Adapt cross-industry analogies to a local challenge
Compare sourced cases from other contexts, extract transferable mechanisms, and turn only verified analogies into locally testable adaptation hypotheses.
Why
The workflow problem
Teams miss mechanisms used elsewhere, while generic web synthesis encourages false transfer and treats different local contexts as interchangeable.
Cross-industry comparison can expand the solution space beyond familiar patterns. AI can accelerate comparison, but source verification and contextual judgement determine whether an analogy is useful rather than misleading.
Concrete output
What you will produce
An analogy matrix containing the original case, source URL, transferable mechanism, contextual mismatch, local adaptation hypothesis, and proposed test.
How
Run the workflow
Specify the local challenge
Humans define the evidence-backed local need, affected people, setting, non-negotiable constraints, and harms that a borrowed mechanism must not introduce. Keep a short list of what is not transferable.
Gather source-linked cases
Use a source-aware tool or human research to collect original case material, not summaries alone. A human records publisher, date, context, outcomes claimed, and a stable source URL for each candidate.
Extract mechanisms and mismatches
Ask AI to compare only the supplied cases and to separate the underlying mechanism from surface features. Every output remains a draft until a human checks the original source.
Compare cases without claiming transferability
Compare the source-linked cases below against our local challenge. Use only supplied information. Local challenge and constraints: [insert] Cases: [case ID | original URL | context | observed mechanism | limits] Return: case ID | transferable mechanism | surface feature to ignore | contextual mismatch | local adaptation hypothesis | assumption requiring local research. Do not invent outcomes, infer equivalence between populations, or recommend implementation.Verify and score the analogies
Humans inspect originals, score contextual distance and risk, consult local expertise where needed, and discard cases with weak sourcing or harmful assumptions.
Formulate local experiments
Convert the strongest mechanisms into small adaptation hypotheses with a target context, owner, and learning question. Test with affected people before committing to delivery.
Human–AI partnership
Who contributes what
AI contribution
Organise supplied cases, compare mechanisms, and identify stated mismatches. It does not establish that an external result is true or transferable.
Human responsibility
Verify sources, understand local conditions, involve affected people and domain experts, reject false analogies, and define responsible experiments.
Stop rule
Stop AI use when a case lacks an original source, an analogy treats vulnerable populations as interchangeable, local constraints are missing, or a proposed adaptation bypasses local-user research.
Check before use
Watch out
Case studies often omit failures and power conditions. Check publication incentives, date, geography, governance, and who was excluded. Never use an analogy to justify a decision without local evidence.
Evidence base
What supports this workflow
One harvested educational-project reference supports the idea of AI-assisted international case research, but it does not evaluate this workflow. The analogy matrix is a method-led reconstruction with limited direct evidence for AI benefit.
- Moldova.org and Fundația Orange Moldova · Orange Digital Center: în 2 luni, absolvenții cursului Design Thinking au dezvoltat 8 proiecte
Direct but thin source lineage for AI-supported international case research used in ideation; the method is described briefly and outcomes are not independently evaluated.
- Stanford d.school · Idea Expedition
Canonical source for expanding and selecting a design idea space.