【AI + Efficiency】 Using AI may not necessarily improve efficiency and could become more "brain-burning" — which of the top ten job positions are most prone to "AI brain fog"?

The world has entered the AI era, with industries and job positions utilizing AI to assist work and improve efficiency. However, after widespread AI adoption, some people believe that AI has not simplified work but has instead caused a phenomenon called “brain fry.” According to a study published in Harvard Business Review, this phenomenon is called “AI Brain Fry,” which refers to mental fatigue caused by overusing or managing AI tools beyond one’s cognitive capacity. The study also lists the top ten roles most likely to experience “AI Brain Fry.”

BCG conducted a study on 1,488 full-time employees from large U.S. companies, examining their AI usage patterns, work experience, and cognitive and emotional issues. Respondents spanned multiple industries, roles, and levels, with nearly equal gender representation. About 60% were individual contributors, and 40% were management.

The study found that 14% of respondents who use AI in their work reported experiencing “AI Brain Fry.” When “AI Brain Fry” occurs, individuals report feelings of buzzing or mental confusion, difficulty concentrating, slower decision-making, and headaches.

The study also found that when AI is used to replace routine or repetitive tasks, burnout scores decrease, but mental fatigue scores do not.

How does AI usage trigger “AI Brain Fry”?

1. The most cognitively demanding form of AI operation is supervision. Employees working with highly supervised AI require 14% more mental effort than those with less supervision, experience 12% more psychological fatigue, and feel 19% more information overload.

2. Increased AI tool usage expands workload. Supervising AI and increasing workload broaden employees’ responsibilities, leading to higher cognitive load and subsequent psychological fatigue.

Interestingly, when employees increase from using one AI tool to two, their productivity significantly improves. Using a third tool boosts productivity again but at a slower rate, and using four or more tools actually decreases productivity.

The study indicates that information overload and task switching are key factors leading to “AI Brain Fry.” There is a strong correlation between “AI Brain Fry” and information overload, but the relationship with task switching is less direct.

What are the negative impacts of “AI Brain Fry”?

1. Decision fatigue. When employees are overwhelmed by high cognitive loads from intensive AI work, their mental resources for making high-quality decisions diminish. Employees experiencing “AI Brain Fry” report decision fatigue 33% higher than those who do not.

2. Error frequency. Employees with “AI Brain Fry” make significantly more mistakes, with minor errors 11% higher and major errors 39% higher than those without.

3. Talent attrition. Among employees who have not experienced “AI Brain Fry,” 25% are actively considering leaving, compared to 34% of those who have. Notably, high-performing employees who use AI intensively are often the ones companies want to retain.

The research suggests that while AI can help improve work efficiency, expand thinking, and boost innovation, it can also lead to cognitive overload and a series of personal and organizational impacts. The key is not just how much individuals use AI, but how employees, teams, leaders, and organizations shape AI usage. Therefore, it is recommended to: 1) redesign work, tasks, and tools to foster human-AI collaboration; 2) set clear expectations for AI and workload; 3) shift measurement metrics from activity volume and intensity to impact; 4) develop employees’ skills in managing AI workload; 5) treat human attention as a limited resource and deploy it strategically.