Accuracy is a lab metric. Production needs better. I've shipped enough AI systems to know this truth: a model can be 98% accurate in testing and still fail spectacularly in production. Why? Because accuracy doesn't capture reliability, explainability, or business impact. 📊 The metric I actually trust? Time to resolution for errors. How fast can the system detect when it's wrong, route to human oversight, and learn from the correction? That tells me everything about operation