Workflow-First AI for Drug Development

This is the core wedge for AI in drug development, because the expensive work is not storing documents, it is spotting gaps across a chain of interdependent documents before they turn into amendments, redlines, and review delays. Systems like Veeva and IQVIA keep the official record, but teams still manually compare protocols, site packets, specs, methods, and submission sections to figure out what is missing and who should fix it next.

• Protocol amendments are common and costly, with Tufts research estimating substantial direct costs and FDA data showing first cycle approval is far from automatic. That makes document coordination a real budget and timeline problem, not just an admin nuisance.
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• The reason this was under addressed before AI is that rules lived in people, SOPs, and scattered files. A human had to read across Word docs, spreadsheets, and quality systems to catch drift. Models now make that comparison work cheap enough to run continuously, document by document.
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• That also explains why incumbents did not solve it fully. Veeva built regulated content systems like eTMF, QualityDocs, and RIM, while IQVIA built clinical data and service workflows. Neither was built primarily to draft the next artifact, trace inconsistencies, and route fixes with source linked evidence.
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The market opens up as AI turns review from a periodic human fire drill into a continuous software layer on top of the system of record. The winners are likely to be products that become part editor, part reviewer, and part workflow coordinator, then expand from protocol and CMC work into broader regulatory and operating document flows.