AI Ship Loop Checklist
Most AI prototypes stall because the builder keeps adding features instead of closing the gaps that matter. This checklist is the shortest path from “it works on my machine” to “someone uses it and pays for it.”
Work through each step in order. If you get stuck, that step is your bottleneck — fix it before moving on.
Score the prototype
Run the AI Ship Score diagnostic to get a baseline across all four readiness dimensions. Be honest — the value is in the gaps it reveals, not a high number.
Check: Do you know your current prototype, deployment, adoption and commercial readiness scores?
Identify the weakest readiness area
Look at your four sub-scores. The lowest one is the bottleneck. A prototype that scores 80 on functionality but 10 on deployment is not close to shipping — it is close to being forgotten.
Check: Which readiness dimension scored lowest, and do you understand why?
Simplify scope
Cut everything that is not needed for one user to complete one task successfully. Features you remove now can be added later. Features that delay shipping are liabilities.
Check: Can you describe the single core task your tool helps one user complete?
Deploy
Get it on a URL that someone else can open. It does not need to be polished. It needs to be accessible. A staging link counts. A localhost does not.
Check: Can someone outside your machine open it in a browser right now?
Test with real users
Give it to one real person in the target audience and watch them use it. Do not explain how it works first. If they cannot figure it out, the onboarding is broken.
Check: Has at least one person outside the build team used it without guidance?
Measure adoption
Add basic analytics or logging. You need to know whether people come back, where they drop off, and whether the core task gets completed. Without data, you are guessing.
Check: Do you know how many people have used it, and whether they completed the core task?
Run commercial experiments
Test whether anyone would pay, approve, or adopt this. That might mean a price on a landing page, a proposal to a client, an internal pitch, or a conversation with someone who has the problem.
Check: Have you tested whether someone would pay for this, approve its use, or adopt it?
Improve, monetise, or kill
Based on what you learned: improve the weakest area, start charging or pitching, or let it go. The worst outcome is not killing a project — it is leaving it half-alive indefinitely.
Check: Have you made a clear decision: improve, monetise, or kill?
After the checklist
If you made it through all eight steps, you have either shipped something real or made a clear decision to stop. Both are better than the default outcome — an abandoned prototype that nobody uses.
If you want an outside perspective on where your build stands, run the free AI Ship Score diagnostic or request a human review.