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AI makes assessing work harder, survey of managers finds
A Nielsen survey commissioned by Monday.com describes managers’ difficulties in assessing work produced with AI. Despite strong reported enthusiasm, respondents report more supervision and an accumulation of tools. These findings shed light on their perceptions without establishing a measured assessment of productivity.
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AI is also transforming management work. A Nielsen survey covered by TechCrunch reveals a gap between managers’ enthusiasm and the difficulties they report in assessing contributions, checking deliverables and organizing tools. It invites scrutiny of the supervision workload behind promises of productivity.
AI adoption in the workplace is also changing the tasks of those who manage teams. According to a Nielsen survey commissioned by Monday.com and covered by TechCrunch, more than half of the executives surveyed find it harder to assess employees’ work after adopting these tools. The survey covers 1,700 directors and senior executives in seven countries.
The difficulty described stems in particular from how deliverables are presented. Managers receive already polished work and struggle to distinguish the employee’s contribution from that of AI. Nearly half of managers say they now spend more time reviewing and guiding their teams’ work. This suggests a possible shift in workload toward supervision, without demonstrating that the organization is becoming less efficient overall.
Enthusiasm nevertheless remains high: 94% of respondents say they are enthusiastic about improving work with AI. Meanwhile, 78% say they have changed how they evaluate employees. The ability to formulate instructions, choose tasks to delegate and experiment with new methods is playing a larger role in their criteria. Evaluation therefore appears to need to account for the choices made around the tool, beyond the result delivered.
Managers themselves use these technologies to inform their judgment. According to the survey, 46% rely on AI to inform their decisions, and 39% of executives use it to write performance reviews. These practices make clarifying the criteria particularly useful: AI-assisted work can now be assessed with the help of another form of automated assistance. However, these statements are insufficient to establish the quality or fairness of the resulting evaluations.
The proliferation of software adds another difficulty. TechCrunch reports that managers have noted an increase in the number of tools, while only 29% describe their AI infrastructure as fully optimized and centralized. The issue could therefore be as much about organizing use as about access to models.
These findings remain self-reported responses collected in a survey commissioned by a work management software provider. They are not a direct measure of productivity gains. Their value lies in highlighting an operational question: how should verification, responsibility and evaluation be allocated when AI is involved in everyday production?