Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility

2026-05-07

AI Summary

Generative AI demonstrates strong performance on standard benchmarks yet often fails to deliver tangible utility in real-world domains such as education, healthcare, software engineering, and law, highlighting a misalignment between current evaluation paradigms and practical needs. This work establishes “utility” as the central construct for evaluating generative AI and introduces SCU-GenEval, a four-stage evaluation framework supported by three key tools: a structured deployment protocol, a context-conditioned user simulator, and goal-oriented agent metrics. Integrating stakeholder-objective mapping, construct-metric specification, mechanism modeling, and longitudinal utility measurement—and validated through 28 deployment cases—the study identifies three dominant failure modes in existing evaluations and offers domain-specific, actionable pathways for utility-driven assessment, shifting the focus from model outputs to measurable improvements in human outcomes.

Abstract

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.

Problem

• generative AI
• real-world utility
• evaluation gap
• benchmark limitations
• human outcomes

Innovation

• utility-centered evaluation
• generative AI
• longitudinal measurement
• stakeholder-goal alignment
• SCU-GenEval