The AI Validation Trap: Why Adding AI Isn’t the Same as Solving a Problem
A team spends three months building an AI feature. The demo goes well. Clients say nice things. Someone posts about it on LinkedIn. Six months later, usage has quietly flattened, nobody can say what the tool actually fixed, and the only measurable change is the line item for hosting and API calls. Nothing broke. The model didn’t hallucinate its way into a scandal. The mistake happened earlier, at the moment someone asked “should we use AI here?” instead of “what, specifically, is broken here?”