AI promises productivity, but for managers, it’s mostly a headache for now
More than 50% of managers say they're finding it harder to evaluate employees' work after adopting AI, according to a new survey.
Companies are equipping their employees with AI tools to boost productivity, yet these tools are causing additional challenges for managers. A Nielsen survey of 1,700 directors and senior managers from seven countries, commissioned by work-management firm Monday.com, reveals that over half of the senior leaders find it more difficult to assess employee work after integrating AI.
This is because much of the output from employees is now polished and delivered directly to managers' inboxes, making it hard to determine how much of the work is generated by the employee versus AI. Consequently, nearly half of the managers surveyed spend more time reviewing and guiding their team's output compared to before. Despite this frustration, 94% of the surveyed managers express excitement about leveraging AI to enhance work, noting its potential for optimization.
However, Monday.com's head of market research and customer insights, Michal Miller Levi, draws a parallel to the early days of the internet in the 2000s, describing AI as a similarly "magical" technology. Nevertheless, she emphasizes that AI is evolving faster than companies can adapt, leading to difficulties in managing AI's rapid advancement.
Managers are also rethinking their approach to evaluating team members, with 78% of respondents adjusting their evaluation methods to place more emphasis on staff's ability to create prompts, delegate tasks to AI, and experiment with innovative working methods. However, the challenge arises when managers struggle to ascertain the extent to which AI contributed to the work, given the difficulty in determining the proportion of AI-generated content in the output.
Moreover, with 46% of managers utilizing AI to aid their decision-making and 39% employing AI to draft performance reviews, there is a risk of introducing bias into the evaluation process. Research from the Luxembourg Institute of Socio-Economic Research suggests that when LLMs lack clear objective standards, they tend to rate performance similarly to humans.
Furthermore, a research paper from early 2025 indicates that AI models can harbor biases against certain social groups, such as black male candidates. Levi expresses uncertainty about how companies will address these bias concerns. AI also alters the nature of a manager's daily tasks, requiring decision-making on which tasks to delegate to AI agents, monitoring their output, and intervening when issues arise.
This oversight could represent a new form of "invisible labor." Additionally, the proliferation of AI tools has led to a growing number of software applications in use, with only 29% of managers reporting fully optimized and centralized AI infrastructure. Levi notes that as AI agents become more autonomous, managers are increasingly concerned about losing control over their actions, with 58% expecting to oversee more AI agents than human employees in the coming year.
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