AI IN HRM SYSTEMS: A STUDY OF EFFICACY IN OPERATIONAL EFFICIENCY, EMPLOYEE RETENTION, AND BIAS MANAGEMENT IN SAUDI ENTERPRISES

Abstract

Human Resource Management (HRM) has progressed through an out-of-date managerial function to a planned driver of organizational performance, leveraging data-driven insights for workforce organization. The incorporation of Artificial Intelligence (AI) in HRM symbolizes a main development in organizational practices, refining employee efficiency, engagement score, and long-term retention. Digital transformation and workforce localization are national priorities in Saudi Arabia, implementing AI-based HR solutions helps to improve operational effectiveness and guide tactical decision-making (DM). This investigation evaluates the impact of AI integration on employment outcomes. Records of hiring, performance reviews, and feedback from workers have been obtained from Saudi firms in the IT, finance, and service industries during a two-year period. Using IBM SPSS 28, statistical approaches incorporated multiple regression analysis (MRA), descriptive statistics (DS), and Pearson correlation analysis (PCA) to analyze the connections between employee retention rates (ERR), HR Operational Efficiency (HROE), candidate-job alignment (CJA), recruitment cycle time (RCT), employee satisfaction (ES), training effectiveness (TE), and recruitment duration. DS showed high ES (4.1), HROE (72.5%), ERR (85.3%), EES (4.0), and CJA (78.4%),. PCA revealed strong correlations (r = -0.56 to 0.72, p < 0.01), and MRA confirmed that all predictors significantly enhanced employee retention (p = 0.001). All things considered, AI-enabled HRM solutions greatly increase operational effectiveness and retention results, bolstering well-informed HR strategies and emphasizing the vital role that ethical oversight plays in Saudi corporate operations.