Most AI projects are structured as technology projects. A team of data scientists or ML engineers, working on a model, optimising for technical performance metrics. The model improves, accuracy goes up, and the demo gets more impressive. And the production deployment keeps not happening because nobody owns the gap: The gap between what the model does and what the business process actually requires. The gap between the data the model was built on and the data that actually flows through the business. The gap between the technical output and the operational decision it's supposed to inform. The gap between the people who built it and the people who are supposed to use it. AI projects don't get stuck in pilot purgatory because the AI is hard. They get stuck because the integration between the AI and the business is harder than the AI itself — and most project structures treat that integration as an afterthought. The data scientists are optimising for model performance. The vendor is optimising for contract renewal. The business is waiting for something to change. And nobody is sitting in the gap, owning the problem of making the model actually useful inside a real operational context.