USING ARTIFICIAL INTELLIGENCE FOR PROACTIVE TALENT RETENTION: PREDICTING BURNOUT AND JOB ABANDONMENT

In the post-pandemic workplace, employee turnover and burnout have emerged as critical challenges, driving organizations to seek innovative, proactive retention strategies. Here, the application of Artificial Intelligence (AI) in predicting employee burnout and the likelihood of job abandonment is examined, thus enabling early interventions to enhance talent retention. Recently, AI has become a powerful tool in predictive analysis. This study uses a focus group approach and explores how AI can be used to analyze workplace data to predict and flag employees who are at risk before their turnover intentions solidify. A focus group discussion was held using Human Resource (HR) managers and employees to gather insight on the perceived benefits, concerns and anticipated challenges for implementing an AI-based retention system. It was found that an AI-based system enhanced early burnout detection based on continuous monitoring of stress indicators and patterns of work. This allowed individualistic personalized intervention, such as personal wellness and workload adjustment, to reduce the turnover. The participants highlighted that, in any AI-based retention initiative, ethical considerations, transparency of AI algorithms, data privacy and the need for continuous human oversight were essential. This paper concentrates on group finding, drawing on literature on AI in Human Resource Management (HRM) and Employee Well-Being (EWB). The outcomes stated that the AI’s success will depend on careful, human-centric implementation that addresses trust and ethical issues while offering significant potential to support proactive talent retention by predicting burnout and attrition risks. This study presents a framework for integrating AI into retention strategies. This study also offers practical recommendations for HR practitioners to leverage AI insights and foster EWB and loyalty