AI readiness for UK teams.
A practical briefing on the foundations teams should check before adding AI to regular work.
30 May 2026 · 7 min read
Start with workflow reality
AI readiness begins with an honest view of how work currently happens. Teams need to know where information comes from, where delays appear, who checks quality, and what decisions depend on context that is not written down.
A workflow map does not need to be complicated. It should show the task, the user, the inputs, the output, the quality standard, and the point where review is needed.
Check the foundations
Useful foundations include clean documents, agreed naming, clear permissions, training time, staff confidence, and a route for reporting problems. Without those basics, even a simple use case can become difficult to manage.
Teams should also know what information cannot be entered into AI-assisted processes, and who can approve exceptions.
Pilot with evidence
A readiness pilot should measure more than speed. It should check accuracy, consistency, ease of review, staff confidence, and whether the assisted process is genuinely better than the previous one.
If a pilot produces mixed results, that is still useful. It may show that the task needs better instructions, better data, a narrower scope, or no AI assistance at all.
Keep decisions visible
The final readiness question is accountability. If a team cannot explain who owns the process, who checks outputs, and how mistakes are corrected, the project is not ready for broad use.
Good readiness work makes AI less mysterious. It turns adoption into a sequence of understandable decisions.