Clinical trials have never been more important or expensive. A single new drug now takes over a decade and billions of dollars to bring to market and trials account for much of that cost and time. They are also growing more complex: more data, more sites, more decentralised elements and rising regulatory expectations. This is precisely why AI matters now. Applied well, it can compress the slowest, most repetitive parts of the process, screening records, cleaning data, interpreting protocols, drafting documentation and return skilled people to the work that needs them. The difficulty lies in moving from a promising pilot to something that works safely, inside a regulated environment. AI has already reshaped early discovery, protein structure prediction, target identification, molecule design, virtual screening but the dominant cost and uncertainty lie downstream, in demonstrating that a candidate works in humans.