Opportunity Solution Tree
A visual framework that maps the path from a desired outcome through opportunities discovered in user research to potential solutions and the experiments that validate them. It ensures every initiative connects clearly to a measurable business outcome.
The Opportunity Solution Tree, created by Teresa Torres, structures product discovery by making the logical connections between goals, user needs, and solutions explicit. The tree starts with a target outcome at the top, branches into opportunity spaces discovered through research, further branches into possible solutions for each opportunity, and finally maps to experiments that test each solution. This visual structure prevents teams from jumping to solutions without understanding the problem space.
For AI product teams, the opportunity solution tree is particularly valuable because it prevents the common pattern of implementing AI because it is available rather than because it solves a validated user need. The tree forces teams to trace every AI feature back through a specific opportunity to a measurable outcome. Growth teams use the tree to organize their experiment backlog, ensuring each test maps to a clear hypothesis about an opportunity. When experiments fail, the tree makes it easy to pivot to alternative solutions for the same opportunity rather than abandoning the entire initiative.
Related Terms
Product-Market Fit
The degree to which a product satisfies strong market demand. Achieving product-market fit means customers are actively seeking, using, and recommending your product because it solves a real and pressing problem for them.
Jobs to Be Done
A framework that defines customer needs as functional, emotional, and social jobs people hire products to accomplish. It shifts focus from demographic segments to the underlying progress customers are trying to make in specific circumstances.
Minimum Viable Product
The simplest version of a product that can be released to test a core hypothesis with real users. An MVP delivers just enough functionality to gather validated learning while minimizing development time and cost.
Minimum Lovable Product
An evolution of the MVP concept that emphasizes delivering enough quality and delight that early users genuinely love the product. It balances speed-to-market with the emotional engagement needed to drive organic word-of-mouth growth.
Design Sprint
A five-day structured process for rapidly prototyping and testing ideas with real users. Developed at Google Ventures, it compresses months of debate into a focused week of mapping, sketching, deciding, prototyping, and testing.
Lean Startup
A methodology for developing businesses and products through validated learning, rapid experimentation, and iterative releases. It emphasizes reducing waste by testing assumptions before building fully-featured solutions.