Do enterprise AI projects actually fail because the AI isn't good enough?

Lately I have started wondering if we blame the model too much. You can have a genuinely good model and still end up with a terrible AI product. The model is rarely where things break. The data is messy. Two systems call the same thing by different names. Nobody quite knows which number is the right one. Half the context that matters lives in someone's head, undocumented. And then we expect an agent to walk into all of that and make a confident decision. I have watched teams spend months carefully evaluating models, when the real problem was everything sitting behind the model. Here is the part I find interesting. Once you fix the data and the context underneath, the AI part often becomes the easy bit. It gets simpler, faster, and a lot more reliable, almost like it was waiting for a clean foundation all along. So I am genuinely curious. When an enterprise AI project stalls, what have you seen as the real reason?