While corporations continue to pour billions of dollars into artificial intelligence initiatives under the banner of workplace productivity, a mounting body of evidence suggests that these powerful new tools are quietly upending the daily realities of frontline managers. Far from streamlining operations and reducing friction, the rapid influx of generative AI into offices worldwide is generating a complex web of oversight challenges, evaluation dilemmas, and technological clutter that organizations are struggling to govern.
According to a comprehensive new survey commissioned by work-management platform Monday.com and conducted across 1,700 directors and senior managers in seven countries, more than half of all senior leaders now find it significantly harder to evaluate the day-to-day work of their employees following the adoption of AI tools. The root of the problem lies in the polished, seamless nature of modern artificial intelligence output. Because employees can now feed raw ideas, rough drafts, and preliminary data points into large language models and instantly receive professional-grade prose, polished presentations, and fully realized reports, managers are often left staring at an inbox where it is virtually impossible to decipher the boundary between human effort and machine generation.
This opacity has forced a profound shift in managerial workflows. Nearly half of the managers surveyed reported that they now spend more time carefully reviewing, fact-checking, and guiding their teams’ AI-assisted output than they did prior to widespread adoption.
Despite these emerging friction points, however, the overarching sentiment among leadership is far from pessimistic. In fact, enthusiasm for the technology remains remarkably high. A staggering 94% of the managers polled expressed genuine excitement about the potential to improve workplace efficiency using AI, with many exhibiting high confidence in their ability to pinpoint where these tools can deliver the maximum value.
Michal Miller Levi, head of market research and customer insights at Monday.com, draws a direct parallel between the current corporate gold rush toward artificial intelligence and the dawn of the internet era in the early 2000s. She notes that workers and leaders alike continue to perceive a distinct, transformative "magic" in AI capabilities.
The core challenge, Levi explains, is not a lack of enthusiasm or utility, but rather a profound temporal mismatch. Artificial intelligence is developing at a breakneck speed that far outstrips the ability of traditional corporate structures and internal processes to adapt. While AI functions brilliantly for individual employees looking to speed up specific tasks, those same individuals occupy a managerial role that requires steering the entire organization. The resulting friction stems directly from the fact that technological capabilities are accelerating faster than organizational governance can mature.
This dynamic is fundamentally reshaping how leadership evaluates talent. Fully 78% of survey respondents noted that they have overhauled their performance evaluation criteria since adopting AI. Instead of judging employees solely on the final product—which may have been heavily assisted or entirely generated by a machine—managers are placing significantly greater weight on meta-skills: how effectively staff members craft prompts, how wisely they decide which tasks to delegate to algorithms, and how creatively they experiment with novel ways of working.
Yet, this shift creates a circular dilemma. If managers cannot easily determine how much of an employee’s work was genuinely produced by human ingenuity, how fairly can that employee be judged? Compounding the issue is the reality that managers themselves are increasingly turning to AI to solve their administrative burdens. The survey revealed that 46% of managers now use artificial intelligence to inform their managerial decisions, while 39% of leaders rely on AI to draft employee performance reviews.
Delegating performance evaluations to large language models introduces significant risks regarding bias. Because LLMs are fundamentally prediction machines designed to mirror the patterns present in their training data, they inherently carry the risk of replicating historical human biases. Research published earlier this year by the Luxembourg Institute of Socio-Economic Research highlighted that in the absence of clear objective standards, and when individuals are rated independently, LLMs tend to replicate human evaluation biases quite closely. Furthermore, a research paper published in early 2025 demonstrated that various AI models can exhibit significant biases against specific demographic groups, such as Black male candidates, with biases varying in direction and magnitude across different social segments.
When questioned about whether integrating AI into performance evaluations risks importing external bias into corporate review cycles, Levi acknowledged that companies have yet to figure out comprehensive frameworks to handle such complex ethical and procedural hurdles.
Beyond evaluation and oversight, artificial intelligence is radically transforming the daily responsibilities of a manager. Someone must actively decide which workflows to hand over to automated AI agents, continuously audit what those agents produce, and intervene immediately when errors or hallucinations occur. The Monday.com report suggests that this ongoing supervision is effectively creating a new, unrecognized layer of "invisible labor" within the modern enterprise.
Managing the software itself has also grown remarkably complex, confounding early corporate expectations. While businesses initially hoped that adopting AI tools would help consolidate their software stacks and reduce operational bloat, the reality has proven to be the exact opposite. Fifty-seven percent of managers reported that their organizations now have more software tools in circulation than they did previously, and roughly 51% noted that their toolsets have grown increasingly interdependent and clustered since the arrival of AI. Only 29% of respondents described their current AI infrastructure as fully optimized and centralized.
Instead of a streamlined digital ecosystem, companies are grappling with an expansion of applications, complex third-party integrations, and widespread "shadow AI"—a phenomenon where employees independently adopt unauthorized external tools outside of approved corporate IT systems.
Levi notes that as companies closed out 2025, managers went in with the firm expectation that they would be consolidating their toolsets and trimming software expenditures. Instead, the exact reverse occurred. More than half of all managers report utilizing more tools now than they did the previous year, and that upward trajectory shows no signs of slowing down.
This urgent need for governance and control is intensifying as businesses begin deploying autonomous AI agents capable of executing complex workflows independently. According to the survey, 58% of managers expect to oversee more AI agents than human employees within the next year alone. At the same time, 46% admitted to worrying about losing control over the actions of those agents.
Fear of accountability is also a prominent concern, with 35% of respondents expressing anxiety over being held personally responsible for decisions executed by autonomous systems, given the inherent unpredictability of large language models. Managers are understandably hesitant to grant systems full autonomy, preferring instead to keep humans closely involved and consulted for critical tasks and consequential business decisions.
Ultimately, the findings of the Monday.com report underscore that the widespread adoption of artificial intelligence has firmly transitioned from a straightforward productivity opportunity into a complex governance challenge. Without the establishment of robust internal processes, clear standards, and optimized technical infrastructure, managers across industries find themselves scrambling to maintain operational stability in an era of unprecedented technological change.
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