AI can help you sort and compare research material. The useful part is keeping every theme connected to the observations behind it.
Start with a decision question.
“Summarize these interviews” is broad. “What prevents new administrators from inviting their team in the first week?” gives the synthesis a purpose. Write the decision you expect the research to inform, the people it concerns, and what remains uncertain.
Prepare a small evidence set.
Use material your organization has approved for the chosen tool. Remove unnecessary personal information. Give each source an ID and retain enough context to interpret it: participant role, task, date, and where the observation came from.
Keep observed behavior, a participant's explanation, and your interpretation in separate fields. A customer saying “I would use this” is different evidence from watching them complete a task.
Ask for structure before conclusions.
Try this instruction: “Group these observations by the obstacle they describe. For each proposed theme, include source IDs, supporting excerpts, contradictory evidence, and unanswered questions. Mark interpretations explicitly. Do not create quotes or fill gaps.”
Check the output against the original material. A convincing theme with an incorrect source is not usable evidence. A repeated phrase is not automatically the most important problem.
Look for disagreement.
Compare new and experienced users, different roles, and successful and unsuccessful attempts. A theme that applies to one group may not apply to another. Preserve outliers that could change your decision rather than asking the tool to smooth everything into consensus.
Turn a theme into a testable decision.
Write a short chain: observation, interpretation, proposed change, uncertainty, and next test. For example: administrators pause at permissions; we suspect unclear consequences; test a clearer invitation preview; measure whether people can explain access correctly and complete an invitation.
That is a hypothesis to investigate, not a proven explanation or a guaranteed improvement.
Use a quality check before sharing.
- Can another person find the supporting evidence?
- Are conflicting observations visible?
- Are the sample and research limitations stated?
- Does the recommendation answer the original decision question?
- Is there a clear next step if the hypothesis is wrong?
AI speeds up organization. Your team remains responsible for interpretation, customer context, and the decision.
Put this into practice.
Start with the free worksheet or talk with Ashley about your specific challenge.