The most expensive AI projects fail for an unglamorous reason: the data underneath them was never ready. Models amplify whatever foundation you give them — including the gaps.
Readiness is mostly about data
Before any model adds value, you need data that is collected consistently, owned by someone, and trusted by the people who use it. Most "AI problems" are really data-engineering problems wearing a more exciting label.
A practical readiness checklist
- Is your core business data captured in one reliable place?
- Does each important metric have a clear, agreed definition?
- Is there a decision your team would actually change based on output?
If you can answer yes to those three, you are ready to pilot. If not, the highest-ROI "AI" work is fixing the foundation first — and that work pays off whether or not you ever ship a model.