Opportunity-Solution Tree

A visual thinking framework (by Teresa Torres) for connecting business outcomes to user opportunities to potential solutions to experiments — that AI can help build from research or a product goal.

Syntax

product-owner-ai
Build an Opportunity-Solution Tree for this product goal:

Desired outcome: [business or user metric to improve]

For each level:
1. Outcome: [What we want to achieve]
2. Opportunities: [User problems or needs that, if addressed, would achieve the outcome]
3. Solutions: [Product ideas that address each opportunity]
4. Experiments: [Smallest test to validate each solution]

Example

product-owner-ai
Outcome: Increase invoice payment rate within 30 days

Opportunity 1: Clients forget to pay
- Solution: Automated payment reminders at 7, 14, 21 days
- Experiment: Manual reminder pilot for 20 clients — measure impact

Opportunity 2: Clients find the payment process confusing
- Solution: One-click pay link in invoice email
- Experiment: A/B test current flow vs. simplified one-click flow

Opportunity 3: Invoice arrives at wrong time
- Solution: Scheduled invoice delivery
- Experiment: Survey 10 clients on preferred invoice timing