Generative AI: Boosting Adaptability and Reducing Workplace Overload

Abstract

ABSTRACT In the evolving digital work environment, the rising prevalence of generative AI tools presents a complex challenge for practitioners: deciding whether to allow or restrict their use in organizational settings. Our research contributes to expanding the broaden-and-build theory and the job demands-resources model (JD-R) within the context of generative AI tools usage. This study investigates the impact of generative AI tools on employees’ perceived work overload, focusing on the mediating role of employee adaptability. Utilizing a survey of 307 employees and Structural Equation Modeling (SEM) techniques, findings show that generative AI tools usage by employees not only directly reduces perceived work overload but also significantly boosts employee adaptability, further decreasing perceived work overload. This highlights the dual benefits of generative AI tools in the workplace, offering valuable insights for managers on consciously integrating these technologies to enhance adaptability and reduce workload stress.