Written by: Akshita Pandey, Moitrayee Das
In today’s fast-changing workplaces, artificial intelligence (AI) is no longer science fiction; it’s already here, integrated into everyday work, performance reviews, and decision-making. While companies highlight AI’s advantages: greater efficiency, fewer errors, and data-driven precision, there is a vital and usually overlooked expense: the effect of AI implementation on workers’ mental well-being.
An increasingly large body of scholarship is starting to uncover the layers of life and work under algorithmic rule. Among the strongest additions to this discussion is a 2024 paper by Byung-Jik Kim and Julak Lee, in Humanities and Social Sciences Communications. Based on a survey of 418 South Korean professionals, one of the country’s most technologically advanced populations, the research examines the impact of AI adoption in the workplace on worker stress, burnout, and psychological well-being. The findings are at once urgent and disconcerting (Kim & Lee, 2024).
The Invisible Weight of Algorithms
Kim and Lee’s study reveals a psychological domino effect: AI adoption does not necessarily lead to burnout, but it drastically increases job stress, which becomes a significant predictor of burnout. It’s a subtle move with far-reaching implications. Workers are not necessarily angry about the existence of AI itself. Instead, it’s the pressure AI imposes upon them—the rate of change, the ongoing requirement to learn, the threat of obsolescence—that stokes this growing worry (Kim & Lee, 2024).
AI in the workplace fosters an environment of techno-stress—a catch-all term for several stressors that include:
- Techno-overload: being overwhelmed by the compulsion to deal with new digital tasks and devices
- Techno-insecurity: the fear that automation will take one’s job
- Techno-complexity: having difficulty learning and adapting to AI systems promptly
- Techno-uncertainty: uncertain expectations regarding performance under AI systems.
This pressure comes at a significant psychological cost. As workplaces become increasingly mechanized and systems of digital monitoring continue to expand, many workers experience a growing sense of detachment and loss of control. Tasks that once relied on human judgment and creativity are now often shaped by algorithms, leaving employees feeling less autonomous and more like extensions of the systems they work within. This shift has contributed to rising feelings of emotional exhaustion, disengagement, and helplessness, a pattern reflected across industries ranging from finance and education to healthcare (Kim & Lee, 2024).
Psychological Theories Behind the Impact
Kim and Lee base their conclusions on three dominant psychological paradigms:
- The Job Demands-Resources (JD-R) Model: The model proposes that burnout occurs when job demands (such as AI upskilling) exceed available resources (e.g., training, assistance, or affective resilience).
- The Conservation of Resources (COR) Theory: In accordance with the theory, people attempt to preserve their physical, emotional, and psychological resources. As AI compromises these resources, e.g., by making employees feel irrelevant or incompetent, people suffer from stress and burnout.
- The Transactional Model of Stress and Coping (TMSC): According to this model, stress arises from the way people evaluate and react to problems. If AI is seen as a threat instead of an opportunity, and if coping skills are poor or lack support, then the outcome is psychological strain.
Simply put, it isn’t AI alone that hurts employees; it’s how AI is deployed, governed, and understood as part of an organizational environment (Kim & Lee, 2024).
Digital Self-Efficacy: Psychological Armor of the Future
Another of the study’s most influential findings is AI learning self-efficacy, or more simply stated, an individual’s confidence that they can master and utilize AI.
Employees with the perception that they can successfully operate AI systems feel much less stressed and more resilient when encountering technological change. Low self-efficacy results in helplessness, anxiety, and burnout. As the researchers point out, self-efficacy not only impacts performance but it also affects perception and mental health outcomes (Kim & Lee, 2024).
In this context, self-efficacy is a fresh face of workplace armor. It does not merely facilitate employees to excel; it equips them to endure emotionally in AI-powered worlds.
A New Face of Burnout
Historically, burnout has been linked to long working hours, poor management, unrealistic expectations, and emotionally demanding jobs. It was often understood as the consequence of being overworked in physically or mentally exhausting environments. However, in the algorithmic era, burnout is taking on a new and more invisible form, one rooted not only in overwork, but also in disconnection, disempowerment, and constant digital overload. Today, algorithms shape how we work, communicate, consume information, and even rest. Notifications blur the boundaries between personal and professional life, while productivity-driven digital cultures create pressure to always be available, responsive, and efficient. At the same time, algorithmic systems reduce autonomy by monitoring performance, predicting behaviour, and dictating routines in ways that can make individuals feel replaceable and emotionally detached from their work. The result is a form of burnout that is less about physical exhaustion alone and more about cognitive fatigue, emotional numbness, and the persistent feeling of never truly being “offline.”
Employees increasingly report feeling sidelined by AI systems that make decisions with little to no human input. Many feel as though their creativity, judgment, and individuality no longer hold the same value in workplaces driven by data, automation, and predictive analytics. Others describe a growing sense of being constantly “watched” through performance metrics, productivity trackers, and algorithmic evaluations that monitor everything from response times to efficiency levels. This culture of continuous surveillance often leads employees to engage in chronic self-monitoring, creating heightened pressure to appear productive at all times. Over time, this can contribute to anxiety, emotional exhaustion, and a weakening sense of personal agency within the workplace.
This type of burnout is less obtrusive, less visible, but no less harmful. It may undermine self-esteem, drive up absenteeism, and, in extreme instances, result in depression or leaving the workforce altogether (Kim & Lee, 2024).
So, What Can Be Done?
1. Normalize Psychological Support in AI Transitions
Too frequently, AI is implemented in a purely technical way—get the system installed, train the employees, and done. Missing is the emotional onboarding. Organizations need to realize that implementing AI is not merely a technical change but a psychological one. That requires providing mental health support, check-ins, and safe spaces for employees to vent anxiety or confusion.
2. Foster Digital Self-Efficacy Across the Board
This is not simply a matter of giving one-off lessons. Organizations need to roll out continuous training, mentoring, and peer support initiatives to develop digital confidence. The objective is not technical competence alone but emotional resilience.
3. Redesign AI with Empathy in Mind
AI systems must be constructed with user agency at their center. Workers need to know how algorithms work, what data is being drawn upon, and where human input is placed in the decision-making pipeline. Transparency and inclusion lower fear and resistance.
4. Reframe Measures of Success
Burnout usually results from being judged by inhuman criteria. Businesses must be careful not to over-rely on algorithmic assessment. Human feedback, teamwork, and imagination need to be preserved in performance measurement.
5. Create Ethical Principles Around Mental Health and AI
We require sector-wide guidelines that take into account the psychological effects of AI. Mental health needs to be an integral part of ethical AI, no less than privacy, bias, or transparency.
Re-Centering the Human in the AI Dialogue
One of the most significant contributions of Kim and Lee’s research is that the real cost of AI isn’t simply in terms of job loss or data risk, but in the emotional lives of actual human beings. AI isn’t neutral. AI alters how people feel about their work, their worth, and their future. If we forget the mental health consequences of AI adoption, we risk creating an efficient but inhuman workplace, optimised, but emotionally unsustainable. The algorithm age is upon us. The question is whether we will greet it with empathy, foresight, and care, or whether we will trade human well-being at the altar of automation. The future of work doesn’t just rely on smarter machines. It relies on more humane systems.
References
Kim, B.-J., & Lee, J. (2024). The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-04018-w
Akshita Pandey is an undergraduate student at FLAME University, and Moitrayee Das is an assistant professor of psychology at FLAME University.