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Companies are giving employees AI tools to make them more productive, but the tools appear to be creating new problems for their managers.
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 is that 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, in fact: 94% of the managers polled said they were excited to improve work using AI, and many are confident they know where to use it to get the most 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” to 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.
Managers are also reconsidering how they judge their teams: 78% of respondents said they have changed how they evaluate employees after adopting AI, placing greater weight on how well staff can create 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 if the managers themselves are increasingly relying on AI to make those judgments? The survey found that 46% of managers use AI to help inform their decisions, and 39% of leaders are using AI to draft performance 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, and 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.
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.
Levi said businesses had expected AI to help shrink their software stacks. Instead, they’re dealing with more apps, integrations, and shadow AI usage as employees adopt tools outside their companies’ approved systems.
“Managers completed 2025 with the feeling that they will consolidate tools, actually; that they’ll reduce the number of software. But what happened is that the number of tools that they’re using increased,” Levi said. “More than 50% are saying that they’re using more tools now than they did last year, and it’s not stopping […].”
This need for control seems to be growing urgent as companies deploy AI agents that can act on their own. The survey found that 58% of managers expect to oversee more AI agents than human employees within the next year, and 46% worry about losing control over those agents’ actions.
There’s also quite a bit of fear over being held responsible for decisions made by AI (35% of respondents said so), as LLMs are still largely unpredictable. “They don’t want their agents to be fully autonomous,” Levi said, explaining that managers want people to be involved and consulted by AI when it comes to crucial tasks and decisions.
At heart, the report’s findings show that AI adoption is increasingly becoming as much a governance problem as it is an opportunity to raise productivity. Without proper processes and infrastructure to deal with it, managers seem to be scrambling to keep things chugging.