Beyond Productivity: The Performance-Wellbeing Paradox in AI-Enabled Work

Introduction

Artificial intelligence (AI) is rapidly transforming contemporary workplaces, reshaping how work is organised, managed, and experienced. While these developments promise enhanced efficiency and productivity, they also surface critical tensions between organisational performance imperatives and employee wellbeing (Chandhary et al., 2023). Emerging scholarship highlights both the opportunities and the risks associated with AI-enabled systems, including increased augmentation and innovation alongside intensified surveillance, job insecurity, and psychosocial strain (Bankins et al., 2024; Mettler, 2023).

This special issue, Beyond Productivity: The Performance–Wellbeing Paradox in AI-Enabled Work, invites contributions that critically examine how AI reconfigures the relationship between performance and wellbeing across organisational contexts to create social impact. AI-enabled work refers to the integration of AI-related technologies to the organisational infrastructure and as part of the position description concerning employee’s job responsibilities, while simultaneously keeping the human-in-the-loop to minimise risk and errors (Rabhi, Beheshti and Gill 2025; Rani and Dhir 2024).

Social impact in business research is the “ creation of value for people through business scholarly activities resulting in intentional improvement in micro, meso or macro economic, human and environmental phenomena over time with the market and organisations leading, partnering, supporting or yielding (to) change” (Russell-Bennett and Reid 2026 p.5). In the context of AI-enabled systems in the workplace, examples of social impact phenomena include job security, minimisation of psychosocial strain, productivity, empowerment, fairness, and employee autonomy, as well as broader outcomes such as inclusion and inequality (Bankins et al., 2024; Budhwar et al., 2023; Giermindl et al., 2021; Mettler, 2023; Robert et al., 2020). These phenomena reflect how AI-enabled organisational practices can simultaneously enhance performance and augment human capabilities, while also introducing risks related to surveillance, work intensification, and the erosion of trust and wellbeing, thereby positioning workplace wellbeing as a critical dimension of social impact in contemporary organisations (Sarala et al., 2025; Nanjundeswaraswamy et al., 2026; Wood, 2021).

Recent research suggests that AI-driven management practices, such as people analytics and algorithmic decision-making, can enhance productivity while simultaneously undermining autonomy, fairness, and trust (Giermindl et al., 2021; Robert et al., 2020). These dynamics reveal a paradox where systems designed to optimise performance may inadvertently erode the human conditions necessary for sustainable work.

At the same time, the rapid diffusion of AI across industries has intensified debates regarding the future of work, inequality, and organisational responsibility (Sarala et al., 2025). Organisations are increasingly implementing AI without fully understanding the social impact of such implementation such as employee wellbeing, inclusion, and long-term social impact (Nanjundeswaraswamy et al., 2026). This creates an urgent need for interdisciplinary and practice-oriented research that moves beyond instrumental views of technology toward a more nuanced understanding of human–AI entanglements.

We welcome submissions that explore how AI reshapes organisational systems and creates social impact for employees in areas such as performance management, learning and development, reward structures, and workforce planning. Contributions may adopt critical, empirical, or conceptual approaches to investigate issues including autonomy, identity, job quality, inclusion, and organisational ethics.

By advancing the concept of a performance–wellbeing paradox, this special issue aims to generate actionable insights for building more human-centred, equitable, and sustainable AI-enabled workplaces and creating social impact for employees.

References:

Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159-182. https://doi.org/10.1002/job.2735

Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A. and Dey, P.K., 2023. Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), pp.606-659.

Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G.J., Beltran, J.R., Chaudhary, M., Sharma, D., Dhiman, M. C., & Chaudhary, A. (2023). How human resource managers can utilize AI to promote employee well-being. In AI and emotional intelligence for modern business management (pp. 263-281). IGI Global Scientific Publishing.

Giermindl, L.M., Strich, F., Christ, O., Leicht-Deobald, U. and Redzepi, A. (2021). The dark sides of people analytics: reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410-435. https://doi.org/10.1080/0960085X.2021.1927213

Mettler, T. (2023). The Connected Workplace: Characteristics and Social Consequences of Work Surveillance in the Age of Datification, Sensorization, and Artificial Intelligence. Journal of Information Technology, 39(3), 547-567. https://doi.org/10.1177/02683962231202535

Nanjundeswaraswamy, T., Nagesh, P., & Bharath, S. (2026). The role of artificial intelligence in enhancing employee wellbeing and performance: a study of hybrid work environments. Journal of Organizational Effectiveness: People and Performance. https://doi.org/10.1108/JOEPP-09-2025-0858

Rabhi, F., Beheshti, A. and Gill, A., 2025. Business transformation through AI-enabled technologies. Frontiers in Artificial Intelligence, 8, p.1577540.

Rani, U. and Dhir, R.K., 2024. AI-enabled business model and human-in-the-loop (deceptive AI): implications for labor. In Handbook of artificial intelligence at work (pp. 47-75). Edward Elgar Publishing.

Sarala, R.M., Post, C., Doh, J. and Muzio, D. (2025). Advancing Research on the Future of Work in the Age of Artificial Intelligence (AI). Journal of Management Studies. 62, 1863-1884. https://doi.org/10.1111/joms.13195

Robert, L. P., Pierce, C., Marquis, L., Kim, S., & Alahmad, R. (2020). Designing fair AI for managing employees in organizations: a review, critique, and design agenda. Human–Computer Interaction, 35(5-6), 545-575. https://doi.org/10.1080/07370024.2020.1735391

Russell-Bennett, R. and Reid, M. (2026), “Editorial: social impact in business research”, Journal of Social Impact in Business Research, 2(1): p1-19

Wood, A.J., 2021. Algorithmic management. Consequences for Work Organisation and Working Conditions.

List of topic areas

• The performance-wellbeing paradox in AI-enabled workplaces: Tensions between optimisation, control, productivity, and human agility and organisational resilience.
• Data-driven performance management and people analytics: Impacts on autonomy, fairness, psychological and psychosocial safety, and trust.
• Organisational design, workforce planning, and job insecurity: AI-driven restructuring, precarity, and the lived experience of future-of-work anxiety.
• Cross-cultural implications on tech-based wellbeing management: Influential sector specifications and differences due to workplace cultures, opportunities and risks for diversity and inclusion regarding neurodiversity, multi-generation, and gender.
• Wellbeing as social impact in digital transformation: Implications for inequality, ethics, governance, and sustainable business practices.

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/jsibr

Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/jsibr

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key deadlines

Opening date for manuscripts submissions: 01/07/2026

Closing date for manuscripts submission: 28/09/2026

Email for submissions: [email protected]