The emerging world of AI-powered simulation sandboxes could provide an opportunity test how a client would react to a message before it’s even sent. With AI simulation sandboxes, which EY Consulting’s Sameer Munshi, head of behavioral science and simulation, compared to crystal balls, advisors can “recreate” any demographic in the world via synthetic data and agentic AI. “It’s recreating the parameters of a human,” he said. “It’s decoding human behavior based on how you describe it.” Once that simulation is set up, Munshi said advisors can interact with these “people” in a qualitative conversation, posing targeted questions in order to test what messaging — or even a new price point — resonates with a particular client population. The immediate benefits of the AI simulation sandbox are obvious, said Munshi. “The power of this technology is understanding what investors or consumers actually want, even if it’s not a perfect correlation to what exists today from a research perspective,” he said. “The fact that you can get it in days instead of months is going to completely change how we think about research.” Compliance is an important use case for AI-powered simulation environments, said William Trout, director of securities and investments at technology data firm Datos Insights. In terms of investment suitability, simulations could test portfolio recommendations against diverse client profiles, ensuring recommendations truly serve client interests rather than advisor compensation structures, he said. The next step for firms considering adoption of these sandboxes is to begin with a narrow pilot program that uses nonsensitive data. For example, an advisory team could test how an AI-generated client reacts to a quarterly market commentary before distributing it. While AI adoption among financial advisors has increased dramatically in 2025, the specific concept of comprehensive simulation environments for testing client reactions is “pretty nascent,” said Trout, who has not seen many deployed use cases for AI-powered simulation sandboxes in wealth management. Such sandboxes enable risk-free experimentation with client interactions, marketing strategies and portfolio recommendations in controlled environments, said Trout. “They accelerate client growth by helping advisors identify high-potential prospects and refine outreach strategies that improve conversion rates and retention,” he said. “Enhanced productivity comes from offloading routine tasks like meeting preparation, follow-ups and client research to AI agents, allowing advisors to focus on high-value relationship building and strategic planning.” Another example of how advisors can use sandboxes is determining whether to raise fees, and if so, how much, said Munshi. Advisors can also use the technology to decide how to invest in ads, said Munshi. The sandboxes also provide continuous learning opportunities through data generation that trains models and refines strategies over time, said Trout.