// Resources / AI-readiness checklist
Most AI projects stall on foundations, not models. Work through the checklist below to see where you stand — and where a single sprint would close the gap. Nothing is stored; it is yours to think with.
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// The checklist
0 of 20 checked
// Reading your result
There is no pass or fail — the pattern of what is unchecked tells you where to start.
Mostly checked
The foundations are in place. The fastest path to value is scoping a first feature and shipping it in a sprint — the readiness work is largely behind you.
About half
You can begin, but a sprint zero to close the open items — data access, a clear use case, or an owner — will de-risk everything that follows.
Mostly unchecked
The highest-leverage work is groundwork: get data queryable, name an owner, and pick one concrete use case before building AI features on top.
// Answers
It means you can put an AI feature in front of users and trust the result — the data underneath is queryable, the use case is specific and measurable, the team can ship and review in a weekly cadence, and the security questions are answered. It is mostly about foundations, not models.
No. Most teams start with data that grew organically. What matters is that it is reachable and that someone can explain where the numbers come from — cleanup and validation can be built into the work. Waiting for "perfect" is how AI projects stall before they begin.
That is useful, not discouraging — it tells you the first sprint should be foundations, not features. A sprint zero focused on data access, a single scoped use case, and a named owner turns a low score into a green light within weeks.
Yes. Closing these gaps — wiring up data, scoping a use case, standing up the toolchain on Google Cloud Platform or AWS — is exactly the kind of work a fixed-scope sprint zero is built for. From there it folds straight into the 7-day cadence.
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