Clinical Development has always been Data-Rich and Insight-Poor. That's finally changing. For most of the last decade, "AI in clinical research" meant back-office automation and document search. That era is over. AI now sits much closer to the science itself — in how studies are monitored, how safety signals surface, how evidence is assembled, and how submissions are built. Consider what is already running in clinical programs today: language models that turn thousands of unstructured source records into consistent, review-ready patient narratives. Live dashboards that fuse enrollment, labs, adverse events and protocol deviations into a single view of a study — and a single view of a site. NLP pipelines that read years of free-text clinical documentation and make it structured, queryable evidence. None of this is experimental anymore. It is becoming core infrastructure for how trials are run and how drugs, devices and diagnostics reach approval. Why now? The data was a...