The pattern is remarkably consistent: fourteen systems that do not speak to each other, four different definitions of revenue, a monthly reporting cycle that takes four days of manual work and is stale on arrival, and a management team that has stopped believing any number a data system produces. Two AI projects have usually been attempted already, and both stalled for the same reason nobody named at the time: the data underneath them was never ready.
We audit the estate before designing anything, build a medallion architecture with a tested transformation layer, agree the definitions that end the four-revenue-numbers problem, and hand the whole thing over with documentation and training. The AI comes after that, and it works, because by then there is something underneath it worth building on.