Agentic AI cuts drug development timelines: report

Healthcare GenAI market seen growing to $22b by 2027.

Agentic artificial intelligence is accelerating documentation-heavy processes in biopharma, potentially shortening drug development timelines in an industry where bringing a therapy to market can take up to 15 years and cost billions, according to Boston Consulting Group (BCG).

BCG said rising manufacturing costs, competition from generics and fast followers, and one of the steepest patent cliffs in history are straining traditional business models, making efficiency gains increasingly critical.

Documentation-intensive tasks such as clinical trial protocols, quality reporting, and regulatory submissions are among the primary targets for AI-enabled automation.

In one case, BCG developed a multi-agent AI system for a global pharmaceutical company to reduce the time required to produce clinical trial protocols, a process that previously took teams of medical writers up to six months for a single document.

The system integrates internal content management platforms and regulatory systems with external scientific databases, combining generative AI with governance controls to maintain terminology accuracy, reference citations, and regulatory compliance.

BCG projects that the generative AI market in healthcare will grow at a compound annual rate of 85%, expanding from $1b this year to $22b by 2027.

Approximately 25% of biopharma companies report that AI has delivered cost reductions and revenue increases of at least 5%, alongside gains in speed and agility.

In research and development, generative AI tools can reduce early-stage drug discovery timelines by 25% or more by supporting in silico identification and optimisation of small and large molecule candidates.

Commercial functions are also seeing impact, with AI-enabled personalised materials for physicians and patients linked to revenue increases of up to 10% and reductions in external agency costs of 25%, BCG said.

The report argued that companies adopting agentic AI at scale are likely to gain a competitive advantage as margin pressures intensify across the value chain.