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

Kawthar Bouzerda ,Selimane Hani ,Hasnae Rahmani ,Ali Hebaz ,Abdessamad Dibi ,Hasna Mharzi

doi:10.18282/hrms4595

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

Kawthar Bouzerda

Selimane Hani

Hasnae Rahmani

Ali Hebaz

Abdessamad Dibi

Hasna Mharzi

Human Resources Management and Services 2025, 7(4), 4595; https://doi.org/10.18282/hrms4595

Submitted：17 Jun 2025

Accepted：31 Jul 2025

Published：14 Nov 2025

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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.

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References

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