Evaluating AI-Driven Decision Support Systems in Operational Environments

Abstract

AI-driven decision support systems are increasingly deployed in operational environments where decisions must be made under time pressure, uncertainty, and resource constraints. While algorithmic accuracy has improved substantially, far less attention has been given to how these systems perform and behave when embedded within real operational workflows. This paper presents a comprehensive evaluation approach for AI-driven decision support systems that examines analytical performance, decision quality, system reliability, and human interaction under operational conditions. The proposed evaluation framework integrates quantitative performance metrics with stability, consistency, and governance indicators to capture the full operational impact of AI-driven decision support. Results from controlled operational scenarios demonstrate that system effectiveness depends as much on decision coherence and trust as on predictive accuracy.