What it is
A visual map connecting a desired outcome to customer opportunities from research, candidate solutions, and the assumption tests showing whether a solution is worth building. It keeps choices traceable to evidence but does not replace it.
Why use it
Teams often jump from a goal to a favorite solution. Mapping makes reasoning visible, shows where evidence is missing, and encourages comparing options.
Use this when
- Research produced many opportunities and the team must choose
- There is a target outcome but several candidate solutions
- Build decisions must be traceable for stakeholders
- Discovery continues alongside delivery
You may not need this when
- The scope is fixed and the solution already chosen with evidence
- No agreed outcome exists to anchor the tree
- Research is too early to name opportunities; synthesize first
Dependencies — what you need
- An agreed, ideally measurable, outcome at the root
- Synthesized research identifying opportunities
- Time to update the tree as evidence arrives
- Collaboration tools (e.g., FigJam, Miro)
People involved
- Design
- Research
- Product
- Engineering
- Stakeholders
Typical outputs
- A tree linking outcome, opportunities, solutions, and tests
- Prioritized opportunities with evidence
- Several candidate solutions per chosen opportunity
- Assumptions to test next
Template
Opportunity Tree template
A fill-in structure for running this method: what to prepare, what to do, what to record, and how to read the result.
How AI can help
AI as augmentation — not replacement
- Draft opportunity statements from research, for the team to check against the evidence
- Generate alternative solutions for a chosen opportunity
- Identify assumptions a solution depends on
- Flag branches with no evidence
AI prompt
A prompt for any AI assistant. It works only from material you paste in, keeps assumptions apart from findings, and leaves the decisions to you.