Abstract
Predicting workplace conflicts before they escalate into formal disputes or collective action represents a persistent challenge in organizational management, one that traditional statistical methods and expert judgment systems address inadequately due to labor relations' complex, multi-factorial nature. We present a deep learning-based early warning system that integrates heterogeneous organizational data-HR records, communication logs, performance trajectories, satisfaction surveys, and external economic indicators-through attention-based multi-modal fusion, explicitly designed to capture both structural vulnerabilities and evolving behavioral dynamics that precede conflict emergence. The architecture combines MLP processing of cross-sectional organizational characteristics with LSTM modeling of temporal patterns, learning to weight modality contributions adaptively rather than applying fixed fusion schemes. Rigorous evaluation across twelve enterprise deployments spanning four industries demonstrates 89.2% prediction accuracy, substantially exceeding modern gradient boosting baselines (XGBoost: 83.4%, LightGBM: 84.1%) and recent tabular deep learning architectures (FT-Transformer: 86.1%), with ablation studies confirming that multi-modal integration contributes 4.5-12.8% performance gains beyond any single data source. Real-world deployment achieved 87.3% early warning success rates with 5-21 day lead times enabling proactive intervention, though 12.7% false positive rates and systematic errors during high-stress operational periods reveal important limitations. We provide detailed deployment statistics, case studies illustrating practical value and failure modes, validation on public benchmarks for reproducibility, and extensive discussion of ethical considerations surrounding workplace prediction algorithms, contributing methodological foundations for organizational risk assessment through multi-source data fusion while identifying clear directions for future enhancement.