Major players like Eli Lilly, in collaboration with Nvidia, believe AI can boost development success rates, but the core challenge of discovering new therapeutic molecules remains unsolved by AI.

AI is viewed as augmentation of human intelligence, with gains varying by deployment and with measurable impacts on timelines and costs rather than full automation.

Analysts estimate agentic AI could raise clinical development productivity by about 35% to 45% over the next five years, though precise savings depend on deployment.

While the potential is substantial, measuring AI’s real impact on timelines and costs remains contingent on how deployment unfolds.

ITM is piloting AI to convert lengthy trial reports into FDA template formats, potentially saving weeks of staff time, with deployment not yet complete.

AI is speeding up practical drug development tasks—from choosing clinical trial sites to recruiting participants and drafting regulatory documents—among both large pharma and smaller biotechs seen at the JP Morgan Healthcare Conference.

Teva is applying AI across multiple areas to push new drugs to market, focusing on efficiency in essential but less glamorous activities.

Industry executives say measurable AI benefits will likely emerge in one to three years and depend on how and where AI is deployed.

Industry optimism mirrors McKinsey’s view that agentic AI could deliver roughly a one-third to nearly half uplift in productivity, but actual impact will vary by approach.

Notable quotes emphasize AI handling the less glamorous efficiency tasks and augmenting intelligence, while true AI-generated drug discovery remains an open question.

Executives stress AI’s role in supporting trial planning and regulatory prep rather than replacing human expertise at this stage.

Examples include Alleviate Health’s AI-driven patient outreach, Genmab using Claude to automate post-trial outputs, and ITM turning long trial reports into FDA-ready templates.