Artificial intelligence is transforming pharmacovigilance — improving signal detection, automating case processing, and speeding up literature review. But for most safety organizations, the hardest part isn't the model. It's the database underneath it.
Case-management systems in pharmacovigilance were largely designed in an era when compliance meant structured forms, fixed fields, and predictable batch processing — not free-text narratives, streaming social listening data, or model outputs that need to be explainable to a regulator. Retrofitting AI onto that foundation surfaces problems long before a model ever gets to make a prediction.
What this means for safety teams
None of this makes AI in pharmacovigilance a bad investment — the opposite is usually true once the data foundation is sound. But the organizations getting real value from it are the ones that treated data integration as its own project, staffed and budgeted separately from the modeling work, rather than as a two-week task on the way to a demo.
