AI use-case value canvas
Frame the outcome before selecting a model or workflow.
- Whose work or decision should improve?
- What observable outcome matters?
- Why is AI appropriate for this constraint?
- What is the credible non-AI alternative?
Open resource · AI value and governance
Govern AI close enough to the work to improve it.
A practical toolkit for moving from an interesting AI possibility to an accountable product or workflow with observable value.
Download the toolkitWhy this exists
Possibility is not yet value.
AI work often begins with model access and a demonstration. Useful adoption begins somewhere else: a meaningful outcome, a person accountable for the consequences, controls shaped around real failure modes and evidence strong enough to guide investment.
The tools are intentionally lightweight. Use only the sections that can change a decision. Increase rigor as impact, uncertainty and potential harm increase.
Six connected tools
From value hypothesis to operating evidence.
Start with the value canvas. Return to the accountability map and guardrails whenever the use case, affected group, data or operating context changes.
Human-accountability map
Make ownership visible across design, use, review and consequences.
- Who owns the product outcome?
- Who can approve, override or stop the system?
- Who reviews affected-person and domain risk?
- Who responds when the system fails?
Guardrail design checklist
Place controls near the decisions and failure modes they must influence.
- Which data must not enter the model?
- Which outputs require human review?
- What limits, fallbacks and kill switches are needed?
- What must be logged without exposing sensitive content?
POC-to-MVP decision gate
Separate technical possibility from a product worth operating.
- Which risky assumption did the POC reduce?
- What remains unknown about real use?
- Can the MVP be operated safely end to end?
- What evidence would justify further investment?
Outcome and evidence tracker
Measure behavior, quality, risk and economics before scale.
- What baseline are we changing?
- Which quality and risk signals can be observed?
- What is the cost per useful outcome?
- What evidence would cause us to stop or redesign?
Governance review agenda
Run a proportionate review that changes the work, not only the paperwork.
- Is the value hypothesis still credible?
- Are failure modes and affected groups understood?
- Are controls operating as intended?
- What decision and owner leave the review?
POC to MVP to scale
Progress only when the next commitment is justified.
A POC reduces uncertainty. An MVP tests a usable outcome. Scale is an operating commitment. They need different evidence.
GateEvidence to progressReason to pause
Problem
A real user or operating constraint is evidenced.
Do not progress from an AI-first idea.
Value
A measurable outcome and baseline are defined.
Do not use activity as a proxy for value.
Control
Ownership, review, fallback and stop conditions are clear.
Do not rely on policy alone.
Evidence
Representative evaluation covers quality, risk, cost and behavior.
Do not scale from a compelling demo.
Operation
Monitoring, incident response and change ownership are ready.
Do not treat launch as completion.
A 45-minute review
Make one decision and leave with one accountable owner.
- 05 minRestate the value hypothesis
What should improve, for whom and against which baseline?
- 10 minExamine current evidence
Review quality, risk, behavior, cost and operational signals.
- 10 minTest accountability
Confirm who decides, reviews, overrides, responds and can stop the system.
- 10 minInspect guardrails
Check whether controls are working in the product and workflow.
- 10 minMake the next decision
Continue, adapt, constrain or stop. Name the owner and next evidence date.
Boundaries
A working aid, not a substitute for specialist judgment.
Proportionate
Increase review depth as impact, uncertainty and potential harm increase.
Evidence-led
Mark assumptions and unknowns. Do not invent confidence where evaluation is incomplete.
Accountable
Keep a named person responsible for decisions and consequences throughout the lifecycle.