Companies are equipping employees with artificial intelligence (AI) tools to boost productivity, but those same tools are creating new management challenges. A Nielsen survey of 1,700 directors and senior managers across seven countries, commissioned by Monday.com, highlights growing difficulties for leaders as AI reshapes workflows.
Harder to judge employee performance
More than half of senior leaders said it has become harder to evaluate employees’ work since adopting AI. Much of the output reaching managers now appears polished, making it difficult to determine how much of a deliverable came from an employee and how much from AI. Consequently, nearly half of managers report spending more time reviewing and guiding their teams’ outputs than they did previously.
Enthusiasm paired with adaptation gaps
Despite these challenges, managers are broadly enthusiastic about AI: 94% of those surveyed said they are excited to improve work with AI, and many feel confident about where AI can deliver the most value. Michal Miller Levi, head of market research and customer insights at Monday.com, compared the current excitement to the early internet era in the 2000s.
Levi told TechCrunch that the core problem is speed: AI is evolving faster than organizations can update their processes. “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,” she said.
New evaluation criteria
Seventy-eight percent of respondents said they have changed how they evaluate employees since adopting AI. Managers now give greater weight to staff skills such as crafting effective prompts, deciding which tasks to delegate to AI, and experimenting with new ways of working.
That raises fairness questions: how can employees be judged accurately when it’s difficult to separate human contribution from AI output? The survey found that 46% of managers use AI to help inform decisions, and 39% use AI to draft performance reviews.
Risk of bias and accountability concerns
Using AI to assess or assist with performance reviews can introduce additional bias. Large language models (LLMs) are predictive systems that can reproduce patterns present in their training data. Research from the Luxembourg Institute of Socio-Economic Research found that, in the absence of clear objective standards and when raters evaluate independently, LLMs tend to rate performance similarly to humans. A research paper from early 2025 also found that AI models can show biases against Black male candidates and exhibit significant, varying biases across different social groups.
When asked whether AI involvement in performance evaluation could introduce external bias, Levi noted companies have not yet fully figured out how to handle such issues.
Invisible supervisory labor and a growing toolset
AI is changing managers’ daily work: someone must decide which tasks to hand off to AI agents, check outputs, and intervene when things go wrong. The report suggests this supervision may amount to a new layer of “invisible labor.”
The software environment is also becoming more complex. Fifty-seven percent of managers say their organizations now have more tools in circulation than before, and about 51% say their tools have become more interdependent and clustered since adopting AI. Only 29% describe their AI infrastructure as fully optimized and centralized.
Levi said many businesses expected AI would shrink their software stacks; instead, they face more apps, integrations, and shadow AI use as employees adopt unapproved tools. “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,” she said. “More than 50% are saying that they’re using more tools now than they did last year, and it’s not stopping […].”
More AI agents, pressure to retain control
The survey found 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. Thirty-five percent of respondents fear being held accountable for decisions made by AI, since LLMs remain largely unpredictable.
Levi said managers do not want agents to be fully autonomous; they prefer people be involved and consulted by AI on critical tasks and decisions.
Governance as central challenge
Overall, the findings suggest AI adoption is increasingly a governance challenge as much as an opportunity to raise productivity. Without appropriate processes, oversight and infrastructure, managers are scrambling to keep systems and workflows running smoothly.
