Impact of Generative AI on Stress Management and the Maintenance of Work-Life Balance: A Paradoxical Analysis

Abstract

The rapid integration of Generative Artificial Intelligence (GenAI) into organizational processes presents a paradoxical challenge to employee well-being, simultaneously acting as a resource for stress reduction and a source of new psychological demands. Empirically and conceptually, GenAI reduces workload stress by automating repetitive and time-consuming tasks, facilitating better time management and offering 24/7 mental health support via wellness applications and chatbots. Conversely, its widespread use introduces significant stressors, including job insecurity and skill obsolescence anxiety (technostress), increased surveillance pressures, and the erosion of the work-family boundary. The paper concludes that the net impact on WLB hinges on responsible, thoughtful GenAI implementation strategies that prioritize employee autonomy, ethical data governance, and organizational support systems. 1 2 3 4 5 6 The swift incorporation of Generative Artificial Intelligence (GenAI) across corporate environments introduces a significant contradiction to staff welfare, functioning as both an effective method for alleviating stress and a generator of novel mental burdens. This analysis, grounded in established theoretical models, explores the two-sided effect of adopting GenAI on an individual\'s ability to manage stress and sustain their Work-Life Balance (WLB). Fundamentally, GenAI diminishes pressure related to workload by mechanizing routine and labour-intensive activities, thereby enabling superior organization of time and continuous mental health assistance through dedicated wellness tools and virtual assistants. Conversely, the broad deployment of these technologies initiates considerable stress factors, such as the anxiety stemming from job instability and the fear of skills becoming irrelevant (technostress), elevated oversight demands, and the blurring of professional and personal life boundaries. The ultimate finding of this research is that the overall consequence for WLB is dependent upon judicious and ethical GenAI deployment tactics that place importance on employee independence, transparent data handling, and robust organizational support structures.

Introduction

GenAI is widely used in organizations to improve efficiency, automate routine tasks, and support decision-making. It helps employees save time, reduce workload, and focus on more creative or strategic work. It also supports better scheduling, flexibility, and even mental health through AI-based wellness tools.

However, the same technology also introduces new challenges. Employees experience technostress, job insecurity due to automation, pressure to continuously upskill, and increased workload from reviewing or correcting AI-generated outputs. AI also contributes to an “always-on” work culture, weakening the boundary between work and personal life and negatively affecting WLB.

The study uses the Job Demands–Resources (JD-R) model, which explains that GenAI acts both as:

• a job resource (reducing workload, improving efficiency, supporting well-being), and
• a job demand (creating stress through surveillance, skill anxiety, and cognitive overload).

Key insight:

The impact of GenAI is not purely positive or negative—it depends on how it is implemented in organizations. Factors like AI literacy, trust in AI systems, and employee autonomy determine whether GenAI reduces stress or increases it.

Conclusion

Generative AI fundamentally reshapes the terrain of stress and work-life balance. By automating routine tasks and delivering direct wellness support, it offers essential tools to handle workload stress and improve personal time. Yet, the concurrent rise of technostress, job insecurity, and surveillance pressures means that AI embodies a complex paradox. Future research should prioritize longitudinal studies and empirical investigations that quantify the \"Productivity - anxiety paradox\" examining the trade-off between time saved and mental energy expended due to new demands. In the end, GenAI’s beneficial impact on work-life balance will become evident only when companies embrace a balanced, ethical, and people-focused strategy that boosts efficiency without compromising the rights, trust or well-being of human employees.

References

[1] The literature review emphasizing AI\'s paradoxical influence and dual pathways (Job Demands-Resources model) on WLB and stress. [2] Empirical studies from the Indian context showing AI\'s role in reducing stress by minimizing repetitive activities and enhancing time management, while highlighting challenges related to privacy and job displacement. [3] Discussion of AI tools for well-being, including chatbots (Wysa, Woebot) for mental health support and smart scheduling for workload optimization. [4] Survey data revealing the psychological distress and anxiety (job insecurity, skill obsolescence) associated with GenAI use. [5] Conceptual discussion on the risks of deskilling, over-reliance on AI, and the shift in cognitive demands leading to increased high-stress case management. [6] The conceptualization of GenAI as a \"personal assistant\" that, through user training, can unlock time and energy to reclaim the day, supporting WLB. [7] Tariq, M. U. (Year N/A). AI and work-life balance: Transforming employee wellbeing in the modern workplace. [8] Wang, W., Hackett, R. D., Archer, N., Xu, Z., & Yuan, Y. (2025). Will AI-enabled conversational agents acting as digital employees enhance employee job identity? Information & Management, 62(2), 104099. [9] Bruni, A. (Year N/A). Employee Usage of Generative AI as Moderator of High Workload Effects on Emotional Exhaustion and Extra-Role Performance. [10] The literature review, The Relationship between Work-Life Balance, Job Stress, and Artificial Intelligence: A Comprehensive Literature Review

Copyright

Copyright © 2026 Dr. Ruchi Saxena. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.