A healthcare AI company claimed its error rate was less than 1 in 100,000. Then the Texas Attorney General came knocking. Here's what happened → The company built software that drafted clinical notes and summarized patient charts across four major hospitals. They marketed an almost impossibly low hallucination rate to win trust. But when regulators looked under the hood, they found the metrics couldn't be properly verified. The result was a first-of-its-kind enforcement settlement that now requires five years of mandatory transparency about how accuracy numbers are defined, calculated, and disclosed. This matters way beyond healthcare. The real lesson isn't about one company getting caught. It's about a pattern we see everywhere in enterprise AI right now → teams bolting a thin software layer on top of a general-purpose model, then marketing precision they can't actually prove. Our research keeps pointing to the same gap. Only about 5% of companies are getting real, measurable business value from AI at scale. The ones succeeding aren't chasing impressive-sounding benchmarks. They're investing in data quality, domain-specific validation, and genuine human oversight before anything reaches a customer or a patient. The era of "trust our accuracy number" is ending. The era of "show your work" is here. Honest question for anyone working with AI tools in their organization → if your vendor had to publicly disclose exactly how they measure accuracy, would you feel more confident or less? #EnterpriseAI #AIGovernance #AIAccountability