Research Article

Artificial Intelligence in Human Resource Management: Applications, Ethical Challenges and Future Research Directions

Mr. Hari Kumar H, Ms. Gayathri M and Ms. Sangamithra R

Authors Mr. Hari Kumar H, Ms. Gayathri M and Ms. Sangamithra R
Pages 1-10
Received 2026-07-15
Accepted 2026-07-29
Published 2026-08-11

Abstract

Purpose: Artificial intelligence (AI) is increasingly transforming human resource management (HRM) by changing how organisations attract, select, develop, evaluate and retain employees. This conceptual review examines the principal applications of AI across the employee lifecycle, the organisational benefits associated with AI-enabled HRM, and the ethical and managerial challenges that may constrain responsible adoption. Design/methodology/approach: The paper adopts a conceptual review approach based on secondary literature from peer-reviewed academic research and selected institutional and professional sources. The literature is organised thematically around AI applications in recruitment and selection, onboarding, learning and development, performance management, employee engagement, workforce analytics and payroll-related administration. Findings: AI can improve processing speed, support evidence-informed HR decisions, personalise employee services and strengthen workforce analytics. However, the literature also identifies important risks involving algorithmic bias, privacy, explainability, accountability, employee acceptance and over-reliance on automated recommendations. AI in HR therefore requires governance mechanisms that combine technological capability with meaningful human oversight. Originality/value: The paper integrates the operational and ethical dimensions of AI-enabled HRM into a unified conceptual framework. It argues that the future of AI in HRM should be understood not as simple automation, but as a human–AI decision system in which efficiency, fairness, transparency and employee trust must be jointly managed.

Keywords: artificial intelligence; human resource management; HR analytics; recruitment; employee engagement; algorithmic bias; ethical AI; workforce analytics
📄

Full Article PDF

Download PDF

Cite This Article

Cite this Article

Scroll to Top