AI and Workplace Wellbeing
Artificial intelligence (AI) is a field of computer science focused on developing systems that can perform tasks typically associated with human intelligence, such as learning, reasoning, decision-making, pattern recognition, and language processing (García-Madurga et al., 2024; Russell et al., 1995). By analyzing large datasets and identifying meaningful patterns, AI systems are increasingly applied to complex organizational challenges, including workplace well-being. The relevance of such applications is underscored by the growing prevalence of mental health conditions among employees, with more than 40% of sickness benefit claims attributed to mental or behavioral disorders (Cahill et al., 2021). This trend highlights the need for innovative, preventive, and data-driven approaches to support employee mental health.
One central contribution of AI lies in its capacity for continuous monitoring and early detection of psychological strain. AI systems can analyze real-time indicators such as mood, stress, and fatigue to generate personalized feedback and self-care recommendations (Chang, 2020). At the same time, aggregated insights can support line managers in identifying emerging patterns of strain within teams and responding proactively. This monitoring function can be implemented through advanced sensing technologies. For example, Izumi et al. (2021) show how machine learning algorithms estimate stress and well-being using physiological and behavioral data from cameras, microphones, and wearable devices, constructing individualized digital stress profiles over time.
In addition to sensor-based monitoring, AI can also interpret communicative and behavioral data to assess mental health trends. Hoque Tania et al. (2022) explore the application of sentiment analysis to understand how individuals express emotions related to work and health in real time. Their conceptual model illustrates how publicly available digital traces, when systematically combined, may reveal evolving patterns of psychological well-being. These approaches collectively position AI as a predictive tool capable of identifying psychosocial risks before they escalate.
However, detection alone is insufficient without supportive intervention. AI technologies can also provide direct emotional assistance through chatbots and intelligent messaging platforms that offer conversational guidance and basic counseling (García-Madurga et al., 2024). Such tools increase accessibility to support services and may reduce barriers associated with stigma or limited resources. Moreover, the interaction between employees and AI systems can influence how workplace demands are experienced. Loureiro et al. (2023) argue that manageable or “benign” stress may enhance engagement and happiness when employees effectively collaborate with AI technologies. In this way, AI can function not only as a monitoring mechanism but also as a supportive resource that strengthens job satisfaction and organizational commitment.
Building upon monitoring and emotional support, AI also enables the development of personalized wellness initiatives. Through mobile applications and wearable technologies, continuous health data—including mental health indicators—can inform targeted interventions such as ergonomic adjustments and injury prevention strategies (Cahill et al., 2021; García-Madurga et al., 2024). Corporate wellness programs increasingly incorporate self-tracking technologies to encourage proactive health management. Lorenzini et al. (2023) extend this perspective by emphasizing that effective workplace well-being requires systematic assessment of both physical and psychosocial risks. They highlight the importance of accurate, noninvasive monitoring systems capable of detecting ergonomic strain and physiological stress responses, arguing that comprehensive risk identification must include stressful work conditions and behavioral patterns that may undermine mental health.
Finally, AI contributes to workplace well-being through education and skill development. AI-driven training systems can deliver personalized learning experiences that strengthen coping strategies and resilience. Howard (2019) examines the use of AI-supported virtual reality simulations that recreate hazardous work scenarios in immersive environments. By allowing employees to practice hazard recognition and response in a controlled setting, these technologies can enhance preparedness, confidence, and psychological resilience.
Overall, the literature suggests a progressive integration of AI in workplace well-being strategies. AI is positioned as a tool that can support early detection of mental health risks, enable continuous monitoring, deliver personalized interventions, provide accessible emotional support, and strengthen long-term resilience through training and development (García-Madurga et al., 2024). When implemented responsibly and combined with human oversight, AI has the potential to contribute to more proactive, comprehensive, and sustainable approaches to employee mental health and organizational well-being.
References:
Cahill, J., Howard, V., Huang, Y., Ye, J., Ralph, S., & Dillon, A. (2021, July). Intelligent work: Person centered operations, worker wellness and the triple bottom line. In International Conference on Human-Computer Interaction (pp. 307–314). Cham: Springer International Publishing.
Chang, K. (2020). Artificial intelligence in personnel management: The development of APM model. The Bottom Line, 33(4), 377–388.
García-Madurga, M.-Á., Gil-Lacruz, A.-I., Saz-Gil, I., & Gil-Lacruz, M. (2024). The role of artificial intelligence in improving workplace well-being: A systematic review. Businesses, 4(3), 389–410. https://doi.org/10.3390/businesses4030024
Hoque Tania, M., Hossain, M. R., Jahanara, N., Andreev, I., & Clifton, D. A. (2022). Thinking aloud or screaming inside: Exploratory study of sentiment around work. JMIR Formative Research, 6(9), e30113.
Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917–926.
Izumi, K., Minato, K., Shiga, K., Sugio, T., Hanashiro, S., Cortright, K., … Kishimoto, T. (2021). Unobtrusive sensing technology for quantifying stress and well-being using pulse, speech, body motion, and electrodermal data in a workplace setting: Study concept and design. Frontiers in Psychiatry, 12, 611243.
Lorenzini, M., Lagomarsino, M., Fortini, L., Gholami, S., & Ajoudani, A. (2023). Ergonomic human-robot collaboration in industry: A review. Frontiers in Robotics and AI, 9, 813907.
Loureiro, S. M. C., Bilro, R. G., & Neto, D. (2023). Working with AI: Can stress bring happiness? Service Business, 17(1), 233–255.
Russell, S., & Norvig, P. (1995). Artificial intelligence: A modern approach. Prentice Hall.
