Development of an AI-Enabled Cognitive Workforce Intelligence and Management Platform for Employee Burnout Detection, Performance Evaluation, and Organizational Cost Impact Analysis
DOI:
https://doi.org/10.15662/IJEETR.2026.0802052Keywords:
employee burnout prediction, workforce intelligence, organizational cost analysis, machine learning, AI-driven decision support, ROWI, cognitive computing.Abstract
Workplace burnout constitutes a growing occupational crisis with profound individual, organizational, and financial consequences. Current HR management systems address burnout detection, performance evaluation, and cost analysis as isolated concerns. This paper presents the Cognitive Workforce Intelligence and Management Platform (CWIMP), an AI-enabled system integrating machine-learning-based burnout risk prediction, real-time performance evaluation, and organizational cost impact analysis within a unified six-module architecture. A stacked ensemble learning model combining XGBoost, SVM, and LSTM achieves burnout prediction accuracy of 91.7% and AUC-ROC of 0.96. Performance evaluation yields MAE of 0.14 (54.8% reduction over baseline), cost estimation achieves MAPE of 7.4%, and the ROWI simulation achieves 87.3% accuracy projecting a median 4.2× return per intervention dollar. Validation on a synthetic 1,247-record enterprise dataset confirms the platform's viability as a proactive, scalable workforce management solution
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