TL;DR
The FDA is opening public comment on AI model monitoring, signaling that the regulator is moving faster than most pharmaceutical companies. While many organizations still reconcile spreadsheets, the FDA is building AI assessment infrastructure. The gap between regulatory AI-readiness and industry data maturity is widening. The new generation of vertical AI learns directly from natural language — protocols, batch records, narratives — rather than forcing information into rigid schemas. The real race is in development, and it's already underway.
This week, the FDA opened public comment on how it plans to monitor and evaluate AI models in the real world — another decisive move to embed AI into how science and regulation actually work.
For anyone in drug development or manufacturing, this isn't just a policy update. It's a signal that the regulator is moving faster than many of the companies it oversees. While most are still reconciling spreadsheets, the FDA is building the infrastructure to assess AI systems that will soon review those very documents.
That gap — between where regulators are heading and where industry data still lives — is widening.
For years, "digital transformation" meant turning paper into PDFs. But the current state of AI has moved far beyond that.
The new generation of vertical AI isn't about forcing information into rigid schemas or predefined data models. It's about learning directly from the natural language of work — the protocols, batch records, and narratives that scientists and QA teams already write every day. Instead of translating everything into fields and tables, these systems now build living context around human reasoning itself.
In practice, this means development teams no longer need to adapt their workflows to fit the machine. AI adapts to them. It understands how context, provenance, and intent connect across documents — how a deviation links to a batch record, or how a stability study justifies a shelf-life claim. The result is data that's naturally structured through human language, traceable by design, and ready for machine and regulator alike.
The question isn't whether AI will change how we work — it's whether our data will be ready when it does.
Discovery will always be exciting. But the real race now is in development — and it's already underway.
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