More than half of managers say AI made evaluating employees harder, survey finds

A new survey reported by TechCrunch on October 21 finds that more than 50 percent of managers say evaluating employees' work has become harder since their teams adopted AI.

The figure cuts against the dominant enterprise narrative of the past two years. Vendors and consultants keep promising higher output per person, but the survey points at something else: output went up, and the ruler for judging output did not.

Attribution is the first problem. A document, a piece of code or an analysis may be written by a person, generated by a model, or produced through several rounds of human-model collaboration. Managers struggle to trace the final result back to a specific contributor, which makes it hard to say who actually solved the problem.

Traditional proxies for effort break down as well. When a first draft takes minutes instead of hours, delivery speed stops signalling judgement, review skill or business understanding, which are precisely the capabilities that matter more when models do the drafting.

The practical implication is that performance systems may need to shift from how much was produced to what was judged: who defined the problem, who caught the errors in a model's output, who owned the final conclusion. That kind of contribution is harder to quantify but closer to real value.

The survey has limits worth stating. It captures managers' perceptions rather than a direct measurement of productivity, and feeling that evaluation is harder can reflect either more complex work or standards that no longer fit.

What to watch is whether evaluation tools and metrics designed for AI-assisted teams begin to appear, and how companies record model involvement during performance cycles. The answers will shape hiring, promotion and team design over the next year.