AI roadmaps are often written as lists of use cases. Each use case, however, depends on things outside its own scope: data that is governed and accessible, identities and permissions for the people and agents involved, and integration with the systems where work happens.
When those dependencies are treated as architecture, they can be sequenced and funded once and reused by many use cases. When they are left to each project, every pilot rebuilds them — or skips them.
The practical step is to map the priority use cases against the capabilities they need, and let that map shape the order of investment.