Enterprise AI Evaluation and Benchmarking Framework

Enterprise AI systems require evaluation methods that extend beyond accuracy because predictive models, Generative AI, Retrieval-Augmented Generation, and agentic systems exhibit different failure modes and business risks. Organizations often rely on isolated benchmark scores that do not reflect enterprise data, business processes, security constraints, user expectations, or operational cost. This paper proposes an Enterprise AI Evaluation and Benchmarking Framework that combines task quality, factuality, robustness, fairness, privacy, security, explainability, latency, cost, usability, business value, and policy compliance. The framework defines evaluation datasets, golden cases, red-team scenarios, human review, automated judges, statistical confidence, baseline comparison, regression testing, risk-tier-based acceptance criteria, and production monitoring. It introduces benchmark governance, score normalization, composite scorecards, release gates, and continuous evaluation. The proposed approach enables enterprises to compare models and AI solutions consistently, select fit-for-purpose technologies, detect regressions, and maintain defensible evidence of production readiness.