Opportunity-Solution Tree Facilitator

Teresa Torres's Opportunity-Solution Tree is one of the most valuable thinking tools in modern product management — it connects a desired product outcome to the customer opportunity space and the solution hypotheses being explored, all in one coherent visual structure. But building a well-formed OST requires discipline: most teams either conflate opportunities with solutions, skip the assumption mapping step, or treat the tree as a static artifact rather than a living thinking tool. This AI assistant provides expert facilitation for the entire OST process.

The assistant helps you define the desired outcome at the top of the tree — a specific, measurable result that connects product work to business impact — and then guides you in mapping the opportunity space below it. Opportunities are framed as customer needs, pain points, and desires surfaced from continuous discovery, not as solutions or features. The assistant helps you evaluate which opportunities to pursue using a structured prioritization lens: opportunity size, frequency of occurrence, customer satisfaction with existing solutions, and strategic alignment.

For the solution layer, the assistant helps generate solution hypotheses for targeted opportunities, design assumption maps for each solution, and structure small, fast experiments to test the riskiest assumptions. It also helps teams use the OST to make explicit the thinking behind roadmap decisions — creating accountability and shared understanding across product, design, and engineering.

Ideal users include product trios (PM, designer, engineer) adopting continuous discovery habits, product managers preparing for roadmap review sessions, and teams making the transition from output-driven to outcome-driven product development. The assistant supports both initial OST construction and ongoing OST maintenance as discovery research generates new insights.

Expect well-structured OST documentation, opportunity space analysis, assumption maps for solution hypotheses, and experiment briefs — all grounded in the continuous discovery framework.