Background: The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) practices represents a significant transformation in how organisations recruit, develop, and manage human capital. However, AI-HRM adoption is uneven and fraught with ethical, legal, and workforce management challenges.

Objective: This study investigates the key enablers and barriers of AI-HRM adoption, examines adoption levels across industry sectors and organisation sizes, and identifies the perceived organisational outcomes of AI-HRM implementation.

Method: A quantitative survey was conducted with 280 HR professionals and line managers across manufacturing, financial services, information technology, and healthcare sectors, selected through purposive sampling. Data were analysed using descriptive statistics and one-way ANOVA in SPSS v29. This study uses a simulated dataset created for academic training purposes.

Key Results: Operational efficiency gains (M = 4.31) and data-driven decision-making (M = 4.18) were the highest-ranked enablers. Algorithmic bias and fairness concerns (M = 4.09) and data privacy risks (M = 3.98) constituted the most significant barriers. AI-HRM adoption was highest in the IT sector (58% high adoption) and lowest in healthcare (18%). Large organisations demonstrated significantly higher adoption rates than small firms.

Conclusion: AI offers transformative potential for HRM but requires robust ethical governance frameworks, AI literacy investment, and context-sensitive implementation strategies to realise its benefits equitably across sector and size contexts.