Inteligência Artificial e o bem-estar no trabalho: Da teoria à transformação organizacional
Palavras-chave:
Inteligência Artificial, Bem-estar organizacional, Saúde Mental, Colaboradores, Maturidade digital, Ética OrganizacionalResumo
A procura por soluções que contribuam para o bem-estar dos colaboradores nas organizações tem intensificado, graças à crescente preocupação com a saúde física e mental dos profissionais. Com base na análise de artigos empíricos e de reflexão crítica, o presente artigo avalia a contribuição da Inteligência Artificial (IA) no bem-estar organizacional em seis domínios: monitorização da saúde mental, análise preditiva de risco psicológico, aconselhamento emocional personalizado, otimização do conforto físico, redução da carga cognitiva e decisões éticas nos processos de recursos humanos. Considera-se que a IA funciona como uma oportunidade para transformar positivamente a experiência laboral, quando bem implementada. Todavia, para garantir que a IA funciona como uma promessa tecnológica com impacto real na saúde corporativa, é necessário garantir que a sua integração na cultura organizacional é ética e responsável e que os colaboradores possuem maturidade e literacia digital.
Referências
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
Downloads
Publicado
Como Citar
Edição
Secção
Licença
Direitos de Autor (c) 2025 The Trends Hub
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição-NãoComercial-SemDerivações 4.0.
