Speed is only part of the result
A team can produce more and become less able to explain, challenge or repair what it produces. This matters when AI handles familiar analytical tasks or a few experienced people become permanent escalation points.
The useful question is whether people are becoming more capable as the work gets faster. Can they recognise a weak answer, handle an unfamiliar exception and recover when a tool is unavailable?
Look at what people can still do
Output alone misses an important part of performance. Pay attention to whether people can explain their decisions, challenge a result and share what they have learned.
A faster team that depends increasingly on one expert or one tool may be building a new constraint. That dependence deserves attention before an absence or a failure exposes it.
Context changes the interpretation
More questions during a major transition may be healthy. The same pattern in a settled operation may suggest that information or responsibilities no longer fit. Workload, staffing and task complexity matter.
The aim is to understand how the work is changing, not to rank individuals or turn judgement into a simplistic score.
The decision that follows
If output is rising while understanding is falling, make room for people to learn and challenge the work. Sustainable speed depends on people who can still judge when the answer is wrong.