Can AI read the room? Predicting policy sentiment with synthetic personas

Learned a lot lending an editorial hand here:

PwC Strategy& Ideation Center, September 26, 2026

by Dima Sayess, Fatima Koaik, Robin Schnider, Pujen Shrestha, George Farajallah, and Dario Krpan





For policymakers, understanding how the public is likely to respond to a new or reformed policy is crucial to that policy’s successful implementation and uptake. Yet measuring policy sentiment through surveys and experiments is often slow, costly, and challenging to scale— especially when the room that policymakers are trying to read is an entire country. The burgeoning field of generative AI presents a new approach to this persistent challenge through the use of synthetic personas.

Synthetic personas are artificial entities generated by large language models (LLMs) to simulate human responses. Studies have found that they can mimic human decision-making with a moderate to high degree of accuracy, and they offer a tool for exploring public responses when traditional approaches are constrained by time, cost, or feasibility. Now, Strategy&’s Ideation Center has extended this research for Middle East policymakers by studying the ability of synthetic personas to predict policy sentiment in three Gulf countries. Read the rest here.