A new study of nearly 1500 full-time U.S workers by the Boston Consulting Group published in the Harvard Business Review found that certain AI use cases can create mental stress, while others significantly reduce it, highlighting the importance of leveraging AI solutions in the right contexts.
What is “AI brain fry?” The term, coined by the study, refers to mental “fog,” “buzzing,” and an “inability to think clearly, like a mental hangover” that is associated with intensive back-and-forth with AI tools.
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While AI is meant to create efficiencies and speed up productivity, certain uses (and overuses) seem have the opposite effect: “instead of moving faster, my brain just started to feel cluttered…[and I realized] I was working harder to manage the tools than to actually solve the problem,” reported one user.
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The study argues that these instances can create significant business costs, as brain fry can lead to decision fatigue, major and minor errors, and a greater desire to quit—all of which impact the bottom line.
In what use cases is brain fry most common? According to the study, the most mentally taxing form of AI engagement is oversight, or the extent to which AI tools require workers’ direct monitoring (e.g., of multiple AI agents). High oversight use cases required up to 14% more mental effort, and predicted 12% more mental fatigue and 19% greater information overload.
Mental fatigue varies significantly by function, with HR near the top. Marketing roles reported significantly higher rates of “AI brain fry” than any other function, with HR second. Legal, product management, and management/leadership all reported comparably very low levels of mental fatigue. Interestingly, engineering/software development was fourth.
Where can AI alleviate stress and burnout? Unsurprisingly, using AI for routine, repetitive, and mundane tasks is associated with significantly lower levels of burnout (but not mental fatigue). Employing AI for such tasks allows for a “higher degree of social connection with peers as well.”
Lessons for HR leaders:
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Invest in AI training and keep the conversation going: It is not only essential to make sure your employees understand where AI can be helpful and where it cannot, but also for managers to have ongoing dialogues with employees about the same.
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Be clear about your AI strategy and expectations: The study shows correlation between uncommunicated pressure to use AI and mental fatigue, while clearly-communicated AI strategies and expectations lead to less burnout.
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Think about job design and AI use cases holistically: Avoid simply layering AI oversight on top of human oversight, or stacking AI tools endlessly for each employee. Instead, embed AI deeply in overall workflows and treat it as a collective capability rather than an individual differentiator.
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Measure AI by impact, not activity or intensity: Incentivizing mere quantity of use will lead to waste, low quality work, and burnout. Instead, focus on the actual impact, and begin with a clear strategic north star tied to business objectives.
