Editorial illustration for AI promises productivity, but for managers it’s mostly a headache for now
AI Makes It Harder for Managers to Track Work
AI promises productivity, but for managers it’s mostly a headache for now
More than half of senior leaders say AI has made it harder to tell what their employees actually did. That's the finding from a Nielsen survey of 1,700 directors and senior managers across seven countries, commissioned by work-management company Monday.com. The problem isn't that AI tools are bad at the job. It's that the output they produce lands in a manager's inbox looking finished, which makes it nearly impossible to tell how much of it came from the employee and how much came from a chatbot.
Nearly half of the managers surveyed say they now spend more time reviewing and guiding their teams' work than they did before AI showed up. That's a strange outcome for a technology sold on the promise of saving time, and it points to a gap between what AI does for an individual worker and what it does to the processes a manager is supposed to run. Michal Miller Levi, head of market research and customer insights at Monday.com, has been studying that gap, and her read on it starts with how enthusiastic managers still are about the technology, even as it scrambles their day-to-day job.
More than half of senior leaders say they find it harder to evaluate employees’ work after adopting AI, according to a Nielsen survey of 1,700 directors and senior managers across seven countries, commissioned by work-management company Monday.com. The reason is that much of employees’ output now arrives polished in the manager’s inbox, making it hard to tell how much came from the employee and how much from AI.
Why this matters
For founders building AI tools and researchers studying workplace adoption, the Monday.com data points to a gap that product design hasn't caught up with yet. Tools optimized for individual output, polished drafts, fast turnaround, are colliding with management structures built around visible effort and iterative review. Levi's point about AI outpacing organizational process is the real signal here: it's not a training problem or a tooling problem, it's a sequencing problem.
Companies rolled out AI to individual contributors before rethinking how managers verify, evaluate, or trust that output. If you're building AI products for the enterprise, this is a market opening. There's an unmet need for tools that help managers see process, not just polished results, things like version history, prompt trails, or confidence scoring on AI-assisted work.
Until that layer exists, expect more surveys like this one, with senior leaders reporting productivity gains on paper and evaluation headaches in practice. The 1,700-person sample across seven countries suggests this isn't a regional quirk. It's a structural lag worth watching as more companies push AI deeper into daily workflows.
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Common Questions Answered
Why are managers finding it harder to evaluate employee work after AI adoption?
According to the Nielsen survey, more than half of senior leaders struggle to assess employee performance because AI-generated output arrives polished and finished in their inbox, making it nearly impossible to distinguish how much work the employee actually did versus how much came from AI tools. The problem isn't that AI tools perform poorly, but rather that their finished output obscures the employee's actual contribution and effort.
What did the Monday.com Nielsen survey reveal about senior leaders' experience with AI in the workplace?
The Nielsen survey of 1,700 directors and senior managers across seven countries found that more than half report AI has made it harder to tell what their employees actually accomplished. This challenge stems from the difficulty in evaluating work when polished AI outputs are indistinguishable from employee-generated content in managers' inboxes.
What organizational gap does the Monday.com data identify regarding AI tool design?
The data reveals a fundamental mismatch between AI tools optimized for individual output and fast turnaround versus management structures built around visible effort and iterative review. This is described as a sequencing problem rather than a training or tooling problem, indicating that AI has outpaced the organizational processes needed to effectively manage its use.
How does the polished nature of AI output create challenges for performance management?
When AI tools produce finished, polished work that lands directly in a manager's inbox, it becomes nearly impossible to assess the iterative process and actual effort an employee invested in the task. Managers can no longer use visible work progression and revision cycles as indicators of employee contribution, which were previously key metrics in traditional performance evaluation.
Further Reading
- AI Promised Efficiency, But Companies Got a Management Problem Instead - Reworked
- Using AI can add extra labor and cause 'brain fry,' experts say - CNBC
- 'AI brain fry' affects employees managing too many agents - The Register
- More AI tools, more burnout! New research explains why - Help Net Security
- Using AI to raise productivity without weakening capability and trust - CIPD
