AI-Powered Human Resource Management for Enhancing Employee Recruitment Efficiency and Talent Retention in Organizations - Chheda, Kajal (Management (Montevideo), 2025)
Artificial Intelligence (AI)-powered Human Resource Management (HRM) systems address inefficiencies in recruitment and employee retention. Traditional methods are slow, biased, and reactive. Integrating AI enables predictive insights, automated screening, and employee satisfaction monitoring, transforming HR practices into data-driven, strategic decision-making processes. This research aims to evaluate the impact of AI on improving recruitment efficiency and talent retention. It investigates whether AI-based tools significantly reduce hiring time, enhance job candidate fit, and predict attrition risk. Data was sourced from 1,000 anonymzed employee records, including 400 resumes, 280 satisfaction responses, and 320 attrition cases across the IT and finance sectors. Collected over a three-year period, the dataset supports recruitment analysis and employee retention prediction using AI-based models. Five variables were analyzed: recruitment time (RT), candidate-job match score (CJMS), employee satisfaction score (ESS), retention rate (RR), and AI-predicted attrition risk (APAR). These variables represent both continuous and ordinal data types, suitable for independent sample t-tests and regression analysis in SPSS 25. SPSS analysis showed significant reductions in recruitment time (p < 0,01) and improvements in job match scores. Among independent sample t-test results, the highest t-value was observed for CJMS (t = 22,15, p < 0,001). Spearman’s correlation indicated a strong positive link between satisfaction and retention. Regression analysis confirmed high predictive accuracy of AI-based attrition risk models. In regression findings, APAR had the highest R² value (R² = 0,42, p < 0,001). AI-powered HR systems significantly enhance recruitment efficiency and retention strategies. Statistical evidence confirms the effectiveness of AI in predicting attrition and improving candidate-job alignment, enabling organizations to make proactive, data-informed HR decisions and foster a more stable workforce.
ISME
Keywords: Artificial Intelligence (AI); Talent Retention; Human Resource Management (HRM); Employee Satisfaction