Artificial Intelligence and well-being at work: From theory to organisational transformation

Authors

DOI:

https://doi.org/10.34630/tth.v1i5.6216

Keywords:

Artificial Intelligence, Organizational Well-being, Mental Health, Employees, Digital Maturity, Organizational Ethics

Abstract

The search for solutions that contribute to employee well-being within organizations has intensified, driven by the growing concern for professionals’ physical and mental health. Based on the analysis of empirical studies and critical reflections, this article evaluates the contribution of Artificial Intelligence (AI) to organizational well-being across six domains: mental health monitoring, predictive analysis of psychological risk, personalized emotional counseling, optimization of physical comfort, reduction of cognitive load, and ethical decision-making in human resource processes. AI is considered a valuable opportunity to positively transform the work experience when properly implemented. However, to ensure that AI moves beyond a technological promise and generates real impact on corporate health, its integration into organizational culture must be both ethical and responsible, and employees must possess sufficient digital literacy and maturity.

References

Anan, T., Kajiki, S., Oka, H., Fujii, T., Kawamata, K., Mori, K., & Matsudaira, K. (2021). Effects of an artificial intelligence–assisted health program on workers with neck/shoulder pain/stiffness and low back pain: randomized controlled trial. JMIR mHealth and uHealth, 9(9), e27535. https://doi.org/10.2196/27535

Arakawa, Y. (2019). Sensing and changing human behavior for workplace wellness. Journal of information processing, 27, 614-623. https://doi.org/10.2197/ipsjjip.27.614

Arakawa, R., Yakura, H., & Goto, M. (2023, April). CatAlyst: domain-extensible intervention for preventing task procrastination using large generative models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-19). https://doi.org/10.1145/3544548.3581133

Baydili, İ., Tasci, B., & Tasci, G. (2025). Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses. Diagnostics, 15(4), 434. https://doi.org/10.3390/diagnostics15040434

Brachten, F., Brünker, F., Frick, N. R., Ross, B., & Stieglitz, S. (2020). On the ability of virtual agents to decrease cognitive load: an experimental study. Information Systems and e-Business Management, 18(2), 187-207. https://doi.org/10.1007/s10257-020-00471-7

Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-computer Interaction, 5(CSCW1), 1-21. https://doi.org/10.1145/3449287

Chung, J., & Teo, J. (2022). Mental health prediction using machine learning: taxonomy, applications, and challenges. Applied Computational Intelligence and Soft Computing, 2022(1), 9970363. https://doi.org/10.1155/2022/9970363

Deloitte. (2020). 2020 Global Human Capital Trends: The social enterprise at work—Paradox as a path forward. https://www2.deloitte.com/content/dam/insights/us/articles/us43244_human-capital-trends-2020/us43244_human-capital-trends-2020/di_hc-trends-2020.pdf

de Oliveira, C., Saka, M., Bone, L., & Jacobs, R. (2023). The role of mental health on workplace productivity: a critical review of the literature. Applied health economics and health policy, 21(2), 167-193. https://doi.org/10.1007/s40258-022-00761-w

Direção-Geral da Administração e do Emprego Público. (n.d.). Guia técnico: Vigilância da saúde mental dos trabalhadores – Versão síntese. https://bussola.gov.pt/Guias%20Prticos/Guia%20técnico%20vigilância%20da%20saúde%20mental%20dos%20trabalhadores%20-%20Versão%20síntese.pdf

Donisi, L., Cesarelli, G., Pisani, N., Ponsiglione, A. M., Ricciardi, C., & Capodaglio, E. (2022). Wearable sensors and artificial intelligence for physical ergonomics: A systematic review of literature. Diagnostics, 12(12), 3048. https://doi.org/10.3390/diagnostics12123048

dos Psicólogos Portugueses, O. (2021). Locais de Trabalho Mais Saudáveis e Produtivos: A Importância do Bem-Estar Organizacional. https://recursos.ordemdospsicologos.pt/files/artigos/contributo_cientifico_opp_a_import__ncia_do_bem_estar_organizacional.pdf

dos Santos Gomes, V. H., Silva, R. C. A., & da Silva Neri, T. C. (2023). Ética em sistemas de IA: um olhar sobre a injustiça algorítmica e a deficiência. Revista dos Mestrados Profissionais, 12(2), 238-260. https://dialnet.unirioja.es/servlet/articulo?codigo=9291349

Gallup. (2023, June 13). State of the Global Workplace: 2023 Report. https://www.gallup.com/workplace/349484/state-of-the-global-workplace.aspx

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, (ART-2024-139520). https://zaguan.unizar.es/record/144759

Gartner. (2024). Future of work trends. https://www.onestream.com/gartner

Hernández, E. G. (2024). Towards an ethical and inclusive implementation of artificial intelligence in organizations: a multidimensional framework. arXiv preprint arXiv:2405.01697. https://arxiv.org/abs/2405.01697

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. https://doi.org/10.3389/fpsyt.2021.611243

Jindo, T., Kai, Y., Kitano, N., Wakaba, K., Makishima, M., Takeda, K., ... & Arao, T. (2020). Impact of activity-based working and height-adjustable desks on physical activity, sedentary behavior, and space utilization among office workers: a natural experiment. International journal of environmental research and public health, 17(1), 236. https://doi.org/10.3390/ijerph17010236

Kirsten, W. (2024). The evolution from occupational health to healthy workplaces. American Journal of Lifestyle Medicine, 18(1), 64-74. https://doi.org/10.1177/15598276221113509

Koivunen, S., Ala-Luopa, S., Olsson, T., & Haapakorpi, A. (2022). The march of Chatbots into recruitment: recruiters’ experiences, expectations, and design opportunities. Computer Supported Cooperative Work (CSCW), 31(3), 487-516. https://doi.org/10.1007/s10606-022-09429-4

