Why AI Systems Fail At Scale And What You Should Measure Instead Of Model Accuracy
CIO, Wednesday, April 15th, 2026
A model can be 95% accurate and still be a disaster if it's too slow or drifts. Don't just watch the model - watch the plumbing, the data loops and the blast radius.
A few years ago, I was part of a team rolling out an AI capability into a large enterprise environment. The model itself looked great in testing, accuracy was above 95%, the evaluation metrics were strong and everyone involved felt confident about the rollout. But within a few weeks of deployment, things started behaving in ways we hadn't expected.