AI in Pharmaceuticals

Novartis deployed Market Logic Software's Sherlock + DeepSights generative-AI platform to centralize market insights across 7,500 internal users, cutting primary research spend by 56% ($30M annually) and saving two hours per major research query.

Bayer GBS automated its SAP purchase-requisition-to-order workflow with UiPath RPA plus generative AI exception handling, cutting procurement errors by 70% across peak volumes of 900 purchase requests per day.

Sanofi's Quality Regulatory Science team is scaling Plai, an in-house AI quality-intelligence tool, from a deviation-trending pilot to a global deployment across multiple manufacturing sites. Built on the RAISE (Responsible AI at Sanofi Enterprise) governance framework, Plai accelerates investigations, generates root-cause hypotheses, and integrates QMI and QRE risk metrics to surface predictive insights before deviations occur.

Merck deployed an internal generative AI platform leveraging LLMs to draft clinical study reports, cutting fully human-reviewed CSR draft time from 2-3 weeks to 3-4 days and accelerating the delivery of medicines to patients.

Bayer built E.L.Y. Crop Protection (E.L.Y. Mini) — a small language model on Microsoft Phi and Azure AI Foundry, fine-tuned on Bayer's proprietary label data — to give agronomic advisors precise, compliant answers. Early users report 5-10% productivity gains; complex questions resolved in <30 seconds vs days.

Bayer's PRINCE — an agentic RAG system built on LangGraph — lets preclinical researchers query decades of structured and unstructured study data through natural language. A multi-agent workflow (Clarify, Think & Plan, Researcher, Reflection, Writer) chains context-disciplined steps with daily live-t

Bayer China built Dr. Bei, an LLM-based generative AI training system on AWS, to train pharmaceutical medical representatives. The system delivers intelligent dialogue and personalized evaluation for role-play coaching, saving 6,000-7,000 training hours per year and improving 1-on-1 role-play training efficiency by more than 50%. The system reached 1,000 medical representatives within 72 days of initiative launch.

In October 2025 Eli Lilly and NVIDIA announced a strategic collaboration to deploy the world's first NVIDIA DGX SuperPOD with DGX B300 systems — an AI factory of 1,016 NVIDIA Blackwell Ultra GPUs delivering over 9,000 petaflops of AI performance, equivalent to ~7 million Cray supercomputers per GPU. The factory trains large biomedical foundation and frontier models for drug discovery, runs NVIDIA BioNeMo for molecular and antibody design, Omniverse digital twins of manufacturing lines, Isaac robotics in factories, NeMo-based AI agents in labs, and MONAI for imaging. Selected models are exposed externally via Lilly TuneLab, a federated-learning platform providing biotechs access to over USD 1 billion of Lilly's proprietary drug-discovery data, now also offering NVIDIA Clara open foundation models. The deployment is anchored by Lilly's USD 50B U.S. manufacturing and R&D expansion including the $4.5B Medicine Foundry in Indiana.