The modern customer has just one need that matters: Getting the thing they want when they want it. The old standard RAG model embed+retrieve+LLM misunderstands intent, overloads context and misses freshness, repeatedly sending customers down the wrong paths. Instead, intent-first architecture uses a lightweight language model to parse the query for intent and context, before delivering to the most relevant content sources (documents, APIs, people). Enterprise AI is a speeding train headed for a cliff. Organizations are deploying LLM-powered search applications at a record pace, while a fundamental architectural issue is setting most up for failure. A recent Coveo study revealed that 72% of enterprise search queries fail to deliver meaningful results on the first attempt, while Gartner also predicts that the majority of conversational AI deployments have been falling short of enterprise expectations. The problem isn’t the underlying models. It’s the architecture around them. After designing and running live AI-driven customer interaction platforms at scale, serving millions of customer and citizen users at some of the world’s largest telecommunications and healthcare organizations, I’ve come …