Generative AI Use Cases: Lessons from Pharmaceutical R&D
The first time our discovery group tested Generative AI Use Cases, the excitement centered on molecule generation. Within weeks, however, the more valuable lesson emerged elsewhere: the technology was most useful when it helped scientists navigate evidence, challenge assumptions, and prepare decisions without obscuring scientific accountability. That distinction matters in research-based biopharma, where a persuasive output cannot substitute for experimental confirmation, GxP controls, or an expert willing to defend the conclusion. A practical review of Generative AI Use Cases shows how widely the technology can contribute across discovery, clinical development, pharmacovigilance, regulatory affairs, and manufacturing. Our experience taught us to evaluate that range as a series of controlled workflows rather than one enterprise-wide transformation. Each workflow has different evidence requirements, failure consequences, source systems, and human reviewers. Treating them as equivalent ...