Definition
User and Workflow Research User and workflow research studies how people actually complete tasks, make decisions, recover from exceptions and coordinate handoffs in context. In AI product work it maps where information is missing, where judgment is required, where automation could help and where model errors would interrupt trust, safety or accountability.
User and workflow research studies actual tasks, handoffs, exceptions, information needs and workarounds. AI should fit or deliberately reshape a real workflow.
Jobs to be done describe the progress a user seeks rather than starting from a feature. This helps distinguish the underlying need from a preferred implementation.
Engineering Mode
Workflow research affects where AI belongs in the interface and architecture. Engineers learn which tasks need automation, suggestion, review or explanation. Design must fit the AI into real interruptions, handoffs and accessibility needs. Build work captures events and feedback at the right moments. Operations watch adoption, overrides and failure recovery. The risk is designing for an imagined user who never appears in production.
Evaluation check
Ask what observable evidence would show that this concept has been applied well in a real system, repository or learning exercise.
Real Example
AI writing assistants in Google Docs or Microsoft Word depend on workflow research. Teams need to observe where writers pause, accept suggestions, reject tone changes, collaborate with editors and recover from bad drafts. The AI should appear where it reduces friction, not where it interrupts judgment.
Related Concepts
Further study: Erika Hall, Just Enough Research , and Indi Young, Mental Models .
