Public AI benchmarks are becoming more realistic by testing end-to-end workflows and tool use, but they remain insufficient for production deployment decisions due to task specificity, potential optimization bias, and gaps between test and real-world conditions. Organizations should use benchmarks to build candidate shortlists and identify what to evaluate further, then conduct production-like testing of the full system including model, tools, reliability, and business outcomes before shipping.