Makridis, C. A., Zhao, D. Y., Bejan, C. A., & Alterovitz, G. (2021). Leveraging machine learning to characterize the role of socio-economic determinants on physical health and well-being among veterans. Computers in Biology and Medicine, 133, 104354. https://doi.org/10.1016/j.compbiomed.2021.104587

Michael Page. (2024). Burnout e stress no trabalho: Como identificar sinais e apoiar os colaboradores. https://www.michaelpage.pt/advice/lideranca-e-gestao-de-equipas/burnout-stress-no-trabalho

Mitravinda, K. M., Nair, D. S., & Srinivasa, G. (2023). Mental health in tech: Analysis of workplace risk factors and impact of covid-19. SN computer science, 4(2), 197. https://doi.org/10.1007/s42979-022-01613-z

Mudiyanselage, S. E., Nguyen, P. H. D., Rajabi, M. S., & Akhavian, R. (2021). Automated workers’ ergonomic risk assessment in manual material handling using sEMG wearable sensors and machine learning. Electronics, 10(20), 2558. https://doi.org/10.3390/electronics10202558

Organização Mundial da Saúde. (2010). Ambientes de trabalho saudáveis: um modelo para ação: para empregadores, trabalhadores, formuladores de políticas e profissionais. https://iris.who.int/bitstream/handle/10665/44307/9789241599313_por.pdf?sequence=2

Organização Pan-Americana da Saúde. (2022, 28 de setembro). OMS e OIT fazem chamado para novas medidas de enfrentamento das questões de saúde mental no trabalho. https://www.paho.org/pt/noticias/28-9-2022-oms-e-oit-fazem-chamado-para-novas-medidas-enfrentamento-das-questoes-saude

Peña, A., Serna, I., Morales, A., Fierrez, J., Ortega, A., Herrarte, A., ... & Ortega-Garcia, J. (2023). Human-centric multimodal machine learning: Recent advances and testbed on AI-based recruitment. SN Computer Science, 4(5), 434. https://doi.org/10.1007/s42979-023-01733-0

Rodney, H., Valaskova, K., & Durana, P. (2019). The artificial intelligence recruitment process: How technological advancements have reshaped job application and selection practices. Psychosociological Issues in Human Resource Management, 7(1), 42-47. https://www.ceeol.com/search/article-detail?id=770876

Schaab, B. L., Cunha, L. F., Silveira, D. C., da Silva, P. C., Ballejos, K. G., Diaz, G. B., ... & Reppold, C. T. (2024). A pilot study of a new app based on self-compassion for the prevention and promotion of mental health among Brazilian college students. Frontiers in Psychology, 15, 1414948. https://doi.org/10.3389/fpsyg.2024.1414948

Schaab, B. L., Calvetti, P. Ü., Hoffmann, S., Diaz, G. B., Rech, M., Cazella, S. C., ... & Reppold, C. T. (2024). How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review. Cadernos de Saúde Pública, 40, e00029323. https://doi.org/10.1590/0102-311XPT029323

Schmidhuber, J., Schlögl, S., & Ploder, C. (2021, September). Cognitive load and productivity implications in human-chatbot interaction. In 2021 IEEE 2nd international conference on human-machine systems (ICHMS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICHMS53169.2021.9582445

Segkouli, S., Giakoumis, D., Votis, K., Triantafyllidis, A., Paliokas, I., & Tzovaras, D. (2023). Smart Workplaces for older adults: Coping ‘ethically’with technology pervasiveness. Universal Access in the Information Society, 22(1), 37-49. https://doi.org/10.1007/s10209-021-00829-9

Sundarajan, A. (2025). Enhancing Workplace Productivity and Well-being Using AI Agent. arXiv preprint arXiv:2501.02368. https://doi.org/10.48550/arXiv.2401.09861

Tiwari, P., Pandey, R., Garg, V., & Singhal, A. (2021, January). Application of artificial intelligence in human resource management practices. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 159-163). IEEE. https://doi.org/10.1109/Confluence51648.2021.9377160

Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600-1631. https://doi.org/10.1002/smj.3322

Trenerry, B., Chng, S., Wang, Y., Suhaila, Z. S., Lim, S. S., Lu, H. Y., & Oh, P. H. (2021). Preparing workplaces for digital transformation: An integrative review and framework of multi-level factors. Frontiers in psychology, 12, 620766. https://doi.org/10.3389/fpsyg.2021.620766

Vinson, D. W., Arcan, M., Niland, D. P., & Delahunty, F. (2024). Towards Sustainable Workplace Mental Health: A Novel Approach to Early Intervention and Support. arXiv preprint arXiv:2402.01592. https://arxiv.org/abs/2402.01592

World Health Organization (WHO). (n.d.). Occupational health. World Health Organization. https://www.who.int/health-topics/occupational-health

WTW. (2024, June 5). The grass isn’t greener for employees, as majority prefer to stay in their current jobs. https://www.wtwco.com/en-us/news/2024/06/the-grass-isnt-greener-for-employees-as-majority-prefer-to-stay-in-their-current-jobs-the-grass

Zhang, X., Zheng, P., Peng, T., He, Q., Lee, C. K., & Tang, R. (2022). Promoting employee health in smart office: A survey. Advanced Engineering Informatics, 51, 101518. https://doi.org/10.1016/j.aei.2021.101518

Published

2025-06:-09

How to Cite

Soares, J. F. (2025). Artificial Intelligence and well-being at work: From theory to organisational transformation. The Trends Hub, 1(5). https://doi.org/10.34630/tth.v1i5.6216