Enterprise AI's core commercial problem is architectural, not circumstantial. The sales motion that built the industry — polished demos on curated data, controlled evaluations, vendor-managed pilots — is precisely calibrated to obscure the conditions under which these systems fail. Real operational environments have messy, inconsistent, undocumented data. Language models trained on structured corpora degrade against it. The healthcare practitioner whose AI system hallucinated insurance codes on live patient data — they encountered the standard production environment that demos are designed to avoid.