# Aipom Bet Charter

> 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.

- Skill: `deanpeters/aipom-bet-charter` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add deanpeters/aipom-bet-charter`
- Raw SKILL.md: https://api.skillmd.com/api/skills/deanpeters/aipom-bet-charter/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Dean Peters (https://skillmd.com/u/deanpeters)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/deanpeters/aipom-bet-charter

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# 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

1. Name the decision, scope, owner, and investment horizon.
2. State the actor, current condition, evidence, and cost of the problem.
3. Write the bet: “If we…, for…, then…, because….”
4. Connect customer behavior to product and economic outcomes.
5. Compare AI with non-AI alternatives and explain why AI belongs.
6. Identify the assumptions most likely to invalidate value, feasibility, responsibility, or adoption.
7. Define constraints, non-goals, dependencies, and human accountability.
8. Choose the smallest test that changes a funding decision.
9. 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](template.md)
- [Synthetic worked example](examples/worked-example.md)
- [Weak example](examples/weak-example.md)

## Sources

This skill is an original AIPOM synthesis of evidence-based product and portfolio investment practices.