AIPOM Bet Charter
What Is It
Frame one AI investment as a testable bet: for whom, which condition should change, why AI may help, what value could follow, what constraints apply, who owns the decision, and what evidence earns the next investment.
Why Use It
Pilots become zombies when enthusiasm substitutes for a hypothesis, owner, economics, or stopping rule. A charter makes the next decision explicit. Completing it does not validate the bet; the test must produce decision-relevant evidence.
When to Use It
Use before discovery funding, vendor commitment, pilot launch, or expansion. Do not use it to justify a decision already made or to replace deeper opportunity framing, legal review, or an economic case when stakes require them.
What It Produces
- Bet hypothesis and strategic fit
- Customer, product, and economic outcomes
- Evidence and riskiest assumptions
- Constraints, non-goals, and accountable owner
- Smallest next test with continuation, pivot, pause, or stop criteria
Who Should Participate
Include the investment decision owner, Product Manager, technical and design partners, finance or operations as needed, and governance partners proportionate to consequences.
Evidence to Bring
Bring research, workflow evidence, baselines, economic measures, prior tests, technical constraints, data readiness, evaluation evidence, and risk requirements. Distinguish evidence from estimates and assumptions.
How to Do It
- Name the decision, scope, owner, and investment horizon.
- State the actor, current condition, evidence, and cost of the problem.
- Write the bet: “If we…, for…, then…, because….”
- Connect customer behavior to product and economic outcomes.
- Compare AI with non-AI alternatives and explain why AI belongs.
- Identify the assumptions most likely to invalidate value, feasibility, responsibility, or adoption.
- Define constraints, non-goals, dependencies, and human accountability.
- Choose the smallest test that changes a funding decision.
- Set explicit continue, pivot, pause, and stop rules.
Key Concepts
- Bet, not promise: uncertainty remains visible.
- Economic consequence: revenue, margin, cost, risk, safety, or decision speed—not AI activity.
- Evidence-producing test: learning must change a decision.
- Named owner: a committee may contribute, but a human decides.
Organizational Applications
Use to compare proposals, repair ownerless pilots, prepare quarterly reviews, or hand a chosen strategy into delivery without pretending discovery is complete.
Common Pitfalls
- Starting with a vendor or model
- Naming outputs instead of outcomes
- Hiding assumptions inside confident forecasts
- Funding a full build as the “test”
- Omitting non-AI alternatives and stop criteria
- Treating charter approval as customer validation
Combine With
Use aipom-strategy-thesis-advisor for strategic direction, aipom-use-case-triage for comparisons, aipom-investment-stage-gates for recurring decisions, and aipom-economic-case-builder for deeper economics.
Assets and Templates
- Bet charter template
- Synthetic worked example
- Weak example
Sources
This skill is an original AIPOM synthesis of evidence-based product and portfolio investment practices.
1---2name: aipom-bet-charter3description: Turn an AI idea into an owned investment hypothesis with outcomes, economics, constraints, evidence, and a next learning test. Use before funding or expanding an initiative.4---56# AIPOM Bet Charter78## What Is It910Frame one AI investment as a testable bet: for whom, which condition should change, why AI may help, what value could follow, what constraints apply, who owns the decision, and what evidence earns the next investment.1112## Why Use It1314Pilots become zombies when enthusiasm substitutes for a hypothesis, owner, economics, or stopping rule. A charter makes the next decision explicit. Completing it does not validate the bet; the test must produce decision-relevant evidence.1516## When to Use It1718Use before discovery funding, vendor commitment, pilot launch, or expansion. Do not use it to justify a decision already made or to replace deeper opportunity framing, legal review, or an economic case when stakes require them.1920## What It Produces2122- Bet hypothesis and strategic fit23- Customer, product, and economic outcomes24- Evidence and riskiest assumptions25- Constraints, non-goals, and accountable owner26- Smallest next test with continuation, pivot, pause, or stop criteria2728## Who Should Participate2930Include the investment decision owner, Product Manager, technical and design partners, finance or operations as needed, and governance partners proportionate to consequences.3132## Evidence to Bring3334Bring research, workflow evidence, baselines, economic measures, prior tests, technical constraints, data readiness, evaluation evidence, and risk requirements. Distinguish evidence from estimates and assumptions.3536## How to Do It37381. Name the decision, scope, owner, and investment horizon.392. State the actor, current condition, evidence, and cost of the problem.403. Write the bet: “If we…, for…, then…, because….”414. Connect customer behavior to product and economic outcomes.425. Compare AI with non-AI alternatives and explain why AI belongs.436. Identify the assumptions most likely to invalidate value, feasibility, responsibility, or adoption.447. Define constraints, non-goals, dependencies, and human accountability.458. Choose the smallest test that changes a funding decision.469. Set explicit continue, pivot, pause, and stop rules.4748## Key Concepts4950- **Bet, not promise:** uncertainty remains visible.51- **Economic consequence:** revenue, margin, cost, risk, safety, or decision speed—not AI activity.52- **Evidence-producing test:** learning must change a decision.53- **Named owner:** a committee may contribute, but a human decides.5455## Organizational Applications5657Use to compare proposals, repair ownerless pilots, prepare quarterly reviews, or hand a chosen strategy into delivery without pretending discovery is complete.5859## Common Pitfalls6061- Starting with a vendor or model62- Naming outputs instead of outcomes63- Hiding assumptions inside confident forecasts64- Funding a full build as the “test”65- Omitting non-AI alternatives and stop criteria66- Treating charter approval as customer validation6768## Combine With6970Use `aipom-strategy-thesis-advisor` for strategic direction, `aipom-use-case-triage` for comparisons, `aipom-investment-stage-gates` for recurring decisions, and `aipom-economic-case-builder` for deeper economics.7172## Assets and Templates7374- [Bet charter template](template.md)75- [Synthetic worked example](examples/worked-example.md)76- [Weak example](examples/weak-example.md)7778## Sources7980This skill is an original AIPOM synthesis of evidence-based product and portfolio investment practices.
Dean Peters