A brave new world: the rise of agentic AI in rapid cycle RWE analytics

Real-world evidence (RWE) has become a critical asset across drug discovery and market access, with regulators accepting evidence from real-world data (RWD) to supplement negotiations. With increasing demand for rapid RWE generation across the product lifecycle, agentic artificial intelligence (AI) stands to accelerate study timelines, reduce costs, and reveal untapped insights.

A narrative literature search was conducted across Google Scholar, restricted to articles from 2016 onwards. This commentary traces how RWE analytics has evolved from traditional programming practices to analytically rigid point-and-click platforms to agentic AI. This progression reflects the industry's increasing need for rapid, reliable insights and highlights how agentic capabilities can streamline existing workflows and reshape team structures while still upholding strict governance and human oversight. Agentic AI-driven rapid analyses promise substantial time and cost savings, and their adoption is on the horizon. Regulatory approval processes will continue to evolve over the coming years before these solutions become widely integrated across the industry.

AI-driven RWE generation is still nascent but has transformative potential for the future of the industry. Widespread adoption will depend less on technical feasibility and more on trust, governance, and traceability. With agentic AI executing studies, human judgment is integral.