There is a conversation happening in every enterprise AI post-mortem that nobody wants to be the first to say out loud: the model wasn't the problem. The model did exactly what it was trained to do. It processed what it received, and it produced an output that was only as reliable as what went in. This is not a new concept. Software engineers have lived by "garbage in, garbage out" for decades. But somewhere in the hype cycle of large language models, the enterprise AI indust

Every enterprise AI pilot looks good. This is not a cynical observation — it is a structural reality of how pilots are designed. The data is curated. The scope is narrow. The use case is selected precisely because it is the one where the existing data is cleanest and the model is most likely to perform well. The demo works. The stakeholders are impressed. The project gets approved for production. Then production begins. And something breaks. Over the course of working with en