Even before the boom of AI following the launch of generative AI platforms in 2022, AI was already reshaping clinical research and drug development, with early adopters active by 2012 and improvements in its capabilities thereafter. However, since GenAI models have been released, there has been increased potential for AI models to embed themselves in the daily activities of clinical development, dependent on life sciences companies’ appetite for innovation and risk. In recent years, volumes of data are increasing, timelines are short and review cycles require clarity and traceability in clinical research. Human expertise remains central, yet there are many processes that consist of repeatable steps and document generation. This article highlights how AI can support biometrics practices in clinical data management, biostatistics, programming, medical and regulatory writing and trial designs and how initiatives can remain inspection-ready at submission.