Maximizing the Power of Real-world Evidence (RWE): AI’s Role in Accelerating Life Sciences’ Next Era

Life sciences enterprises are increasingly turning to Real-world Evidence (RWE) as an essential input for decision-making across the product life cycle – from early-stage R&D to post-market access and safety. RWE offers validated insights into treatment effectiveness, patient outcomes, and safety, but fragmented data sources, inconsistent quality, and evolving compliance expectations often challenge its generation. With the rising volume and diversity of Real-world Data (RWD), traditional analytics approaches are no longer sufficient.

AI, including technologies such as NLP, machine learning, and generative models, is redefining how RWE is produced and operationalized. AI is accelerating data curation, enabling predictive analytics, and delivering regulatory-grade evidence at scale. This Viewpoint outlines how AI is transforming the RWE landscape across six domains: drug discovery, clinical trials, manufacturing, commercialization, pharmacovigilance, and regulatory affairs. It also explores emerging models such as insights-as-a-service and autonomous evidence networks, which offer scalable, modular engagement approaches for AI-powered RWE.

The report provides practical recommendations for both enterprises and providers, covering capability investments, infrastructure modernization, governance models, and partnership strategies. It aims to help stakeholders reimagine their data-to-evidence journeys and build future-ready ecosystems for continuous, AI-enabled insight generation.