WORKFLOW DESIGN · 5 min read
Where agentic AI fits in market research
A practical guide to choosing an agentic market research workflow, defining researcher review points and evaluating the output.
A useful starting point for agentic AI in market research is a recurring study task with a clear finish line. Before choosing a model or an agent framework, describe the questionnaire, fieldwork, analysis or reporting work the team needs to complete.
Start with the handover
Look for a process that repeatedly moves between people or systems. A brief becomes a project specification. Fieldwork becomes a QA queue. A validated dataset becomes a reporting pack. These handovers are often easier to define and evaluate than a broad instruction to ‘generate insights’.
Describe the input and the output
List the information a workflow is allowed to use, the format it receives and the deliverable it must produce. State what should happen when data is missing or a tool fails. A useful automation has an observable outcome and a clear exception path.
Separate rules from interpretation
Use explicit rules for checks that have known answers, such as a required field or a routing condition. Use AI where language or context needs interpretation. Send consequential decisions, including methodological choices and exclusions, to the person responsible for them.
Evaluate before expanding
Compare the workflow against representative examples reviewed by your team. Measure quality and completion time, including the time spent checking and fixing outputs. Include unusual cases, missing information and tool failures rather than testing only the easiest examples.
Build for the person who will own it
A production workflow needs documentation, a visible status, a way to correct errors and an owner. The goal is a process the team can operate and understand after the build is complete.