Most enterprise AI never reaches production, and it usually isn't the model's fault. It's the foundation underneath. This free assessment asks five honest questions and scores your exposure to the five reasons projects stall. It takes about two minutes, and you see your result immediately, with no email required.

How the assessment is scored

The assessment is deterministic and transparent. Each question maps to one readiness dimension and scores from 0 to 4, for a total out of 20. It measures exposure to known failure modes, not a proprietary "maturity index." Here is the full rubric.

1. Data foundation: Where does your operational data actually live?

4 · One governed warehouse or lake we trust
3 · A few systems, mostly reconciled
2 · Several tools, stitched together by hand
1 · Scattered across many tools, with no clear owner
0 · Honestly, we're not sure

2. Production track record: The last AI or analytics project you tried, where is it now?

4 · Running in production, used daily
3 · Live, but adoption is uneven
2 · Built, but it stalled before real use
1 · A demo or pilot that never shipped
0 · We haven't really tried yet

3. Failure honesty: When a model or a metric is wrong, how do you find out?

4 · We adversarially review our own output and have killed work that failed
3 · We spot-check and usually catch the big errors
2 · We find out when someone downstream complains
1 · We mostly trust the vendor's number
0 · We wouldn't really know

4. Ownership: Who owns the code and infrastructure behind your AI work?

4 · We do, on our own cloud
3 · Mostly us, with some managed pieces
2 · A mix we don't fully control
1 · A vendor platform we rent
0 · We don't have any yet

5. Use-case clarity: Can you name one decision this quarter that better data would change?

4 · Yes, a specific, measurable, owned decision
3 · Yes, roughly
2 · A few ideas, nothing sharp
1 · Not really
0 · No

What your score means

15 to 20 Ready to build. Your foundation is there. The gap between you and production AI is execution, not readiness, and that is exactly where an embedded build pays off fastest.
8 to 14 Fix the foundation first. You have real gaps that would sink an AI project if you built on top of them now. The good news is they're fixable, and fixing them is most of the work: organize and secure the data, then predict on it.
0 to 7 Not yet, and that's the honest answer. Building AI on this foundation now would most likely join the 95% of pilots that return nothing. That isn't a no; it's an order of operations. The highest-value work right now is making the data real and trusted.

Are you actually ready to build AI?