| name | ai-graduation |
| version | 1.0.0 |
| description | 7-move decision gate for AI deployment. Prevents early promise from becoming unquestioned infrastructure. Based on Stuart Winter-Tear's AI Graduation Path. Activates on "AI deployment", "graduation", "should we scale this AI", "pilot to production", "AI governance", "prove this works", or "when is this ready?"
|
| user-invocable | false |
The Principle
A pilot does not earn authority because it can be repeated. It earns authority when the organisation understands the conditions under which it holds and the conditions under which it breaks.
The 7 Moves
Walk through each move as a decision gate. The user must demonstrate readiness at each gate before advancing. Skipping gates is the primary failure mode.
Move 1: Contain
Gate question: "Is this AI application running in a bounded, observable environment?"
Requirements:
- Defined scope (what it does, what it doesn't)
- Clear boundary conditions (when does it apply, when doesn't it)
- Human in the loop for all consequential outputs
- No access to systems it shouldn't touch
If the AI is running without clear boundaries, stop here. Containment first.
Move 2: Instrument
Gate question: "Can you measure what this AI is actually doing?"
Requirements:
- Logging of inputs, outputs, and decisions
- Baseline metrics established (accuracy, latency, cost, error rate)
- Ability to compare AI decisions against human decisions
- Monitoring for drift (is performance changing over time?)
If you can't measure it, you can't evaluate it. Instrument before you trust.
Move 3: Verify
Gate question: "Have you tested this against known-good answers?"
Requirements:
- Test set with ground truth (known correct answers)
- Performance measured against human baseline
- Edge cases explicitly tested (what happens at the boundaries?)
- Failure modes documented (when does it break? how does it fail?)
Move 4: Prove
Gate question: "Can you demonstrate this works in the conditions where you plan to use it?"
Requirements:
- Tested in production-like environment (not just dev/staging)
- Tested with real users (not just the team that built it)
- Performance validated under realistic load and data quality
- Stakeholders have seen it work AND seen it fail
CRITICAL: Proving means showing both success AND failure conditions. A demo that only shows the happy path proves nothing.
Move 5: Narrow
Gate question: "Have you defined the specific, limited scope for production use?"
Requirements:
- Explicit scope: what decisions this AI is authorized to make
- Explicit exclusions: what decisions require human override
- Escalation path: what happens when the AI encounters something outside its scope
- Rollback plan: how do you revert if it goes wrong
Move 6: Widen
Gate question: "Based on proven performance in the narrow scope, what's the next increment?"
Requirements:
- Evidence from narrow deployment supports expansion
- New scope is an incremental extension, not a leap
- New edge cases identified and tested
- Monitoring from Move 2 is still active and shows stable performance
Move 7: Standardise
Gate question: "Is this AI application ready to become organizational infrastructure?"
Requirements:
- Documentation exists for operators (not just builders)
- Training exists for users
- SLA defined and monitored
- Incident response procedure documented
- Regular review cadence established (quarterly minimum)
Output
# AI Graduation Assessment: [AI Application Name]
Date: [date]
## Current Position
Move [N] of 7: [Move Name]
## Gate Assessment
| Move | Status | Evidence | Gap |
|------|--------|----------|-----|
| 1. Contain | [Pass/Fail/Partial] | [evidence] | [what's missing] |
| 2. Instrument | [Pass/Fail/Partial] | [evidence] | [what's missing] |
| 3. Verify | [Pass/Fail/Partial] | [evidence] | [what's missing] |
| 4. Prove | [Pass/Fail/Partial] | [evidence] | [what's missing] |
| 5. Narrow | [Pass/Fail/Partial] | [evidence] | [what's missing] |
| 6. Widen | [Pass/Fail/Partial] | [evidence] | [what's missing] |
| 7. Standardise | [Pass/Fail/Partial] | [evidence] | [what's missing] |
## Recommendation
[Which move to focus on next and what needs to happen]
## Warning Signs
[Anything that suggests the organization is trying to skip gates]
Based on Stuart Winter-Tear's AI Graduation Path.
Part of the ThinkHaven Method Kit by Kevin Holland.
Full Board of Directors experience: ThinkHaven
