The article explains how artificial intelligence is transforming the generation and application of real-world evidence within Medical Affairs, shifting it from a complex, data-heavy process into a streamlined, insight-driven function. As real-world data from diverse sources such as EHRs, claims, and patient-reported outcomes continues to expand, AI technologies—including machine learning and natural language processing—enable the structuring, analysis, and interpretation of fragmented datasets at scale. By enhancing capabilities in areas such as safety signal detection, patient journey mapping, and comparative effectiveness research, AI allows Medical Affairs to generate deeper, more representative insights that reflect real clinical practice. Supported by improved data standardization, predictive analytics, and cross-functional collaboration, RWE evolves from a retrospective exercise into a proactive, strategic asset. Despite challenges related to data quality, bias, and regulatory acceptance, the integration of AI positions Medical Affairs as a central driver of evidence-based decision-making, continuous insight generation, and patient-centered healthcare innovation.