10/05/2026
When are AI-generated insights reliable enough to inform a real decision?
As synthetic respondents, digital twins and AI-generated simulations become more common in market research, that question is increasingly important. New ARF and MSI research shifts the focus from whether synthetic data is simply “good” or “bad” to something more practical: How should it be validated for the decision at hand?
The research identifies three recurring risks to stress-test for: bias concentration, loss of meaningful variability and sensitivity to changes in prompts, models or other inputs. It also shows why matching human responses is only the beginning of validation—not the end.
The larger takeaway: the validation burden should rise with the stakes. Synthetic evidence may be well suited to exploration or early screening, while consequential decisions call for stronger benchmarking and human or real-world evidence before the final call.
Today at , the ARF’s Tracy Adams, Ph.D., and MSI’s Keith Smith, Ph.D., are sharing this research and a practical framework for determining when synthetic evidence is—and isn’t—decision-ready.
Learn more: https://ow.ly/eIo950ZU16t
