AI productivity promises faster work, smarter decisions and less time spent on repetitive tasks. But for many managers, the reality is turning out to be more complicated. A new workplace survey suggests that while employees are using AI to produce more work, managers are struggling to review that output, evaluate employee contributions and keep workflows under control.

A Nielsen survey of 1,701 directors and senior managers across seven countries, commissioned by monday.com, found that 53% of leaders say AI has made it harder to evaluate employees’ original contributions. Nearly half, 47%, say they now spend more time reviewing and guiding their teams’ work.

AI Is Making Work Look Better, but Is It Making Work Easier?

One of the biggest challenges is that AI-generated work can look polished even when it needs careful checking. A report, presentation or analysis may appear ready to share, but managers can struggle to tell how much came from an employee’s own expertise and how much came from AI.

This creates a new problem for performance reviews. If two employees submit equally polished work, managers may not know who demonstrated stronger judgment, solved the harder problem or relied more heavily on AI. The finished output alone may no longer tell the whole story.

The shift is already changing workplace expectations. According to the survey, 78% of leaders have changed how they evaluate employees since adopting AI, placing greater importance on how people use AI, delegate tasks and apply their own judgment.

Managers Are Excited About AI, but Adoption Is Messy

The findings do not suggest that managers want to abandon AI. In fact, 94% of surveyed leaders said they were excited about using AI to improve work.

The difficulty is that adopting AI is not as simple as giving every employee access to a chat bot. Teams need to decide which tasks AI should handle, how people should check its work and who takes responsibility when something goes wrong.

Companies are also dealing with a growing number of software tools. The survey found that 57% of leaders reported using more tools after adopting AI, while only 29% said AI was fully integrated into a central operating system. Instead of simplifying every workflow, AI can add new apps, integration and coordination work.

The Next Challenge: Managing AI Agents

The problem could become even more complicated as businesses adopt AI agents that can perform multi-step tasks with less direct supervision. The survey found that 58% of leaders expect to manage more AI agents than human employees within the next 12 months. At the same time, 46% worry about losing control over agents’ actions.

AI agents could help with research, reporting and repetitive administrative tasks. But managers still need to set boundaries, review important outputs and make sure agents have access only to appropriate information. That creates a new kind of management work: supervising not just employees using AI, but AI systems doing work themselves.

What Companies Need to Get Right

The lesson is not that AI cannot improve productivity. It is that faster output does not automatically translate into better business results. Companies need clear rules for AI use, realistic performance measures and training that helps employees understand when to trust AI and when to challenge it. Managers should assess the quality of decisions, the accuracy of results and the value employees contribute, rather than judging work only by how quickly it is completed.

They also need clear accountability. AI can assist with tasks and decisions, but people must know who checks the results and who is responsible for the final outcome.

AI’s Productivity Promise Needs Better Management

AI is changing how work gets done, but workplace systems are still catching up. Employees may be producing work faster, yet managers are taking on new responsibilities around review, evaluation, tools and oversight.

The companies that benefit most from AI will not necessarily be the ones using the most tools. They will be the ones that build practical workflows, train their teams and make human accountability part of AI adoption from the start.

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-Written by Shubham Singla