AI Performance Degradation Over Time: Causes, Measurement, and Systemic Mitigation

Abstract

Artificial intelligence systems deployed in real en vironments exhibit a gradual erosion of predictive reliability, robustness, and operational relevance. This phenomenon, referred to as performance degradation, emerges from data distribution shifts, evolving user behavior, infrastructural drift, and feedback loops introduced by model usage itself. Unlike classical software decay, degradation in learning systems is often silent and cumulative. This paper develops a unified analytical framework for understanding AI performance degradation across technical, organizational, and socio technical dimensions. We introduce formal degradation metrics, longitudinal evaluation strategies, and system level mitigation architectures. Empirical results using simulated and real world inspired datasets demonstrate how unmanaged degradation leads to compounding risk, while governance aware adaptive pipelines sustain long term model value.