Predictive People Analytics: Real-Time AI Insights for Engagement and Attrition using Workday Data

For companies that wish to maintain a high level of productivity and a stable level of employees, the topics of employee engagement and attrition remain of significant importance. The conventional HR analytics techniques can be said to be reactive in nature, as they rely on lagging indicators as well as batch-based reporting. The current research has proposed a real-time prediction-based people analytics platform that integrates Workday-based information with multimodal AI models that rely on time series signals, organizational graphs, as well as static tabular-based HR data. The methodology has utilized discrete time survival modeling for the prediction of attrition risk, as well as multi-task learning for improving engagement as well as productivity, along with fairness-based intervention optimization. Experimental assessment across several baselines indicates enhanced predictive capability. The suggested model has AUC = 0.88 (4w) and 0.86 (12w) for attrition, which is better than Logistic Regression (0.72) and XGBoost (0.80). With a 24% drop in RMSE and a R2of 0.72, engagement and productivity prediction was better, showing that behavioural modelling was stronger. The calibration study showed that the Expected Calibration Error was smaller (0.032) and the uncertainty coverage was higher (96.1%). The findings of the intervention uplift showed that changing pay (7.2% uplift) and management check-ins (net benefit score = 0.71) were both cost-effective ways to keep employees. Bias audits showed that differences between men and women, people from different places, and those at different career levels were smaller. These results show that the framework can give quick, accurate, and fair information, which lets firms improve engagement and lower the risk of losing employees.