This study investigates the impact of artificial intelligence (AI) integration on preventing employee burnout through a human-centered, multimodal approach. Given the increasing prevalence of AI in workplace settings, this research seeks to understand how various dimensions of AI integration—such as the intensity of integration, employee training, personalization of AI tools, and the frequency of AI feedback—affect employee burnout. A quantitative approach was employed, involving a survey of 320 participants from high-stress sectors such as healthcare and IT. The findings reveal that the benefits of AI in reducing burnout are substantial yet highly dependent on the implementation strategy. Effective AI integration that includes comprehensive training, high personalization, and regular, constructive feedback correlates with lower levels of burnout. These results suggest that the mere introduction of AI technologies is insufficient for reducing burnout; instead, a holistic strategy that includes thorough employee training, tailored personalization, and continuous feedback is crucial for leveraging AI’s potential to alleviate workplace stress. This study provides valuable insights for organizational leaders and policymakers aiming to develop informed AI deployment strategies that prioritize employee well-being.

Ajayi, F. A. & Udeh, C. A. (2024). Combating burnout in the it industry: a review of employee well-being initiatives. International Journal of Applied Research in Social Sciences, 6(4), 567–588. https://doi.org/10.51594/ijarss.v6i4.1010

Amora, J. T. (2021). Convergent validity assessment in PLS-SEM: A loadings-driven approach. Data Analysis Perspectives Journal, 2(3), 1-6.

Baquero, A. (2023). Hotel Employees’ Burnout and Intention to Quit: The Role of Psychological Distress and Financial Well-Being in a Moderation Mediation Model. Behavioral Sciences, 13(2), 84. https://doi.org/10.3390/bs13020084

De Vos, A., Van der Heijden, B. I. J. M., & Akkermans, J. (2020). Sustainable careers: Towards a conceptual model. Journal of Vocational Behavior, 117, 103196. https://doi.org/10.1016/j.jvb.2018.06.011

Fastje, F., Mesmer-Magnus, J., Guidice, R., et al. (2022). Employee burnout: the dark side of performance-driven work climates. Journal of Organizational Effectiveness: People and Performance, 10(1), 1–21. https://doi.org/10.1108/joepp-10-2021-0274

Fu, J., Long, Y., He, Q., et al. (2020). Can Ethical Leadership Improve Employees’ Well-Being at Work? Another Side of Ethical Leadership Based on Organizational Citizenship Anxiety. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01478

Gabriel, K. P., & Aguinis, H. (2022). How to prevent and combat employee burnout and create healthier workplaces during crises and beyond. Business Horizons, 65(2), 183–192. https://doi.org/10.1016/j.bushor.2021.02.037

Gadolin, C., Larsman, P., Nilsson, M. S., et al. (2022). How do healthcare unit managers promote nurses’ perceived organizational support, and which working conditions enable them to do so? A mixed methods approach. Scandinavian Journal of Psychology, 63(6), 648–657. Portico. https://doi.org/10.1111/sjop.12851

Greenglass, E. R., Fiksenbaum, L., & Burke, R. J. (2020). The Relationship Between Social Support and Burnout Over Time in Teachers. Occupational Stress, 239–248. https://doi.org/10.1201/9781003072430-21

Ivascu, L., Cioca, L.-I. & Filip, F. G. (editors). (2022). Intelligent Techniques for Efficient Use of Valuable Resources. Springer International Publishing. https://doi.org/10.1007/978-3-031-09928-1

Joseph, H., Hult, G. T. M., Ringle, C. M., et al. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature.

Kambur, E., & Akar, C. (2021). Human resource developments with the touch of artificial intelligence: a scale development study. International Journal of Manpower, 43(1), 168–205. https://doi.org/10.1108/ijm-04-2021-0216

Kock, N. (2020). Harman’s single factor test in PLS-SEM: Checking for common method bias. Data Analysis Perspectives Journal, 2(2), 1-6.

Kubo, T., Matsumoto, S., Sasaki, T., et al. (2021). Shorter sleep duration is associated with potential risks for overwork-related death among Japanese truck drivers: use of the Karoshi prodromes from worker’s compensation cases. International Archives of Occupational and Environmental Health, 94(5), 991–1001. https://doi.org/10.1007/s00420-021-01655-5

Lambert, D. M., & Harrington, T. C. (1990). Measuring non-response bias in customer service mail surveys. Journal of Business logistics, 11(2), 5-25.

