Your AI system answered 10,000 employee questions this week. How many of those answers were actually right? This is one of the biggest gaps I see in enterprise AI conversations. Everyone wants to talk about how many questions the system answered, how many workflows it touched, or how much time it saved. Those numbers matter, but they only tell one part of the story. The more important question is whether the system is being evaluated consistently enough for people to trust it. One lesson that stayed with me from my years at IBM is that evaluation cannot be treated like an add-on. With generative AI, you are evaluating how the entire system behaves in the real world, across different users, workflows, edge cases, and business contexts. That means looking at things like: • Which answers were accurate • Which ones needed escalation • Where the system failed repeatedly • Whether users trusted the response • Whether the business outcome actually improved IBM’s Client Zero is a strong e...