The post-approval phase represents one of the most complex and strategically critical stages in the life sciences product life cycle, requiring enterprises to ensure continuous safety monitoring, regulatory compliance, and real-world performance optimization. With increasing diverse patient populations and real-world conditions, the need for continuous, data-driven insights has intensified. However, traditional post-approval operations, characterized by fragmented data, manual processes, and reactive workflows, are struggling to keep pace with these demands.
In response, the life sciences industry is undergoing a fundamental shift toward intelligence-led lifecycle management. Advances in AI, generative AI, and agentic AI are enabling enterprises to move beyond process automation toward predictive and autonomous decision-making. These technologies are transforming key post-approval functions by enabling real-time safety monitoring, intelligent regulatory operations, and more personalized patient engagement. At the same time, Real-world Data (RWD) and Real-world Evidence (RWE) are on the rise and accelerating this transition, supporting continuous benefit-risk assessment and adaptive lifecycle strategies.
This Viewpoint provides Everest Group’s perspective on how AI is reimagining post-approval operations, shifting the paradigm from oversight to intelligence. It examines current AI adoption, agentic systems, and the structural challenges enterprises must overcome to scale AI effectively. It also highlights Human-in-the-Loop (HitL) models as vital in ensuring accountability, explainability, and regulatory alignment as AI systems become more autonomous. Finally, it offers a forward-looking view on how agentic AI will redefine post-approval ecosystems, enabling predictive compliance, integrated decision-making, and sustained lifecycle value.
