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Research guide · 1 min read

Evaluate AI and Synthetic Responses in B2B Research

Separate AI-assisted research tasks from evidence supplied by human participants, and define what must be validated.

Published

Illustration of artificial intelligence and connected data

Artificial intelligence can assist with research tasks, and some studies use AI to moderate interviews or generate simulated responses. These are different uses with different limits. A transcript produced during an interview with a real participant is not the same kind of evidence as an answer generated for a hypothetical persona.

Specify the task before selecting the tool

Identify whether the proposed use is administration, transcription, coding, moderation, stimulus testing, or generating synthetic responses. Define what the team will review and what decisions the output will influence. Tool capability and contractual terms need to be checked for the actual project; capabilities and terms can change over time.

For AI-assisted analysis, retain a traceable connection to the underlying research material. A concise summary can omit disagreement or merge different contexts. Review outputs against source responses before presenting them as findings.

Treat synthetic responses as generated material

Synthetic output may help a team explore possible questions or test a process. It should be labeled clearly and kept distinct from recruited participant responses. Plausible language does not establish that a real buyer has experienced a problem or would make a particular purchase.

Where synthetic material is proposed for a substantive decision, ask what real-world data supports it, how it was evaluated, which audiences were represented, and where the validation does not apply. Narrow professional roles and recent purchasing experiences can be particularly difficult to verify from generated answers alone.

Plan human review and participant support

  • Check how research data will be handled before entering it into any tool.
  • Explain the session format and recording arrangements to participants.
  • Test whether automated follow-ups handle uncertainty and confidentiality boundaries appropriately.
  • Provide a human contact for technical problems or concerns.
  • Report the use of AI and distinguish generated interpretation from participant evidence.