The UK Government’s new roadmap for AI assurance is a welcome development. It’s encouraging to see a clear focus on standards, skills, and independent scrutiny – especially as AI systems become more embedded in critical services and decision-making.
But for those of us working in delivery, assurance isn’t just a policy conversation. It’s something we need to think about from the moment a project begins. Because if you don’t build responsibly from the start, it’s very hard to retrofit trust later.
Where things go wrong
In practice, the biggest risks tend to come from three places:
- Starting with the tech, not the problem – If you’re chasing innovation for its own sake, it’s easy to lose sight of the outcome. That’s when AI becomes a solution looking for a problem.
- Treating assurance as a tick-box – It’s tempting to think you can add governance later. But by then, you’ve already made decisions that are hard to unpick.
- Assuming trust will follow – It won’t. Trust has to be earned – through transparency, explainability, and a clear sense of purpose.
These aren’t abstract concerns. They’re things we see regularly in real-world delivery. And they’re why assurance needs to be part of the build – not just the audit.
What a responsible approach looks like
Responsible AI isn’t about slowing things down. It’s about making sure what you build is fit for purpose – and fit for people. That means:
Designing with governance in mind from day one
Building in explainability, not just accuracy
Thinking about impact – not just performance
It also means being honest about limitations. Not every model needs to be complex. Not every use case needs AI. Sometimes, the responsible thing is to say no.
Where assurance adds value
Third-party assurance has a role to play – especially in regulated sectors or high-stakes environments. But it only works if it’s grounded in the realities of delivery. That means assessors who understand the tech, the context, and the constraints. It means standards that reflect how AI is actually built and used. And it means a shared commitment to doing things properly – not just quickly.
Final thought
AI assurance isn’t just about compliance. It’s about confidence. If we want AI to be trusted, it has to be trustworthy. And that starts with how we build it.
But assurance doesn’t stop at delivery. Organisations also need support in validating and governing AI once it’s live – especially in complex or regulated environments. That’s where experience matters.
If you’re looking at how to approach AI assurance in your organisation – whether you’re just starting out or already scaling – get in touch.