International Journal of Science, Engineering and TechnologyAuthors: Pushpendra Kumar Sharma, Dr.S.Sujatha
Abstract: The fast transition to hybrid work arrangements has dramatically changed employee relations, creating additional challenges for dealing with burnout, engagement, and retention. Survey-based solutions lack objectivity, bias-free data collection, and a forward-looking approach. We have developed a holistic AI solution for analyzing the workforces using various types of passive sensing data collected from collaboration software (Slack, email, Zoom), HR systems, and device usage records to forecast outcomes. The proposed MTL framework incorporates the attention mechanism and TCN to make predictions about three key metrics: burnout (classification task with 92% AUC), engagement (regression, with MAE equal to 0.34), and turnover risk (probability of leaving during the next 6 months with 89% AUC). Applied to 18 months of data from 5,000+ people at a major technology company, the model reveals behavioral features as key indicators: the presence of after-hours activity and the number of meetings show the highest correlations with burnout, whereas engagement is best predicted by peers' interaction diversity.
DOI: https://doi.org/10.5281/zenodo.20233792
