Authors: Manoj Parasa
Abstract: Exit interviews are an underutilized but critical tool for capturing organizational feedback, yet traditional analysis methods often fail to generate meaningful insights. This study investigates the application of artificial intelligence—specifically natural language processing, sentiment analysis, and topic modeling—to interpret qualitative exit interview data within SAP SuccessFactors. Using a mixed-methods design and data extracted from a large multinational enterprise over an 18-month period, the research reveals latent patterns in attrition reasons, identifies hidden organizational issues, and proposes actionable insights for HR leadership. Results demonstrate that AI-enhanced exit analytics uncover unstructured feedback trends more reliably than manual reviews, with significantly higher accuracy in detecting dissatisfaction themes. This paper contributes to social science research by positioning exit interviews as institutional diagnostic tools, offering a predictive lens into workforce behavior. The study concludes by recommending an integrative model for AI-powered offboarding intelligence that can be replicated across enterprise HR platforms.
DOI: https://doi.org/10.5281/zenodo.15718618
