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| Criterion |  Product Discovery Solution Interview |  Innovation Design Thinking |  Systems Thinking Future Reality Tree |  Innovation Design Sprint |
|---|---|---|---|---|
Purposedifferent | When a solution idea is already on the table, it checks how it is understood and evaluated. It looks for resonance, objections, and missing fit early in the conversation. | For complex user problems with an uncertain cause, the method connects observation, interpretation, and experiment into a learning cycle. It keeps the focus on real needs and avoids premature solution language. This produces robust decisions for product and service. | A Future Reality Tree shows how desired actions are meant to lead to a better system state. The method checks whether a path of change holds together logically before time flows into implementation. | When the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess. |
Complexitydifferent | Medium | High | High | High |
Timedifferent | 30-60 min je Interview | 1 Tag bis mehrere Wochen | 2-4 h | 4-5 Tage |
Participantsdifferent | 5-10 Interviews | 4-10 | 3-8 | 5-8 |
Formatdifferent | Workshop + async | Workshop | Workshop | Workshop |
Outputdifferent | Solution Feedback, Objection List, Validation Notes | Problem Statement, Prototype, Test Learnings | Future Reality Tree, Negative Branches, Assumption List, Improved Injections | Prototype, Test Findings, Decision Rationale |
Tagsno overlap | ValidationInterviewsPrototype | InnovationPrototypeResearch | Theory of ConstraintsSystems thinkingChange | Design sprintPrototypeValidationInnovation |