As AI becomes more common in business processes, agentic AI systems have come about. These systems may work somewhat independently, make decisions, and adapt to changing work settings. These methods are clearly good for consistency and productivity, but it's not clear how they will influence workers yet. People are worried about how these technologies damage workers' sense of control over their jobs and their overall health. This study examines the relationship between the utilization of agentic AI and employee well-being, focusing on the role of perceived autonomy as a fundamental mechanism. It also looks into whether the way people feel about AI-driven workplaces is affected by how open the algorithmic systems are and how safe they feel psychologically. The study, grounded in the Job Demands–Resources framework and Self-Determination Theory, regards agentic AI as a possible source of both stress and support. The paper employs a quantitative, cross-sectional survey design, collecting data from 150 employees across industries that integrate AI in their organizations. Using 5-point Likert scale, measures and statistical analysis, the study examines the negative effects on the employees and their worries in understanding and adapting with AI systems at their workplace. The results are meant to add to the discourse about AI that puts people first and give organizations important tips for dealing with the changing nature of employment in the future.
- Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328.
- Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
- Burrell, J. (2016). How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12.
- Danna, K., & Griffin, R. W. (1999). Health and well-being in the workplace: A review and synthesis of the literature. Journal of Management, 25(3), 357–384.
- Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42.
- Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.
- Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383.
- Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
- Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.
- Meijerink, J., Bondarouk, T., & Lepak, D. P. (2021). Human resource management and algorithms: A review and research agenda. The International Journal of Human Resource Management, 32(12), 2547–2570.
- Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 71(4), 1171–1204.
- Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
- Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551.
