Every failed AI initiative we've been asked to rescue had the same root cause: nobody validated data readiness before committing to a timeline.
Check for label consistency, historical depth, and access governance before scoping any model work — these gaps are far more common than model architecture problems.
A two-week data audit upfront saves months of rework later, and it's the first thing we do on every AI engagement.