818 case studies, 48 vendors.

Every named use case, customer story and success story we could find published on a vendor’s own site — not press coverage, not aggregators, not our summary of them. Organised the way we organise everything else here: by where the work sits in the AI use-case lifecycle. The customer and the technologies named in each story are tagged below its headline; unnamed customers are the vendor’s own choice to anonymise, not a gap in the research.

Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.

Named examples from the foundation-model labs and platforms pharma teams use to actually construct, train and generate the model or application.

Named examples from the vendors pharma teams use to get an AI system live inside a real, regulated production workflow — validation, CRM, safety and clinical systems of record.

Named examples from the tools pharma teams use to monitor, govern and keep validating an AI system once it is already live in production.

Named examples from the real-world-data, claims and competitive-intelligence providers pharma AI systems are trained on, benchmarked against, or run inside of.