How do we keep human expertise when AI does the work?
By consciously deciding what people continue to do, check and understand. An expert who no longer practises ends up unable to check. AI’s level of autonomy is also set to preserve that know-how.
Updated on 24 October 2026
What is going on.
When AI takes over a task, those who used to do it move on to something else. For a while, they keep a sharp eye and can spot an error. Then the most experienced people leave, newcomers have never done the task by hand, and nobody can really judge what the tool produces any more.
This risk does not show straight away. It appears the day the tool gets something wrong, changes its behaviour or breaks down, and the company discovers it no longer knows how to do without it. Keeping expertise is a decision: which tasks teams continue to practise, what they need to understand about how the tool works, and at what level of autonomy it is set to make that possible.
What we observe.
- You can only check well what you know how to do.Checking a result requires knowing what a good result looks like.
- The loss of know-how is slow and silent.It shows up in no indicator until the day it becomes expensive.
- Newcomers are the most exposed.They learn the job through the tool, without knowing its foundations.
What you can do right now.
- For each use, identify the know-how you do not want to lose.
- Make sure teams regularly handle part of the cases without the tool.
- Include in newcomers’ training the job as it is done without AI.