Managers say AI tools add oversight burden and make employee evaluation harder
A Nielsen survey of 1,700 directors and senior managers in seven countries, commissioned by Monday.com, shows mixed feelings about AI at work. While 94% of the managers polled are excited to improve work using AI, more than half say it makes it harder to evaluate employee output because polished results arrive in their inboxes, blurring the line between human and machine work. Nearly half report…
Key points
- 94% of managers are excited about AI, but more than half find employee evaluation harder.
- 78% say they now weigh prompt‑creation and AI delegation when assessing staff performance.
- Fifty‑seven percent report more AI tools in use, while only 29% describe their AI infrastructure as fully optimized.
The survey also reveals shifts in performance assessment: 78% of respondents have changed how they evaluate staff, putting greater weight on prompt‑creation, task delegation to AI, and experimentation with new workflows. At the same time, 46% of managers use AI to inform decisions and 39% use it to draft performance reviews, raising concerns about bias. Research cited from the Luxembourg Institute of Socio‑Economic Research and a 2025 paper notes that LLMs can inherit human biases, including against black male candidates.
Tool proliferation adds another layer of complexity. Fifty‑seven percent of managers say their organizations now have more AI tools in circulation, about 51% say those tools have become more interdependent, and only 29% describe their AI infrastructure as fully optimized and centralized. More than 50% report using more tools than the previous year, and 58% expect to oversee more AI agents than human employees within the next year, while 46% worry about losing control over those agents and 35% fear being held responsible for AI‑driven decisions.
AI promises productivity, but for managers, it’s mostly a headache for now
TechCrunch AI · 21 October 2026
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This text was published by TechCrunch AI and written by Ram Iyer. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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