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Introduction
Artificial intelligence is rapidly reshaping the way organisations manage people, design work and evaluate performance. Across sectors, AI-enabled systems are increasingly used in recruitment and selection, people analytics, training and development, task allocation, employee monitoring, performance appraisal and workforce planning. While these developments are often presented as routes to greater efficiency, objectivity and strategic insight, they also raise significant questions about fairness, transparency, employee voice, job quality, capability development, inclusion and organisational effectiveness. The central challenge is no longer simply whether organisations adopt AI, but how AI-enabled people-management systems are designed, governed and experienced, and with what consequences for employees, managers and wider organisational performance.
This Special Issue, “Beyond AI Adoption: People Management, Work Design and Sustainable Performance in AI-Enabled Organisations,” responds to this challenge by shifting scholarly attention from AI adoption to AI-enabled HRM as a people-performance system. Existing research has made important contributions by identifying the promise and limitations of artificial intelligence in HRM. Tambe et al., (2019) and Zhang et al., (2025), for example, highlight the gap between the promise and reality of AI in HRM, drawing attention to issues of data limitations, complexity, accountability, fairness and employee reactions to algorithmic decision-making. Similarly, Samishetti (2025) show that artificial intelligence, robotics and advanced technologies are transforming HRM practices, including recruitment, training, decision-making, collaboration and job performance. However, the relationship between AI-enabled HRM and organisational effectiveness remains insufficiently understood.
The Special Issue is positioned around this underdeveloped relationship. It asks how AI-enabled people-management systems produce, constrain or redirect performance outcomes through specific people-related mechanisms. These include perceived fairness, trust, employee voice, job quality, wellbeing, access to development, inclusion, capability-building, human oversight and governance. Rather than assuming that AI improves organisational performance, the Special Issue invites research that examines when, how, for whom and under what conditions AI-enabled HRM strengthens or undermines sustainable work and organisational effectiveness.
This focus is important because algorithmic systems are not neutral technical tools. They actively reshape organisational control, coordination and evaluation. Mettler (2024) discusses that algorithms introduce new forms of workplace control, affecting how workers are directed, evaluated, disciplined and rewarded. Raisch and Krakowski (2021) and Guo et al. (2025) further suggest that AI creates a persistent automation - augmentation paradox, where organisations must navigate tensions between replacing human work and enhancing human capability. These debates are highly relevant to HRM and organisational effectiveness because people-management systems increasingly mediate the relationship between technological capability and human outcomes.
At the same time, the Special Issue connects AI-enabled HRM with the literature on sustainable HRM and sustainable work. Cooke (2025) argues that sustainable HRM requires attention to outcomes beyond narrow financial performance, including human and social consequences. Austen & Piwowar-Sulej (2025) similarly call for HRM approaches that contribute to the common good and address wider societal challenges. This Special Issue builds on these debates by examining how AI-enabled HRM can support, or potentially undermine, sustainable organisational performance. Sustainable performance is therefore understood not only as productivity or efficiency, but as performance achieved through fair access to opportunity, job quality, employee voice, capability development, wellbeing, inclusion and credible organisational outcomes.
The topicality of the Special Issue is reinforced by growing policy and regulatory attention. The OECD’s work on algorithmic management shows that software systems, including AI, are increasingly used to automate or support managerial tasks traditionally performed by human managers, with potential benefits for productivity and consistency but also risks for workers. The EU AI Act further identifies employment, recruitment and worker management as areas requiring particular attention, especially where AI systems affect access to work, promotion, task allocation or performance evaluation. These developments make it timely to examine how organisations can govern AI-enabled HRM responsibly, not only to comply with regulation, but to build more legitimate, inclusive and effective people-management systems.
The Special Issue therefore welcomes conceptual, empirical, methodological and review-based contributions that advance understanding of AI-enabled HRM and organisational effectiveness. Relevant areas include AI-enabled recruitment and selection; AI-supported training, reskilling and career development; algorithmic performance management and employee monitoring; work design, autonomy and job quality; employee voice, trust and human oversight; equality, diversity and inclusion; disability inclusion and reasonable adjustment; and the governance of AI-enabled HRM systems across different organisational and institutional contexts.
By bringing these themes together, the Special Issue aims to develop a more integrated research agenda for AI-enabled HRM, sustainable work and organisational effectiveness. Its contribution lies in moving beyond technology-centred narratives of adoption and toward a more critical, people-centred and performance-relevant understanding of AI at work. In doing so, it seeks to support scholarship that is theoretically rigorous, empirically grounded and practically relevant to organisations seeking to use AI in ways that enhance, rather than erode, human capability, fairness, wellbeing and sustainable performance.
References
Austen, A., & Piwowar-Sulej, K. (2025). Addressing big societal challenges: Towards Common Good HRM. Organizacja i Kierowanie, 197(1), 23-37
Cooke, F. L. (2025). From strategic HRM to sustainable HRM? Exploring a common good approach through a critical reflection on existing literature. Human Resource Management, 64(5), 1381-1399.
Guo, M., Gu, M., & Huo, B. (2025). The impacts of automation and augmentation AI use on physicians’ performance: an ambidextrous perspective. International Journal of Operations & Production Management, 45(1), 114-151.
Mettler, T. (2024). 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.
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42.
Samishetti, B. (2025). The impact of ai and robotics on employee roles and HR practices: challenges, transformations, and strategic workforce management. Journal of Strategic Human Resource Management, 14(2), 31-39.
Zhang, M. M., Cooke, F. L., Ahlstrom, D., & McNeil, N. (2025). The rise of algorithmic management and implications for work and organisations. New Technology, Work and Employment, 40(3), 659-671.
List of Topic Area
The themes below are designed as empirical sites through which contributors can examine the theoretical gaps identified above. Each theme asks how AI-enabled HRM affects organisational effectiveness through specific people-related mechanisms, rather than treating AI adoption as an outcome in itself.
- AI-enabled recruitment, selection and fair access to work: How AI screening, automated assessments, candidate ranking, video interviews and people analytics affect fairness, validity, transparency, accessibility and selection outcomes.
- AI-supported training, development and capability building: How AI-enabled learning systems, skills analytics, career platforms and talent systems affect reskilling, development access, career visibility and employee capability.
- AI-enabled performance management, monitoring and evaluation: How algorithmic monitoring, datafication and AI-supported appraisal affect fairness, trust, employee agency, wellbeing, reasonable adjustment and performance outcomes.
- Work design, job quality and employee voice in AI-enabled workplaces: How AI changes autonomy, workload, coordination, meaningful work, participation, consultation and human oversight in people-management decisions.
- Inclusion, disability, governance and sustainable organisational effectiveness: How organisations design AI-enabled HRM systems that reduce bias, support EDI, protect disabled workers and applicants, strengthen trust and contribute to sustainable organisational performance.
Submissions Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available at: ScholarOne Manuscripts
Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/joepp
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: 25/06/2026
Closing date for manuscripts submission: 25/01/2027
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