AI-Enabled Employee Engagement Monitoring System for Workforce Retention Enhancement

Abstract

Engaging employees is a key element that can affect productivity and employee retention. This study suggests an AI-based employee engagement monitoring program that can help reduce staff turnover by offering immediate feedback on employee engagement. The methodology incorporates Min-Max Normalization to normalize the different HR and behavioural data types, Recursive Feature Elimination to highlight engagement drivers that have the most significant impact, and a Transformer-based classifier to forecast engagement trends and retention risks. The system, which was implemented in Python with the help of PyTorch and the Hugging Face Transformers library, effectively processes multi-modal, structured and unstructured data, such as performance metrics, collaborative patterns and sentiment feedback. The results from the experiments confirm the high accuracy of the prediction, allowing proactive identification of employees at risk and HR interventions. The proposed framework provides better interpretability, scalability and actionable insights, compared to traditional survey-based approaches. This AI-based system is a data-focused solution that enables organizations to monitor employee engagement on an ongoing basis, helping them to optimize their workforce and enhance their effectiveness.