AI Promises Productivity, But Managers Face Evaluation Crisis as Tools Outpace Organizational Processes

AI Adoption Transforms Workplace Dynamics

Companies racing to deploy AI tools are discovering an uncomfortable truth: while artificial intelligence promises productivity gains, it's creating unprecedented challenges for managers trying to evaluate their teams. A Nielsen survey commissioned by Monday.com, covering 1,700 directors and senior managers across seven countries, reveals that more than half of senior leaders find it harder to evaluate employees' work after AI adoption1

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. The core issue centers on polished output arriving in managers' inboxes, making it nearly impossible to distinguish how much came from the employee versus how much was generated by AI2

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Nearly half of managers now spend more time reviewing and guiding their teams' output than before AI implementation, creating what researchers describe as a new layer of invisible labor1

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. This paradox highlights a fundamental tension: AI makes it harder to evaluate teams even as it promises to streamline workflows.

Enthusiasm Meets Organizational Reality

Despite evaluation challenges, enthusiasm for AI remains remarkably high. Ninety-four percent of managers polled expressed excitement about improving work using AI1

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. Michal Miller Levi, head of market research and customer insights at Monday.com, compared this enthusiasm to the early days of the internet in the 2000s, noting people see similar "magic" in AI capabilities1

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The challenge isn't the technology itself but the pace of change. "AI works very well for [managers] individually, but they also have a different role as managers. They need to drive the organization as well," Levi explained1

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. AI is developing faster than companies can adapt their organizational processes, creating a governance problem that extends beyond individual productivity gains.

Performance Evaluation Gets a Complete Overhaul

Seventy-eight percent of respondents reported changing how they evaluate employees after AI adoption1

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. The traditional performance contract of delivering work, hitting deadlines, and following processes is losing meaning now that AI can satisfy most of those conditions2

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Managers now place greater weight on prompt engineering skills, with 46% citing the ability to prompt AI effectively as the single most important rising criterion for team evaluation2

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. Teams are increasingly judged on their ability to decide which tasks to delegate to AI and experiment with new ways of working1

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AI-Assisted Performance Reviews Raise Bias Concerns

Forty-six percent of managers use AI to help inform their decisions, while 39% are using AI to draft performance reviews1

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. This practice introduces significant concerns about bias in AI systems. Since LLMs are prediction machines that repeat human patterns found in training data, they risk amplifying existing biases1

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Research from the Luxembourg Institute of Socio-Economic Research found that LLMs tend to rate performance similarly to humans when objective standards are absent. A 2025 research paper revealed AI models can demonstrate significant biases against black male candidates, varying in direction and magnitude across different social groups1

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. When asked about bias risks, Levi acknowledged companies have yet to work out how to handle such issues1

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Tool Proliferation Contradicts Consolidation Expectations

While 61% of managers said AI was embedded in their workflows, the reality contradicts expectations of streamlined operations. Fifty-seven percent report their organizations now have more tools in circulation than before AI adoption1

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. About 51% said tools had grown more interdependent and clustered, with only 29% describing their AI infrastructure as fully optimized and centralized1

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"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," Levi noted1

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. Organizations are dealing with more apps, integrations, and shadow AI usage as employees adopt tools outside approved systems, creating dependencies on systems nobody deliberately designed2

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Managing AI Agents Becomes the Next Frontier

Fifty-eight percent of managers expect to oversee more AI agents than human employees within the next year1

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. These autonomous AI agents are being deployed primarily for data analysis and reporting (56%), automating repetitive tasks (51%), and research (50%)2

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Marketing and sales teams each deploy agents at about 23%, while HR stands at 30%. Finance proves the exception, with only 15% of its agents dedicated to finance functions, less than the 17% built for project management2

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Forty-six percent of managers worry about losing control over AI agents' actions, while 35% fear being held responsible for decisions made by unpredictable LLMs1

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. "They don't want their agents to be fully autonomous," Levi explained, noting managers want human involvement and consultation for crucial tasks and decisions1

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