AI-Powered Human Resource Management for Enhancing Employee Recruitment Efficiency and Talent Retention in Organizations

Kajal Chheda, Urvashi Thakur, Rani J, Ganesh Prasad Das, Mukesh Kumar Parashar and Krishna Reddy

Management (Montevideo), 2025, vol. 3, 165

Abstract: 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

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:manage:v:3:y:2025:i::p:165:id:1062486agma2025165

DOI: 10.62486/agma2025165

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