Generative AI Use Cases: Best Practices for Pharma Leaders
Experienced pharmaceutical teams no longer need another demonstration of fluent document generation. They need evidence that a generative system can operate inside real scientific and regulated workflows without obscuring provenance, weakening review, or creating a second layer of manual reconciliation. The central challenge is therefore not model access. It is designing a controlled system that respects compound, study, patient, submission, product, and batch context while producing measurable improvements in cycle time and decision quality. The most valuable Generative AI Use Cases are rarely generic copilots. They are purpose-built workflow interventions: a target-evidence assistant grounded in internal reports, a protocol design workspace that exposes precedents and contradictions, a safety application that structures case information, or a CMC authoring tool tied to approved data and document versions. Each succeeds or fails according to how well it fits existing scientific revie...