Organizations are equipping their staff with AI resources to enhance productivity; however, these resources seem to be generating new challenges for their supervisors.

According to a Nielsen survey of 1,700 directors and senior executives across seven nations, commissioned by work-management firm Monday.com, over half of senior leaders report finding it more challenging to assess employee performance after integrating AI. This difficulty arises because much of what employees submit is now refined before reaching the manager, obscuring the distinction between the employee’s contributions and those of the AI. Consequently, nearly half of managers indicate they devote more time to reviewing and guiding their teams’ work than previously.

This doesn’t imply that managers are dissatisfied with AI. On the contrary, 94% of surveyed managers expressed enthusiasm about enhancing work through AI, and many feel confident in identifying where to leverage it for maximum benefit. Michal Miller Levi, head of market research and customer insights at Monday.com, likened the enthusiasm to the early internet days of the 2000s, noting that individuals perceive a comparable “magic” in AI.

However, the challenge, she told TechCrunch, lies in the rapid pace of AI development compared to corporate adaptability. “AI proves very effective for [managers] individually, but their role as managers is distinct. They are required to steer the organization as well […] Their struggle stems from the fact that AI is advancing more swiftly than organizational frameworks,” Levi remarked.

Managers are also rethinking their evaluation criteria for teams: 78% of respondents reported altering their employee assessment methods after incorporating AI, placing greater emphasis on staff’s ability to formulate prompts, determine tasks suitable for AI delegation, and explore innovative approaches to work.

But how fairly can employees be assessed when it is difficult for managers to discern how much of their output was generated by AI? Moreover, what occurs if the managers themselves are increasingly depending on AI to aid in these evaluations? The survey revealed that 46% of managers utilize AI to inform their decisions, while 39% of leaders employ AI to compose performance assessments.

This practice poses the risk of introducing additional bias into the evaluation procedure, considering that LLMs fundamentally serve as predictive models and typically replicate human patterns observed in their training data. A study from the Luxembourg Institute of Socio-Economic Research earlier this year discovered that “in the absence of clear objective standards and when individuals are rated independently,” LLMs often evaluate performance in a manner similar to human assessors. Furthermore, a research paper from early 2025 revealed that AI models might display biases against black male candidates, showing “significant biases, varying in direction and magnitude across different social groups.”

When I inquired with Levi about the potential for AI to introduce external biases into performance evaluations, she mentioned that organizations are still figuring out how to address such challenges.

AI is also transforming the typical day of a manager. Someone must determine which responsibilities to delegate to AI tools, review their outcomes, and intervene when issues arise. The report indicates that this oversight may represent a new tier of “invisible labor.”

The software itself is becoming more complex to manage. Fifty-seven percent of managers noted that their organizations now have a greater number of tools in circulation than before, and approximately 51% reported that their tools had become more interrelated and clustered since the adoption of AI. Only 29% characterized their AI framework as fully optimized and centralized.

Levi stated that organizations had anticipated AI would assist in reducing their software portfolio. Instead, they are confronted with an increased number of applications, integrations, and unapproved AI usage as staff utilize tools not sanctioned by their companies.

“Managers concluded 2025 believing they would consolidate tools, effectively reducing software use. But what transpired is that the number of tools they utilize has risen,” Levi explained. “Over 50% are reporting that they are employing more tools now than in the previous year, and this trend continues.”

This demand for oversight seems to be intensifying as organizations deploy AI agents capable of operating autonomously. The survey indicated that 58% of managers expect to supervise more AI agents than human employees in the forthcoming year, while 46% expressed concerns about losing control over the actions of those agents.

There is also considerable anxiety regarding accountability for AI-driven decisions (35% of respondents indicated this), given the largely unpredictable nature of LLMs. “They don’t want their agents to be completely autonomous,” Levi explained, noting that managers prefer human involvement and consultation from AI in pivotal tasks and decisions.

Ultimately, the findings of the report illustrate that the integration of AI is progressively becoming as much a governance challenge as it is an opportunity to enhance productivity. Without adequate processes and infrastructure to manage it, managers appear to be struggling to maintain operational continuity.