Luo, M., & Lei, J. (2021). Using the JD-R Model to Predict the Organizational Outcomes of Social Workers in Guangzhou, China. Journal of the Society for Social Work and Research, 12(2), 349–369. https://doi.org/10.1086/714311

Malesic, J. (2022). The End of Burnout: Why Work Drains Us and How to Build Better Lives. University of California Press. https://doi.org/10.1525/9780520975347

Manley, S. C., Hair, J. F., Williams, R. I., et al. (2020). Essential new PLS-SEM analysis methods for your entrepreneurship analytical toolbox. International Entrepreneurship and Management Journal, 17(4), 1805–1825. https://doi.org/10.1007/s11365-020-00687-6

Maslach, C., & Leiter, M. P. (2022). The Burnout Challenge: Managing People’s Relationships with Their Jobs. Harvard University Press. https://doi.org/10.4159/9780674287297

Mendaglio, S., & Swanson, D. (2021). Stress and Burnout in Rehabilitative Settings. Management and Administration of Rehabilitation Programmes, 257–277. https://doi.org/10.4324/9781003110569-12

Mikko, H., Kati. K., (2020). Working hours, health, well-being and participation in working life: Current knowledge and recommendations for health and safety. Available online: https://urn.fi/URN:ISBN:9789522619129 (accessed on 23 May 2023).

Moriano, J. A., Molero, F., Laguía, A., et al. (2021). Security Providing Leadership: A Job Resource to Prevent Employees’ Burnout. International Journal of Environmental Research and Public Health, 18(23), 12551. https://doi.org/10.3390/ijerph182312551

Ogbeibu, S., Jabbour, C. J. C., Gaskin, J., et al. (2021). Leveraging STARA competencies and green creativity to boost green organisational innovative evidence: A praxis for sustainable development. Business Strategy and the Environment, 30(5), 2421–2440. Portico. https://doi.org/10.1002/bse.2754

Osei, H. V., Asiedu-Appiah, F., & Ansah, R. O. (2022). Work intensity, burnout and quality of work life in the hotel industry: The moderating role of psychological detachment. Journal of Human Resources in Hospitality & Tourism, 22(1), 26–48. https://doi.org/10.1080/15332845.2023.2126929

Osman, S. (2021). Factors Influencing Assessment Conceptions among Basic School Teachers: A Multiple Analysis of Variance. International Journal of Research and Innovation in Social Science, 05(11), 77–86. https://doi.org/10.47772/ijriss.2021.51111

Popescu, L., Bocean, C. G., Vărzaru, A. A., et al. (2022). A Two-Stage SEM—Artificial Neural Network Analysis of the Engagement Impact on Employees’ Well-Being. International Journal of Environmental Research and Public Health, 19(12), 7326. https://doi.org/10.3390/ijerph19127326

Ramlawati, R., Trisnawati, E., Yasin, N. A., et al. (2021). External alternatives, job stress on job satisfaction and employee turnover intention. Management Science Letters, 511–518. https://doi.org/10.5267/j.msl.2020.9.016

Rasoolimanesh, S. M. (2022). Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach. Data Analysis Perspectives Journal, 3(2), 1-8.

Rehder, K., Adair, K. C., & Sexton, J. B. (2021). The Science of Health Care Worker Burnout: Assessing and Improving Health Care Worker Well-Being. Archives of Pathology & Laboratory Medicine, 145(9), 1095–1109. https://doi.org/10.5858/arpa.2020-0557-ra

Rouder, J., Saucier, O., Kinder, R., et al. (2021). What to Do with All Those Open-Ended Responses? Data Visualization Techniques for Survey Researchers. Survey Practice, 14(1), 1–9. https://doi.org/10.29115/sp-2021-0008

Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial-Intelligence-Supported Reduction of Employees’ Workload to Increase the Company’s Performance in Today’s VUCA Environment. Sustainability, 15(6), 5019. https://doi.org/10.3390/su15065019

Samad Dahri, A., Asif Qureshi, M., & Ghaffar Mallah, A. (2020). The negative effect of incivility on job satisfaction through emotional exhaustion moderated by resonant leadership. 3C Empresa. Investigación y Pensamiento Crítico, 9(4), 93–123. https://doi.org/10.17993/3cemp.2020.090444.93-123

Sarmah, P., Van den Broeck, A., Schreurs, B., et al. (2021). Autonomy supportive and controlling leadership as antecedents of work design and employee well-being. BRQ Business Research Quarterly, 25(1), 44–61. https://doi.org/10.1177/23409444211054508

Sarstedt, M., Ringle, C. M., Cheah, J.-H., et al. (2019). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554. https://doi.org/10.1177/1354816618823921

Shaikh, F., Afshan, G., Anwar, R. S., et al. (2023). Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being. Asia Pacific Journal of Human Resources, 61(4), 794–820. https://doi.org/10.1111/1744-7941.12385

Swensen, S., & Shanafelt, T. (2020). Mayo Clinic Strategies to Reduce Burnout: 12 Actions to Create the Ideal Workplace. Oxford University Press. https://doi.org/10.1093/med/9780190848965.001.0001

Wu, Y., Wang, J., Liu, J., et al. (2020). The impact of work environment on workplace violence, burnout and work attitudes for hospital nurses: A structural equation modelling analysis. Journal of Nursing Management, 28(3), 495–503. Portico. https://doi.org/10.1111/jonm.12947

Zang, N., Cao, H., Zhou, N., et al. (2022). Job load, job stress, and job exhaustion among Chinese junior middle school teachers: Job satisfaction as a mediator and teacher’s role as a moderator. Social Psychology of Education, 25(5), 1003–1030. https://doi.org/10.1007/s11218-022-09719-1