AI Pilot-to-Production Checklist

Why the demo isn't the finish line

A demo is built by the people most excited about it, on clean data, judged on its best examples, with nobody paying for it at scale. Production removes every one of those conditions at once.

None of the items below are about the model. They are about ownership, operations and the people who have to live with the result. That is where AI work usually stalls, and it's the same place any new product stalls. It just gets there faster.

Tick what is true today. An empty box isn't a failure. It's the work that's left.

Ownership

A named owner for the live product, not only the pilot.
Why it matters: Pilots are owned by enthusiasm. A live product needs someone accountable when it gets something wrong.
A clear answer to who can turn it off, and how fast.
Why it matters: When an AI feature misbehaves in front of customers, finding that person can't take a day.
Budget and people for after launch.
Why it matters: Models, data and vendor terms keep changing, and someone has to keep up with them.

Quality and evaluation

A written definition of a good answer, agreed with the business.
Why it matters: In a demo, quality is judged by the person who built it.
A test set of real cases, including the awkward ones, checked before every change.
Why it matters: A demo shows the best examples. Production shows all of them.
Someone regularly reading samples of live output.
Why it matters: AI quality drifts without throwing errors, so dashboards alone stay green while answers get worse.

Data

Access to production data, approved through the normal process.
Why it matters: Many pilots run on a clean export that the live system will never get.
Confirmation that you may use this data for this purpose.
Why it matters: Privacy rules and contracts often limit reuse, even of data you already hold.
A plan for keeping the data current.
Why it matters: An answer is only as fresh as the data behind it.

Cost

Cost per task estimated at real volume, not pilot volume.
Why it matters: Small pilots hide costs that grow with every user.
A budget owner and an alert before spend runs over.
Why it matters: Usage-based pricing grows with success, and surprise invoices end good projects.
A comparison with how the job gets done today.
Why it matters: A cheaper answer only counts if the quality holds.

Security

A security review that covers AI-specific risks, not only the standard app checklist.
Why it matters: Risks like prompt injection and data leaking through answers aren't on most standard reviews. The OWASP Top 10 for LLM Applications is a good public reference for what to ask.
The smallest set of permissions for anything the AI can act on.
Why it matters: An assistant that can send, change or pay deserves the same limits as a new employee.
Vendor terms checked for data retention and training use.
Why it matters: What the vendor keeps, and for how long, is part of your risk.

Change management

The people whose work changes were involved before launch.
Why it matters: Adoption fails quietly. People route around tools they didn't ask for.
Clear guidance on when to trust the output and when to check it.
Why it matters: Blind trust and blanket distrust are both expensive.
Training on the new way of working, not only on the tool.
Why it matters: The tool is the easy part. The changed process is what people struggle with.

Support

A support team that can explain what the feature does and why it answered the way it did.
Why it matters: The first complaint goes to them, not to the team that built it.
A way for users to flag a bad answer, and someone who reads those flags.
Why it matters: Users find failure cases faster than any test set.
A fallback for when the model or the vendor is down.
Why it matters: An outage you didn't plan for becomes your outage.

Reading your result

If most boxes in a section are empty, that section is where the pilot will stall. Expect it to be ownership, cost or change management more often than the technology. Those sections need decisions from people outside the build team, and those decisions take the longest.

This list is not exhaustive and it is not a launch approval. It names what usually breaks so the right people are in the room early.

What this checklist deliberately isn't

A step-by-step build guide
A tool or vendor recommendation
A legal or compliance review. If you're in a regulated industry or sell into the EU, you still need your own advisers
A guarantee. Ticking every box lowers the risk, it doesn't remove it

Nothing you tick is sent anywhere

Your ticks stay on your screen. They are not sent or stored. The page counts visits and button clicks, nothing else. The analytics are cookieless and respect Do Not Track.

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Also useful: the AI readiness scorecard, for a team-wide view before the next pilot starts.

Pilot-to-Production Checklist