Can use contextual inquiry with a defined task, suitable inputs, and a basic validation check.
traditional skill · Stage 02
Contextual inquiry
Contextual inquiry is an established design capability used to build a verified fact base without confusing generated synthesis with lived evidence. In TADF it remains a human foundation for evaluating, correcting, and contextualising AI output.
Why it matters
This skill supports reliable work in Intelligence Gathering and prevents speed from being mistaken for quality.
Stage outcome
Build a verified fact base, user behavior, market landscape, competitive structure, grounded in the charter's success metrics from Stage 01
What good looks like
01
Frames contextual inquiry against a clear goal, relevant evidence, and the stage quality gate.
02
Applies the craft independently, then uses it to challenge or refine AI-generated work.
03
Documents the decision, trade-off, correction, or override so another designer can follow the reasoning.
Checkpoint application
Apply this skill before the Stage 02 gate is approved. The accountable designer must be able to explain how it changed the work, what evidence was considered, and why the result is safe to advance.
Common failure patterns
- Confusing clustered or synthetic patterns with observed user truth, especially when outliers are inconvenient.
- Allowing AI convenience to weaken an established craft practice that is still necessary for independent judgment.
- Recording the final artefact while omitting the rejected option, correction, trade-off, or override that explains the decision.
Five-minute practice
Use it on the work you already have.
Take one current intelligence gathering artifact. Apply contextual inquiry for five minutes, then record one AI contribution, one human correction, and the evidence that justified the final decision.
Evidence of capability
- A reviewed research synthesis, validated persona, or evidence map showing the skill in use.
- A short rationale linking the decision to evidence, risk, and intended outcome.
- An example of an AI suggestion that was corrected, rejected, or deliberately accepted.
Leadership assessment questions
01
Can the designer demonstrate contextual inquiry on a real intelligence gathering decision?
02
What observable evidence distinguishes competent performance from confident explanation?
03
Which anchor skill depends on this capability, and how would a gap surface in delivery?
Maturity ladder
Can diagnose weak output, redirect the work, combine sources, and explain the resulting trade-off.
Can decide when not to delegate, own the human checkpoint, and remain accountable for the outcome.
Backlinks and relationships