ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT: A SYSTEMATIC REVIEW OF WORKFORCE TRANSFORMATION, EMPLOYEE ENGAGEMENT, AND ETHICAL GOVERNANCE (2023–2026)

This study has presented a comprehensive secondary data analysis of Artificial Intelligence's transformative impact on Human Resource Management, workforce dynamics, employee engagement, and ethical governance during the period 2023–2026. The evidence overwhelmingly indicates that AI has transitioned from experimental HR tooling to core organisational infrastructure, fundamentally reshaping how organisations attract, develop, engage, and retain talent. The key findings can be summarised as follows: (i) the global AI in HR technology market is experiencing robust growth, with enterprise adoption rates increasing from 28% in 2023 to 47% in 2025; (ii) organisations leveraging AI in HRM report substantial performance improvements, particularly in recruitment efficiency (35% faster time-to-hire, 40% lower cost-per-hire) and workforce planning accuracy (42% improvement); (iii) five high-impact opportunities — intelligent talent acquisition, predictive workforce analytics, personalised learning, continuous performance intelligence, and wellbeing monitoring — are reshaping competitive dynamics; (iv) eight significant challenges — algorithmic bias (documented in 32% of systems), employee surveillance, AI anxiety, cultural erosion, regulatory complexity, data quality barriers, skill gaps, and explainability deficits — demand urgent attention; and (v) employee engagement is undergoing fundamental shifts toward continuous monitoring, data-driven recognition, hyper-personalised development, and algorithmic trust formation. The study's contribution lies in its systematic integration of diverse secondary data sources into a coherent analytical framework that bridges academic theory and practitioner imperatives. By employing the Opportunities-Challenges-Implications (OCI) framework and the PESTLE-AMO hybrid model, the research provides both diagnostic clarity and strategic direction for organisations navigating the AI-HRM transition. However, the study also acknowledges significant limitations, including the reliance on secondary data, the rapid obsolescence risk inherent in AI research, and potential biases in vendor-sponsored reports. Future research should prioritise primary data collection through longitudinal organisational studies, experimental designs comparing AI-augmented versus traditional HRM approaches, and cross-cultural comparative analyses examining regional variations in AI-HRM adoption and impact. As the HRM discipline confronts what may be its most significant technological disruption since the computerisation of payroll systems, the imperative for evidence-based, ethically-grounded, and strategically-informed AI adoption has never been greater. This study provides a foundation upon which such adoption can be built — one that recognises both the extraordinary opportunities and the profound responsibilities that accompany AI-driven workforce transformation.

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