R1 · Use-case field guide
Practical AI use cases for design thinking
Each case starts from a professional workflow, produces a concrete artefact, and makes the human–AI division of responsibility explicit.
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Showing 12 of 30 use cases
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.
Build a disposable clickable interface from a verified flow
Turn a verified task flow into a stable, disposable clickable prototype that can be tested without being mistaken for production software.
Build evidence-backed behavioural personas
Create constrained behavioural persona drafts from coded evidence, then audit every attribute before using them to inform design decisions.
Co-ideate through a fixed question protocol
Use a question-led AI exchange to develop a designer-authored concept from an evidence-backed How-Might-We question.
Code and affinity-map research evidence
Use AI for a traceable first pass across an approved qualitative corpus, then have researchers verify, reorganise, and name the themes.
Converge on concepts with a desirability-feasibility-responsibility review
Turn an AI-expanded concept pool into a human decision by independently assessing desirability, feasibility, viability, and responsibility.
Design a contextual observation protocol
Prepare a neutral field-observation sheet that captures behaviour, environment, workarounds, and service context consistently.
Generate concepts from verified opportunity signals
Use AI to recombine a curated signal pack into traceable concepts, then have humans assess the concepts against real-world criteria.
Grow the idea pool after a human-only round
Generate ideas independently first, then use AI to search for genuinely different mechanisms before the team curates one combined portfolio.
Maintain a what-we-heard / what-we-changed prototype log
Pair AI-assisted prototype drafts with an auditable record of de-identified input, participant feedback, changes, rejections, and accountable ownership.
Map the current-state user journey
Assemble fragmented research into a current-state journey map while labelling evidence, operational context, and assumptions separately.
Moderate structured feedback at scale
Use AI moderation only for a fixed, low-risk feedback guide when consistent administration matters more than exploratory discovery.