Rethinking AI Evaluation: From Isolated Tests to Real-World Context

AI benchmarking has historically relied on isolated task-level comparisons between machines and humans, creating a misleading picture of real-world performance. This disconnect leads organizations to misjudge AI capabilities and overlook systemic risks, as demonstrated by FDA-approved radiology AI that excels in benchmarks yet slows hospital workflows. The proposed HAIC (Human-AI, Context-Specific Evaluation) framework shifts focus to assessing AI performance within genuine organizational settings, offering a more accurate foundation for deployment decisions.