01
Start with use, not capability.
The model is rarely the first question. The work, decision, person and consequence usually are.
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Approach
Build only after the problem gets sharper.
I use design to connect real needs, small tests and practical learning. The aim is responsible everyday use, with evidence of what changes and who benefits.
PROCESS
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01
Understand
Explore the existing material, workflow, people, constraints, data reality and strategic goal before treating AI as the answer. I look for role-specific needs, fears, non-goals and the language people already use to describe the work.
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02
Frame
Turn the fieldwork into sharper use cases and testable hypotheses. Prioritize opportunities by business value, feasibility and risk. Make dependencies visible: definitions, data access, validation and ownership. Agree on what useful change would look like and how actual use and value will be followed up.
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03
Prototype
Test the smallest useful version of the workflow with the people who will use it. That may mean an existing tool, a changed working routine or something built for the task: a prompt flow, structured knowledge file, module or interface. Use real work to expose trust, missing data, unclear definitions and whether the approach helps.
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04
Adopt
Help people build confidence through practical examples, shared learning and clear ownership. Follow how AI is used, what changes in the work and whether it creates value. Use those findings to improve the workflow, address barriers and share what others can reuse.
These steps repeat as use reveals new needs, constraints and opportunities. Return to the problem, adjust the frame and test again as the work changes.
PRINCIPLES
02
Prototype to expose judgment.
A working draft reveals what people trust, misunderstand, avoid and need to decide before scaling.
03
Treat adoption as design.
Design the conditions for use: learning, ownership, support and feedback. Follow what changes in practice, then adjust.