How should non-technical executives evaluate and compare AI model performance benchmarks?

Non-technical executives should evaluate AI model performance benchmarks by prioritizing business impact and real-world applicability over isolated technical metrics like accuracy scores, as current benchmarks often focus narrowly on tasks like coding while overlooking broader job elements such as communication and ethics [5]. Instead, assess how models drive value through factors like revenue growth, customer experience, and ROI, shifting from "what is the model’s accuracy?" to "what changed in the enterprise once this shipped?" [3][7]. Emphasize transparency in benchmarking processes, as rare but valuable practices—such as detailed methodologies and data availability—help identify reliable evaluations that reveal operational flaws beyond single success metrics [2][9]. When comparing models, consider domain-specific reliability, cost of errors (e.g., minimizing false negatives in high-stakes areas), and adoption rates to ensure models are used effectively rather than just capable on paper [4][6][7][10].