Why AI systems fail at scale and what you should measure instead of model accuracy

The document warns that model accuracy metrics are insufficient for production AI systems because surrounding infrastructure (pipelines, APIs, latency, and load) can silently degrade performance; it recommends CIOs monitor operational signals like behavior under real load, reliability, and timeliness of predictions to detect and mitigate such failures.