aipom-bet-charter

name	aipom-bet-charter
description	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.
type	component
category	portfolio-and-investment-choices
phase	1
status	active
operating_level	["portfolio","product-team","initiative"]
audience	["CPO","VP Product","Product Operations","Product Manager","Team Lead","Engineering","Design","Finance","AI Governance"]
best_for	["Deciding whether an AI idea deserves discovery funding","Reframing a pilot with unclear outcomes or ownership","Preparing a portfolio review or stage-gate decision"]
evidence_required	["Customer or workflow problem evidence","Expected outcomes and economic assumptions","Feasibility, data, evaluation, and governance constraints","Results from prior tests where available"]
produces	["One-page AI bet charter","Assumption and evidence ledger","Next test and decision rule"]
assessment_questions	["POR-01","POR-02","POR-03","STR-02","STR-04"]
maturity_move	{"from":"emerging","to":"repeatable"}
estimated_time	45-60 min
group_size	3-8
depends_on	["aipom-strategy-thesis-advisor"]
combine_with	["aipom-use-case-triage","aipom-investment-stage-gates","aipom-economic-case-builder"]
sources	[]

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.