Author
Listed:
- Murale Venugopal
- Vandana Madhavan
- Rajiv Prasad
- Raghu Raman
Abstract
This study explores the transformative role of artificial intelligence (AI) in human resource management (HRM), focusing on key functions such as recruitment, retention, and performance management. A comprehensive review was carried out PRISMA framework and BERTopic model on AI and HRM‑related keywords. The resulting publications were analyzed to extract meaningful topics. AI‑driven tools streamline candidate screening and interview analysis, significantly enhancing hiring efficiency and decision‑making accuracy. Concerns about algorithmic bias highlight the need for robust governance frameworks to ensure transparency and fairness in AI‑driven processes. The study emphasizes the importance of aligning AI adoption with Organizational Development principles to foster inclusivity and organizational justice. The integration of AI in performance management facilitates real‑time, objective performance assessments, although overreliance on such technologies can affect employee trust and engagement. Despite these advances, the study highlights ethical concerns surrounding data privacy and the potential for algorithmic bias. Addressing these challenges requires the implementation of comprehensive ethical frameworks to promote fairness and inclusivity in AI‑HRM applications. Strategically, AI transforms HR from a reactive function to a proactive, data‑driven partner aligned with long‑term organizational goals. Successful AI integration depends on governance mechanisms that uphold ethical standards, foster employee trust, and ensure transparency, enabling organizations to fully leverage AI’s potential in enhancing workforce management.
Suggested Citation
Murale Venugopal & Vandana Madhavan & Rajiv Prasad & Raghu Raman, 2024.
"Transformative AI in human resource management: enhancing workforce planning with topic modeling,"
Cogent Business & Management, Taylor & Francis Journals, vol. 11(1), pages 2432550-243, December.
Handle:
RePEc:taf:oabmxx:v:11:y:2024:i:1:p:2432550
DOI: 10.1080/23311975.2024.2432550
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