Many companies are spending millions on generative AI projects, but most never make it to production. When these projects fail, tech leaders often blame the AI model itself—saying it is too slow, too limited, or not smart enough. But according to data engineers, the real problem is usually the data behind the AI. This is called the 'cleanup trap': the false belief that you can feed messy, inconsistent data into an AI system and fix it later with fancy tools. In a common setup called retrieval-augmented generation (RAG), the system pulls data from databases to give accurate answers. If the source data is full of errors, duplicates, or outdated information, the AI will produce wrong or confusing results. No amount of clever prompts or tuning can fix a broken data pipeline. The solution is to treat data quality as a first step, not an afterthought. Companies should check data for errors as soon as it enters the system, use automated tests to catch changes, and enforce security rules at the data level instead of relying on the AI model to decide who sees what. In short, moving from AI experiments to reliable products requires strong data engineering. The winning company will not just have the best AI model, but the cleanest, most reliable data feeding it.