CVS Health is running more than 100,000 “agentic twins”—AI models built from nearly three million consented survey responses gathered via AI-moderated interviews with over 400,000 people—to simulate real customer behavior. By prompting these digital twins, the customer experience and insights team can run compact studies on messaging, product testing and A/B tests in as little as 15 to 30 minutes, down from the four to six weeks a traditional focus-group approach would take. CVS reports that these simulations hit an 85–95 percent accuracy rate compared with live studies, and they open up research on hard-to-reach groups like immunocompromised patients. Before agentic twins, CVS used standard machine-learning models to predict customer actions. Now it can explore not just what customers will do, but why. The twins were developed in partnership with Simile, a simulation-AI startup partly backed by CVS Health Ventures. Each twin carries demographic details—age, prescription habits, even political affiliation—and answers detailed questions about experience, letting teams tweak wording or features before launching to real customers. CVS also puts strict governance in place. Human leads review all prompts and results to prevent bias or misinterpretation. If a twin’s response deviates from expected outcomes, researchers don’t discard it—they investigate. This hands-on oversight guards against privacy risks, says Hopara co-founder Ricardo Mayerhofer, because agentic twins could otherwise reveal individual preferences or plans. Looking ahead, CVS plans to grow its agentic-twin population and use it for “dress rehearsals” of new services. By testing in a confined virtual setting, product teams refine user flows and identify problems early. That way, when new offerings roll out to customers, CVS runs on data-driven confidence rather than guesswork.