AI Promises Productivity, But for Managers It's Mostly a Headache—For Now
Companies are handing employees AI tools to boost productivity, but those tools appear to be creating new problems for their managers.
The Evaluation Problem
More than half of senior leaders say they find it harder to evaluate employees' work after adopting AI, according to a Nielsen survey of 1,700 directors and senior managers across seven countries, commissioned by work-management company Monday.com. The reason: much of employees' output now arrives polished in the manager's inbox, making it hard to tell how much came from the employee and how much from AI. As a result, nearly half of managers say they spend more time reviewing and guiding their teams' output than they used to.
That's not to say managers are unhappy about AI. Quite the opposite: 94% of the managers polled said they were excited to improve work using AI, and many are confident they know where to apply it for maximum value. Michal Miller Levi, head of market research and customer insights at Monday.com, compared the enthusiasm to the early days of the internet in the 2000s, saying people see a similar kind of "magic" in AI.
But the problem, she told TechCrunch, is simply that AI is developing faster than companies can adapt. "AI works very well for [managers] individually, but they also have a different role as managers. They need to drive the organization as well […] Their struggle is [due to] the fact that AI is just moving faster than the organizational processes," Levi said.
Rethinking How Teams Are Judged
Managers are also reconsidering how they assess their teams: 78% of respondents said they have changed how they evaluate employees after adopting AI, placing greater weight on how well staff can craft prompts, decide which tasks to delegate to AI, and experiment with new ways of working.
But how fairly can employees be judged when managers can't easily tell how much of their work was produced by AI? And what happens when the managers themselves increasingly rely on AI to make those judgments? The survey found that 46% of managers use AI to help inform their decisions, and 39% of leaders use AI to draft performance reviews.
The Risk of AI-Assisted Reviews
That practice runs the risk of introducing even more bias than usual into the review process, given that LLMs are, at their core, prediction machines that tend to repeat human patterns found in their training data. Research from the Luxembourg Institute of Socio-Economic Research earlier this year found that "in the absence of clear objective standards and when individuals are rated independently," LLMs tend to rate performance similarly to humans. And a research paper in early 2025 found that AI models can be biased against Black male candidates, and "demonstrate significant biases, varying in terms of the directions and magnitudes across different social groups."
When I asked Levi whether using AI to weigh in on people's performance could introduce outside bias into the process, she said companies have yet to work out how to handle such issues.
The New Invisible Labor
AI is also changing what a manager's day looks like. Someone has to decide which tasks to hand over to AI agents, check what they produce, and step in when something goes wrong. The report suggests that this supervision could amount to a new layer of "invisible labor."
The software itself is becoming harder to manage. Fifty-seven percent of managers say their organizations now have more tools in circulation than before, and about 51% said their tools had grown more interdependent and clustered since adopting AI. Only 29% described their AI infrastructure as fully optimized and centralized.
A 2026 Outlook
As AI adoption accelerates into 2026, the gap between individual productivity gains and organizational readiness remains a central challenge. Managers are caught between enthusiasm for AI's potential and the practical burden of overseeing AI-augmented work without clear standards, reliable evaluation methods, or fully integrated infrastructure. Until companies develop frameworks for attributing and assessing AI-assisted output—and for governing AI's role in performance evaluation—the promised productivity dividend may continue to arrive with a management tax attached.
via TechCrunch AI