Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility
For AI researchers and practitioners, the paper highlights critical failures in current evaluation practices and offers a framework to align AI assessment with actual deployment success, though it remains a conceptual proposal without empirical validation.
The paper identifies a disconnect between generative AI's benchmark performance and real-world utility across 28 deployment cases, proposing a paradigm shift toward utility evaluation grounded in human outcome trajectories. It introduces SCU-GenEval, a four-stage framework with supporting instruments to measure stakeholder capability improvements over time.
Generative AI systems achieve impressive performance on standard benchmarks yet fail to deliver real-world utility, a disconnect we identify across 28 deployment cases spanning education, healthcare, software engineering, and law. We argue that this benchmark utility gap arises from three recurring failures in evaluation practice: proxy displacement, temporal collapse, and distributional concealment. Motivated by these observations, we argue that generative AI evaluation requires a paradigm shift from static benchmark-centered transparency toward stakeholder, goal, and context-conditioned utility transparency grounded in human outcome trajectories. Existing evaluations primarily characterize properties of model outputs, while deployment success depends on whether interaction with AI improves stakeholders' ability to achieve their goals over time. The missing construct is therefore utility: the change in a stakeholder's capability induced through sustained interaction with an AI system within a deployment context. To operationalize this perspective, we propose SCU-GenEval, a four-stage evaluation framework consisting of stakeholder-goal mapping, construct-indicator specification, mechanism modeling, and longitudinal utility measurement. To make these stages practically deployable, we introduce three supporting instruments: structured deployment protocols, context-conditioned user simulators, and persona- and goal-conditioned proxy metrics. We conclude with domain-specific calls to action, arguing that progress in generative AI must be evaluated through measurable improvements in human outcomes rather than benchmark performance alone.