Investing.com -- Jefferies released a research report Tuesday examining artificial intelligence adoption across pharmaceutical services companies, based on analysis of 42 expert call transcripts from the past 90 days.

The firm used AI tools to review commentary on AI implementation across contract research organizations and drug development processes. Expert identities were kept anonymous with only credentials provided.

The analysis found AI produces efficiency gains that vary by development stage. Early discovery and regulatory writing showed cost savings of approximately 40% to 50%, while preclinical workflows at CROs and contract development and manufacturing organizations saw time reductions of about 40% to 50%. Later-stage clinical trials showed more modest savings of roughly 10% to 20% due to continued requirements for human validation.

Specific applications showed notable efficiency improvements. Request for information automation reduced timelines from approximately three days to 10 minutes in customer acquisition processes. Data management and biostatistics automation cut programming hours by about 30% during trial setup. Synthetic cohorts could potentially reduce patient enrollment by approximately 50% in certain trials and overall trial costs by more than 30%.

The report indicated that while AI creates margin benefits for CROs by reducing labor costs, competitive pressures lead companies to share these savings with clients. Experts estimated that approximately 50% of AI-driven savings are retained on average, with some competitors passing through larger portions to secure business.

CROs are using AI to expand capacity and pursue additional programs, particularly with mid-sized and small-cap clients. Workforce changes are occurring, with expected reductions of 10% to 20% in junior positions and 10% to 15% overall workforce reductions over time. Experienced employees remain necessary to validate AI outputs.

Approximately 50% of contracts are shifting toward milestone-based or value-based models, moving away from traditional fee-per-employee or hourly pricing structures toward outcome-based frameworks with greater risk-sharing.