# AI Project Readiness Checklist

**Project:** _______________  **Owner:** _______________  **Date:** _______

## 1. Does this need AI?

- [ ] The task involves judgement, language, or pattern-finding — not arithmetic on structured data
- [ ] Rules would need constant updating, or nobody can enumerate them
- [ ] **A deterministic solution has been considered and ruled out, with a reason**
- [ ] Being occasionally wrong is acceptable, with a human able to catch it

🔴 The most expensive AI projects are the ones that should have been a query, a rule, or a form.
If someone can write the rules down, write the rules down — they are cheaper, testable, and they
explain themselves.

**Deterministic alternative considered:** _______________  **Why rejected:** _______________

## 2. The problem

- [ ] Stated in one sentence, in the user's words
- [ ] The person who has this problem today has been asked about it
- [ ] What they do instead today is documented, with how long it takes
- [ ] The value of solving it is estimated — time, cost, or risk avoided
- [ ] Failure is survivable, or a human reviews before anything acts

**What happens when the system is confidently wrong?** _______________

## 3. Data

- [ ] The knowledge needed **exists in writing**, not only in people's heads
- [ ] Someone owns each source and is accountable for it being current
- [ ] Access is legally and contractually permitted for this use
- [ ] Personal data identified, with a lawful basis
- [ ] Volume and quality checked by looking at it, not by asking about it

If the answer lives in people's heads, this is a documentation project wearing an AI project's
clothes. Do that first.

## 4. Measurement

- [ ] Success is defined as a number, before building
- [ ] A baseline exists — what does the current process achieve?
- [ ] **Test set written from real cases, with known answers**
- [ ] Someone has decided what "good enough to ship" means
- [ ] Cost per operation will be measured
- [ ] Latency requirement stated

| Metric | Baseline | Target |
|---|---|---|
| | | |

Without a baseline you cannot demonstrate benefit, and the project will be judged on impressions.

## 5. Accountability

- [ ] A named person owns the output, not a team
- [ ] It is clear who is responsible when it is wrong
- [ ] Users are told they are interacting with AI where required
- [ ] Generated content is labelled where required
- [ ] There is a route to a human
- [ ] Decisions with legal or significant effect on people have a human in the loop

Automation does not transfer accountability. Whoever owned the outcome before still owns it.

## 6. Operations

- [ ] Where it runs, and who is paged when it stops
- [ ] Cost ceiling set and enforced
- [ ] Model and version pinned
- [ ] Behaviour when the model provider is unavailable
- [ ] Prompts and configuration in version control
- [ ] Bad outputs can be reported, and the report reaches someone

## 7. Before go-live

- [ ] Measured against the test set, result recorded
- [ ] Tested with real users on real work
- [ ] Failure paths exercised deliberately
- [ ] Cost per operation known
- [ ] Rollback or disable path tested
- [ ] Documentation says what it cannot do

**What this system must never be used for:** _______________

## Sign-off

| | Name | Date |
|---|---|---|
| Built by | | |
| Business owner | | |
| Approved for users | | |