The rise of AI in human resource management: A systematic review of task automation through PRISMA

  • Kawthar Bouzerda * El Jadida, Laboratory of Studies and Research in Economic Sciences and Management (LERSEM), National School of Commerce and Management, Chaouaïb Doukkali University, El Jadida 24000, Morocco
  • Selimane Hani El Jadida, Laboratory of Studies and Research in Economic Sciences and Management (LERSEM), National School of Commerce and Management, Chaouaïb Doukkali University, El Jadida 24000, Morocco
  • Hasnae Rahmani El Jadida, Laboratory of Studies and Research in Economic Sciences and Management (LERSEM), National School of Commerce and Management, Chaouaïb Doukkali University, El Jadida 24000, Morocco
  • Ali Hebaz Multidisciplinary Research Laboratory in Social Engineering and Business Management (PRISME), École Nationale Supérieure d’Électricité et de Mécanique, Hassan II University of Casablanca, Casablanca 20360, Morocco
  • Abdessamad Dibi Multidisciplinary Research Laboratory in Social Engineering and Business Management (PRISME), École Nationale Supérieure d’Électricité et de Mécanique, Hassan II University of Casablanca, Casablanca 20360, Morocco
  • Hasna Mharzi Multidisciplinary Research Laboratory in Social Engineering and Business Management (PRISME), École Nationale Supérieure d’Électricité et de Mécanique, Hassan II University of Casablanca, Casablanca 20360, Morocco
Article ID: 4595
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DOI: https://doi.org/10.18282/hrms4595
Keywords: employee involvement; Artificial Intelligence (AI); human resource management; systematic literature review; PRISMA; algorithmic bias; open science

Abstract

Objective: This study synthesizes current evidence on the role of Artificial Intelligence (AI) and, where relevant, Open Science (OS) practices in enhancing Human Resource Management (HRM) performance. It focuses on recruitment processes, ethical considerations, and employee participation. Methodology: A systematic literature review was conducted in Scopus covering the period 2019–2024, following PRISMA guidelines. The initial search yielded 1486 records. After de-duplication and screening using Rayyan, 66 studies (≈ 4.4%) met the inclusion criteria, which targeted peer-reviewed works addressing AI-supported HR decision-making. A combined content and bibliometric analysis was performed in R (Bibliometrix) to identify thematic patterns and conceptual structures. Results: Analysis revealed four thematic clusters: 1) Implementation and employee participation emphasizing human-in-the-loop approaches and effective change management; 2) ethical challenges including algorithmic bias, transparency gaps, and data privacy risks; 3) data-driven decision-making delivering higher accuracy, fewer errors, and personalized recruitment and performance assessment; 4) operational efficiency enabling faster workflows and reduced administrative workloads. AI tools consistently improved selection quality, while OS practices promoted transparency and knowledge sharing. Implications: The successful adoption of AI in HRM requires employee engagement, strong ethical safeguards, and transparent data governance. Future research should address the long-term cultural, organizational, and well-being impacts of AI integration, as well as its sustainability.

Published
2025-11-14
How to Cite
Bouzerda, K., Hani, S., Rahmani, H., Hebaz, A., Dibi, A., & Mharzi, H. (2025). The rise of AI in human resource management: A systematic review of task automation through PRISMA. Human Resources Management and Services, 7(4). https://doi.org/10.18282/hrms4595
Issue
Vol. 7 No. 4 (2025)
Section
Review

Copyright (c) 2025 Kawthar Bouzerda, Selimane Hani, Hasnae Rahmani, Ali Hebaz, Abdessamad Dibi, Hasna Mharzi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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