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AI-Moderated Research

Synthetic Data

Synthetic Data

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Definition:

Synthetic data is information generated algorithmically to replicate the statistical patterns or qualitative characteristics of real-world data, without being collected from actual research participants. In the context of AI-moderated research, the term has become increasingly relevant as some vendors use synthetic respondents or AI-generated personas to simulate consumer reactions rather than capturing genuine human input. While synthetic data has legitimate applications in fields like software testing and privacy-sensitive analytics, its use as a substitute for real participant voices in qualitative research raises serious questions about validity, representational accuracy, and the trustworthiness of findings used to inform business decisions. Research-grade qualitative work depends on authentic human expression, not modeled approximations of it.

How Conveo Does It

Conveo does not use synthetic data or AI-generated respondents at any stage of the research process. Every study runs as AI-moderated video interviews with real participants, recruited through Conveo's integrated panel network or a team's own list. Studies can launch in under 30 minutes, with findings ready in days rather than weeks. At enterprise scale, hundreds of real conversations run in parallel, and every insight traces back to a specific person, a verbatim quote, and a video recording.

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Frequently asked questions.

Synthetic data in qualitative research refers to AI-generated content designed to simulate participant responses, rather than capturing what real people actually said or did. Some platforms use synthetic respondents or modeled personas to approximate consumer sentiment at speed. The core limitation is that no algorithm can fully replicate the nuance, contradiction, or unexpected direction that emerges when a real person describes their experience in their own words.
Business decisions informed by research carry real consequences, and the credibility of those decisions depends on whether the underlying data reflects genuine human behavior. Synthetic data introduces a layer of model bias and statistical approximation that is difficult to audit or disclose to stakeholders. When a product team, brand director, or executive acts on findings, they need confidence that those findings came from real customers, not from an AI predicting what customers might say.
Real participant data is collected directly from people through interviews, surveys, or observation, and it carries the unpredictability and specificity that makes qualitative research valuable. Synthetic data is generated by AI models trained on prior datasets, producing responses that reflect patterns rather than individuals. The practical difference shows up in the details: a real participant might express hesitation, contradict themselves, or raise a concern no one anticipated. A synthetic respondent, by definition, cannot.
AI has made it technically straightforward to generate large volumes of simulated consumer responses, which has prompted some vendors to position synthetic data as a faster alternative to traditional fieldwork. This has sharpened the industry conversation about what research-grade actually means. The more credible direction is using AI to improve how real data is collected and analyzed, through adaptive moderation, multimodal analysis, and faster synthesis, rather than replacing real participants with generated approximations.
The most reliable safeguard is choosing platforms that are explicit about their data provenance. Teams should ask vendors directly whether findings trace back to real participants, whether verbatim quotes and recordings are available, and whether any AI-generated content is introduced at any stage of the workflow. Procurement and governance teams increasingly include these questions in vendor assessments, particularly where research outputs inform regulatory submissions, investor communications, or high-stakes product decisions.

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