aipom-opportunity-framing

name	aipom-opportunity-framing
description	Frame an AI product opportunity around an evidenced condition, affected actors, consequences, alternatives, and the uncertainty that should be tested next.
type	component
category	strategy-and-economic-outcomes
phase	2
status	active
operating_level	["portfolio","product-team","initiative"]
audience	["Product Operations","Product Manager","Team Lead","Design","Engineering","Research","Data","AI Governance"]
best_for	["Turning an AI idea into a problem-led opportunity","Challenging a vendor-first proposal","Preparing an opportunity for outcome mapping and triage"]
evidence_required	["Customer or workflow evidence","Current alternatives and workarounds","Frequency and consequence evidence","Constraints and affected perspectives"]
produces	["AI opportunity frame","Evidence and assumption ledger","Smallest next learning question"]
assessment_questions	["STR-02","STR-03","STR-04","POR-01"]
maturity_move	{"from":"emerging","to":"repeatable"}
estimated_time	45-75 min
group_size	3-8
depends_on	["aipom-strategy-thesis-advisor"]
combine_with	["aipom-outcome-value-map","aipom-use-case-triage","aipom-bet-charter"]
sources	[]

AIPOM Opportunity Framing

What Is It

Define one AI product opportunity as an evidenced condition worth changing, not a model or feature looking for a use. Extend credible problem-framing work by adding AI-specific uncertainty, behavior, data, evaluation, and accountability considerations.

Why Use It

Tool-first ideas skip the question of whether the condition matters, whether AI belongs, and which uncertainty could invalidate the investment. A shared frame makes those choices inspectable before solution momentum hardens them.

When to Use It

Use before a bet charter, use-case comparison, discovery investment, or vendor selection. Use an existing problem-framing method first when the underlying user problem is still unclear; this skill should extend that foundation, not duplicate it.

What It Produces

• Actor, situation, current condition, and desired change
• Evidence, consequences, workarounds, and affected perspectives
• AI fit and credible non-AI alternatives
• Assumption ledger and smallest next learning question

Who Should Participate

Include the Product Manager, Team Lead, Product Operations where the opportunity crosses workflows, people close to the work or customer, design or research, technical and data partners, and governance partners proportionate to the consequences.

Evidence to Bring

Bring observations, research, workflow records, support themes, baselines, existing alternatives, failed attempts, and constraints. Label anecdotes, estimates, and leadership beliefs as such.

How to Do It

1. Name the decision this frame must support and its scope.
2. Describe the actor, situation, current condition, and evidence without prescribing AI.
3. Explain the consequence in customer, product, operational, economic, safety, or risk terms.
4. Record current workarounds and why they remain insufficient.
5. Identify affected or underrepresented perspectives and disagreements.
6. Compare AI, non-AI, process, policy, and no-change alternatives.
7. State why probabilistic or adaptive behavior may help—and where it may make the condition worse.
8. Separate facts, evidence, assumptions, interpretations, and open questions.
9. Choose the smallest next inquiry that could change the opportunity decision.

Key Concepts

• Condition before solution: describe what must change before choosing how.
• AI fit: AI earns consideration through the nature of the work, not fashion.
• Consequence: frequency alone does not establish importance.
• test what could kill the opportunity, not what is easiest to demonstrate.