In one minute
Prioritise assumptions by importance and strength of evidence before investing in a solution.
Extract testable claims, map each by importance and evidence, and test the high-importance, low-evidence corner first. Reposition claims as evidence arrives; the quadrants are a discussion aid, not a probability model.
The problem it addresses
A polished proposal hides the claims on which it would fail.
A museum plans an AI guide for visitors with limited vision. Staff assume voice output is the main barrier; visitor interviews have not yet tested navigation or consent concerns.
When to use it
- Use Assumption Mapping when a polished proposal hides the claims on which it would fail.
- The team can examine: A proposed decision, explicit hypotheses, dated evidence and an experiment owner.
- A relevant situation is: A museum plans an AI guide for visitors with limited vision. Staff assume voice output is the main barrier; visitor interviews have not yet tested navigation or consent concerns.
When not to use it
Do not use it to replace evidence, accountable judgement or affected people's participation.
Confusing confidence with evidence or calling a vague belief a testable hypothesis.
Inputs required
A proposed decision, explicit hypotheses, dated evidence and an experiment owner.
Step-by-step process
1. Bound the decision and name its owner.
Write the scope, decision owner and people who can veto or revise the result before starting the workshop.
2. Collect the necessary evidence and affected perspectives.
Collect and date the necessary evidence: A proposed decision, explicit hypotheses, dated evidence and an experiment owner.
3. List claims that must be true for the proposal to work.
Record the evidence and decision criterion for: List claims that must be true for the proposal to work.
4. Separate desirability, feasibility, viability and external dependency risks.
Record the evidence and decision criterion for: Separate desirability, feasibility, viability and external dependency risks.
5. Place each claim by importance and current evidence, recording disagreement.
Record the evidence and decision criterion for: Place each claim by importance and current evidence, recording disagreement.
6. Turn the riskiest claim into a precise, falsifiable hypothesis.
Record the evidence and decision criterion for: Turn the riskiest claim into a precise, falsifiable hypothesis.
7. Run the smallest ethical test and update the map with results.
Record the evidence and decision criterion for: Run the smallest ethical test and update the map with results.
8. Record the output, next test and review trigger.
Store the artifact with its evidence links, owner, bounded next test and date or trigger for review.
AI automation lens
AI may sort authorised notes and expose missing evidence. It must not invent observations, silently decide for affected people or present a generated map as validated.
Visual model
Text alternative: List claims that must be true for the proposal to work; Separate desirability, feasibility, viability and external dependency risks; Place each claim by importance and current evidence, recording disagreement; Turn the riskiest claim into a precise, falsifiable hypothesis; Run the smallest ethical test and update the map with results.
Interactive example
Scenario: A museum plans an AI guide for visitors with limited vision. Staff assume voice output is the main barrier; visitor interviews have not yet tested navigation or consent concerns.
Worked answer: Rank the untested navigation and consent claims as critical, observe a small accessible walkthrough and revise the product brief before building.
Facilitation notes
Record disagreements before synthesising; ask whose evidence is missing; state who may revise the result.
Expected output
- An inspectable decision artifact, its assumptions and evidence, a responsible owner, a bounded next test and a review date.
- Run the smallest ethical test and update the map with results.
- A named owner, test and review date.
Common mistakes
Confusing confidence with evidence or calling a vague belief a testable hypothesis.
Quality checklist
- The decision and system boundary are explicit.
- Affected people and alternative accounts are included.
- Each important claim has a source or is marked as an assumption.
- An owner, safeguard and disconfirming signal are named.
Template
Knowledge check
Question: What mistake would undermine this method in the scenario?
Answer: Confusing confidence with evidence or calling a vague belief a testable hypothesis.
Related tools
References
- Assumption Mapping — method source (opens in a new tab). Accessed 2026-10-09.
- Further practice reference (opens in a new tab). Accessed 2026-10-09.
Method profile
The method structures a discussion and a test; it does not establish causal proof or guarantee success.