AI pilot to production checklist

An AI pilot is ready for production when it passes seven gates: a measured quality bar on an eval set built from real tasks, approved and permissioned data access, a security review covering prompt injection and data leakage, a cost and latency budget, monitoring and tracing, a staged rollout plan with a fallback, and a named owner after launch. Each gate needs evidence, not a demo. The checklist below lists what each gate requires, who owns it and what to do when a pilot fails one.

AI pilot to production checklist

1. The seven production gates

Gate 1: Quality is measured, not demonstrated

• ☐ Eval set of real tasks from the target users, with expected outputs or grading criteria
• ☐ Baseline: how the task is done today, with its accuracy and time
• ☐ Quality bar agreed with the business owner before the final run
• ☐ Automated evals run on every prompt, model or retrieval change
• ☐ Known failure modes documented, with how the feature behaves when it fails

Gate 2: Data access is approved and permissioned

• ☐ Every data source listed, with its owner's approval
• ☐ Retrieval respects the same permissions users have in the source systems
• ☐ Data freshness rules defined (how stale can retrieved content be)
• ☐ Personal and sensitive data handling reviewed against your policies
• ☐ Data retention for prompts, outputs and logs defined

Gate 3: Security review passed

• ☐ Prompt injection tested, including through retrieved documents and tool outputs
• ☐ Tools and actions scoped: what the system can read, write and change
• ☐ Actions with side effects require confirmation or human approval
• ☐ Tenant isolation verified for multi-customer products
• ☐ Secrets and service accounts follow your standard practices

Gate 4: Cost and latency fit the budget

• ☐ Cost per request and per user measured on realistic traffic
• ☐ Monthly cost forecast at expected adoption, approved by the budget owner
• ☐ Latency measured at p50 and p95 and acceptable to users
• ☐ Rate limits and provider quotas checked for peak traffic

Gate 5: Production observability is in place

• ☐ Tracing of prompts, retrieved context, tool calls, outputs, latency and tokens
• ☐ Quality monitoring on live traffic (sampled review or automated graders)
• ☐ Alerts for error rates, latency, cost spikes and provider outages
• ☐ User feedback captured in the product and reviewed weekly

Gate 6: Rollout plan and fallback

• ☐ Staged rollout: internal users, then a pilot group, then everyone
• ☐ Feature flag or kill switch to turn the AI path off without a deploy
• ☐ Fallback when the model or provider is unavailable
• ☐ Plan for model version changes from the provider

Gate 7: Ownership after launch

• ☐ Named engineering owner and on-call path
• ☐ Named business owner who tracks adoption and value
• ☐ Runbook for incidents, quality regressions and provider changes
• ☐ Review date set to decide whether to expand, improve or retire the feature

2. Gate review sheet

How to use this checklist

1. Share the seven gates with the pilot team before the pilot ends, so they collect evidence as they build instead of at the end.
2. Name an owner for each gate in the review sheet. Owners decide pass or fail for their gate.
3. Agree the quality bar for gate 1 with the business owner before the final eval run. A bar set after seeing results is not a bar.
4. Hold one review meeting with all gate owners. Go through the sheet, link the evidence and record pass, fail or waived for each gate.
5. For any failed gate, write the action and date. Waivers need the accountable executive's name and a reason.
6. Roll out in stages and keep the kill switch ready. Re-run gate 1 and gate 4 on real traffic after the first stage.

Example: a gate review for a support answer-drafting feature

The numbers above illustrate the format. Use your own eval size and bar.

Common mistakes that stall AI pilots before production

• The pilot used clean sample data. Production data is messier, older and permissioned. Run the pilot on real data with real access rules.
• No eval set. Without one, every release is a debate about anecdotes.
• Nobody owns it after the pilot team leaves. A pilot built by a separate innovation team often has no production owner.
• Security review starts at the end. Bring security in at the start so data access and tool scopes are designed in, not bolted on.
• Cost was never measured. A feature that is cheap for ten pilot users can be expensive for ten thousand.
• The pilot proved the model, not the workflow. Users adopt features that fit their existing tools and steps.

Need engineers who take pilots to production?

Ryz AI pod teams are dedicated pods of senior engineers that build AI systems in your cloud and repos and ship them to production alongside your team, with evals, tracing and runbooks in your repos from the start. Our pods have shipped production AI for fraud detection, marketing compliance review, voice platforms and driver support; see the case studies. Only the top 1% of the tens of thousands of engineers we interview make it, and they work US business hours including New York hours. Talk to us to scope your pod, or compare partners to take an AI pilot to production.

Related

• AI POC success criteria template
• LLM evaluation rubric template
• AI team structure template
• AI pod teams
• Hire LLM evaluation engineers
• AI development cost calculator

FAQ

How do you move an AI pilot to production?

Pass seven gates with evidence: measured quality on a real eval set, approved and permissioned data access, a security review, a cost and latency budget, production observability, a staged rollout with a fallback, and named owners after launch. Fix failed gates before rollout rather than after.

Why do AI pilots fail to reach production?

Common causes are pilots built on clean sample data, no eval set to prove quality, security and data access reviews that start too late, unmeasured cost at scale, and no team that owns the system once the pilot team moves on.

What should an AI production readiness review include?

Evidence for each gate: the eval report against an agreed bar, data access approvals, security test results including prompt injection, a cost model at expected adoption, monitoring dashboards and alerts, the rollout plan with a kill switch, and the runbook with named owners.

How long does it take to move an AI pilot to production?

It depends on how much evidence the pilot already has. A pilot built on real data with an eval set and early security input can move quickly. One built on sample data often needs most of its data and evaluation work redone.

Who should approve an AI feature for production?

Each gate owner approves their gate: the AI tech lead for quality, data owners for access, security for the security review, the budget owner for cost and the engineering owner for operations. One accountable executive signs off on the overall launch and on any waivers.

Questions we didn't answer? Email info@ryzlabs.com.