Your Data Isn't AI-Ready. Here's the Honest Checklist.
Eleven questions a board should ask before approving an AI initiative, and the four that usually kill it.
Almost every AI business case we review assumes a data estate that does not exist yet. The pilot works on an extract. Production needs the system of record.
The eleven questions are unglamorous: do you have a single definition of customer, is entitlement data reconciled with billing, how stale is the warehouse at the moment of decision, who owns correction, what is the retention position, can you trace a model output back to a source row, and so on.
Four of them usually end the conversation: lineage, ownership, freshness at point of use, and the legal basis for the data you plan to train or ground on. None of these are model problems, and none get better by choosing a different vendor.
The honest path is to sequence: fix the two datasets that carry the highest-value decisions, prove the workflow on those, then widen. That takes a quarter longer at the start and saves a year at the end.