Jefferies published a research note Tuesday that synthesizes expert commentary on artificial intelligence deployment across pharmaceutical services, drawing on 42 anonymized expert call transcripts collected over the past 90 days. The firm applied AI tools to parse and organize those discussions, and provided credentialed but anonymous attribution for sources.
The review found that the efficiency gains tied to AI vary substantially by stage of drug development. Early-stage activities - including discovery and regulatory writing - showed estimated cost reductions of approximately 40% to 50%. Preclinical workflows at contract research organizations (CROs) and contract development and manufacturing organizations (CDMOs) similarly reported time savings in the neighborhood of 40% to 50%.
By contrast, later-stage clinical programs are realizing more modest benefits. Experts cited time and cost reductions of roughly 10% to 20% in late-phase trials, a level constrained by the continued need for human validation of AI outputs and traditional clinical operations.
Several concrete applications were highlighted as driving these efficiency gains. Automation of requests for information in customer acquisition processes was said to cut typical timelines from approximately three days to 10 minutes. Automation in data management and biostatistics reduced programming hours by about 30% during trial setup. The use of synthetic cohorts was described as a potential mechanism to reduce patient enrollment needs by approximately 50% in certain trial designs, with corresponding overall trial cost declines of more than 30% in those scenarios.
Jefferies noted that while AI can reduce labor intensity and therefore create margin upside for CROs, competitive dynamics are prompting many providers to pass some savings back to clients. Experts estimated that CROs retain about 50% of AI-driven savings on average, with some competitors choosing to pass through a larger portion of savings to win or defend business.
Contracting models are also shifting. The report found that roughly 50% of contracts are moving away from traditional fee-per-employee or hourly pricing toward milestone-based or value-based arrangements that incorporate more explicit risk-sharing and outcome orientation.
Operational capacity and headcount effects were discussed as well. CROs are reportedly using AI to expand program throughput and to pursue more assignments, particularly with mid-sized and small-cap clients. Workforce changes noted by experts include expected reductions of 10% to 20% among junior positions and an anticipated 10% to 15% reduction in overall headcount over time. At the same time, experienced staff remain necessary to validate and interpret AI results.
The Jefferies analysis aggregates practitioner views without identifying speakers by name and relies on AI-assisted processing of the source material. The firm’s findings map specific use cases to estimated efficiencies across the development lifecycle, while also flagging commercial and workforce implications as firms integrate these tools.
Impacted sectors: Pharmaceutical services, clinical research, contract manufacturing, biotech R&D operations, and healthcare data analytics.