Companies are equipping their employees with AI tools in hopes of boosting productivity, but these same tools are generating fresh challenges for the people managing those employees. According to a Nielsen survey of 1,700 directors and senior managers across seven countries—commissioned by the work-management company Monday.com—more than half of senior leaders report that evaluating employees' work has become more difficult since adopting AI. The core issue: much of what employees produce now shows up in managers' inboxes already polished, obscuring how much of the work originated with the employee versus the AI. Consequently, nearly half of managers say they now spend more time reviewing and guiding their teams' output than they did previously.

This isn't to suggest managers are dissatisfied with AI—far from it. In fact, 94% of the managers surveyed expressed enthusiasm about using AI to improve work, and many feel confident about where to apply it for maximum value. Michal Miller Levi, who leads market research and customer insights at Monday.com, likened this excitement to the early days of the internet in the 2000s, noting that people perceive a similar kind of "magic" in AI. "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 explained.

Managers are also rethinking how they assess their teams: 78% of respondents said they've altered their evaluation methods since adopting AI, placing greater emphasis on how well staff craft prompts, determine which tasks to delegate to AI, and experiment with new working methods. But how fairly can employees be judged when managers can't readily discern how much of their work was AI-generated? And what happens when managers themselves increasingly lean on AI to make those judgments? The survey revealed that 46% of managers use AI to help inform their decisions, while 39% of leaders use AI to draft performance reviews. This practice risks introducing even more bias than usual into the review process, given that LLMs are fundamentally prediction machines that tend to replicate 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. Additionally, 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 acknowledged that companies have yet to work out how to handle such issues. AI is also reshaping what a manager's day looks like. Someone must decide which tasks to hand over to AI agents, verify what they produce, and intervene when something goes wrong. The report suggests 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 noted that 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 capable of acting 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 considerable fear about being held responsible for decisions made by AI (35% of respondents said so), as LLMs remain 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 its core, 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 running smoothly.