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
- Name the decision this frame must support and its scope.
- Describe the actor, situation, current condition, and evidence without prescribing AI.
- Explain the consequence in customer, product, operational, economic, safety, or risk terms.
- Record current workarounds and why they remain insufficient.
- Identify affected or underrepresented perspectives and disagreements.
- Compare AI, non-AI, process, policy, and no-change alternatives.
- State why probabilistic or adaptive behavior may help—and where it may make the condition worse.
- Separate facts, evidence, assumptions, interpretations, and open questions.
- 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.
- Harshest uncertainty: test what could kill the opportunity, not what is easiest to demonstrate.
Organizational Applications
Use in discovery intake, portfolio shaping, strategy reviews, internal workflow redesign, and recovery from a vendor-led pilot with no stable problem definition.
Common Pitfalls
- Rewording a proposed feature as a problem
- Treating executive conviction as user evidence
- Ignoring non-AI alternatives
- Framing only the buyer while omitting affected users
- Hiding data, evaluation, or accountability uncertainty
- Producing a polished frame without a next decision
Combine With
Use aipom-outcome-value-map to make causal value explicit, aipom-use-case-triage to compare opportunities, and aipom-bet-charter to establish ownership and the next investment test.
Assets and Templates
- Opportunity frame template
- Synthetic worked example
- Weak example
Sources
This skill is an original AIPOM extension of evidence-based problem and opportunity framing. It deliberately relies on established foundational problem-framing methods rather than recreating them.
1---2name: aipom-opportunity-framing3description: Frame an AI product opportunity around an evidenced condition, affected actors, consequences, alternatives, and the uncertainty that should be tested next.4---56# AIPOM Opportunity Framing78## What Is It910Define 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.1112## Why Use It1314Tool-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.1516## When to Use It1718Use 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.1920## What It Produces2122- Actor, situation, current condition, and desired change23- Evidence, consequences, workarounds, and affected perspectives24- AI fit and credible non-AI alternatives25- Assumption ledger and smallest next learning question2627## Who Should Participate2829Include 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.3031## Evidence to Bring3233Bring observations, research, workflow records, support themes, baselines, existing alternatives, failed attempts, and constraints. Label anecdotes, estimates, and leadership beliefs as such.3435## How to Do It36371. Name the decision this frame must support and its scope.382. Describe the actor, situation, current condition, and evidence without prescribing AI.393. Explain the consequence in customer, product, operational, economic, safety, or risk terms.404. Record current workarounds and why they remain insufficient.415. Identify affected or underrepresented perspectives and disagreements.426. Compare AI, non-AI, process, policy, and no-change alternatives.437. State why probabilistic or adaptive behavior may help—and where it may make the condition worse.448. Separate facts, evidence, assumptions, interpretations, and open questions.459. Choose the smallest next inquiry that could change the opportunity decision.4647## Key Concepts4849- **Condition before solution:** describe what must change before choosing how.50- **AI fit:** AI earns consideration through the nature of the work, not fashion.51- **Consequence:** frequency alone does not establish importance.52- **Harshest uncertainty:** test what could kill the opportunity, not what is easiest to demonstrate.5354## Organizational Applications5556Use in discovery intake, portfolio shaping, strategy reviews, internal workflow redesign, and recovery from a vendor-led pilot with no stable problem definition.5758## Common Pitfalls5960- Rewording a proposed feature as a problem61- Treating executive conviction as user evidence62- Ignoring non-AI alternatives63- Framing only the buyer while omitting affected users64- Hiding data, evaluation, or accountability uncertainty65- Producing a polished frame without a next decision6667## Combine With6869Use `aipom-outcome-value-map` to make causal value explicit, `aipom-use-case-triage` to compare opportunities, and `aipom-bet-charter` to establish ownership and the next investment test.7071## Assets and Templates7273- [Opportunity frame template](template.md)74- [Synthetic worked example](examples/worked-example.md)75- [Weak example](examples/weak-example.md)7677## Sources7879This skill is an original AIPOM extension of evidence-based problem and opportunity framing. It deliberately relies on established foundational problem-framing methods rather than recreating them.
Dean Peters