work are really about the technology. They’re about intent, data, people, and the permission to try — the same quiet foundations that decide whether any ambitious change succeeds in the public sector. The departments and bodies moving ahead aren’t the ones with the shiniest tools. They did the plumbing first, and then gave their people room to experiment once it was in.
None of this is an argument for going slow. AI’s potential in government is real, and so is the cost of standing still while others move. But there’s a difference between moving fast and simply moving first. The organisations that will get the most out of AI are the ones treating it as a capability to build, not a product to buy.
So before the next licence is signed, the more useful question isn’t “which AI tool should we adopt?” It’s “are the foundations underneath it ready, and if not, what’s our plan to get them there?” That’s a harder conversation. It’s also the one that separates the organisations quietly building an advantage from the ones left holding a very expensive chatbot.
This is one part of the wider data and AI strategy work I do with public sector organisations. If it’s set off a thought, I’d be glad to talk it through — find me on LinkedIn or at contact@kompassconsulting.co.uk.
For more detailed analysis on AI adoption in Government read Kompass Consulting’s report on Building AI in Government.