AI Readiness Audit: What to Verify Before Building
A practical framework for finding the first AI use case that has usable data, measurable value, and acceptable operational risk.
Quick brief
Takeaways to carry into your next sprint
- Start from a costly decision or workflow, not from a model.
- Data access and evaluation criteria must exist before prototyping.
- A short discovery phase should end with a build, buy, or stop decision.
An AI initiative becomes expensive when the team starts with a technology and searches for a problem afterward. A readiness audit reverses the order: it maps recurring decisions, bottlenecks, available data, and the cost of being wrong.
The first filter is operational value. A candidate use case should save meaningful time, reduce a measurable error rate, or unlock a service that could not be delivered manually. If the outcome cannot be measured, the prototype cannot produce a useful decision.
The second filter is evidence. We verify where the data lives, who can access it, how representative it is, and whether a human can define correct and incorrect outputs. A model without an evaluation set is a demo, not a production plan.
The third filter is integration risk. Even an accurate model may fail if it adds latency, creates an unreviewable decision, or depends on data that cannot legally leave an existing environment. Architecture and governance belong in discovery, not after it.
A useful audit ends with a ranked opportunity map, a small evaluation plan, and an explicit recommendation: build a bounded proof of value, buy an existing product, improve the data foundation first, or stop. Saying no early is often the highest-return result.