AI in life-sciences operations: where it helps, where it must not

Life sciences is a useful stress test for applied AI, because the cost of a quiet error is high and the parts that are regulated are not negotiable. The right question is never "can AI do this" but "where is it safe to let it, and how would we catch it when it is wrong".

The operational surface is large: research operations, trial administration, the paperwork around approvals, and the processes that surround patient care and supply. Much of that is document-heavy, repetitive and slow, exactly the kind of work where a well-guarded tool removes a handoff rather than a judgement.

The line to hold is the one around decisions that must stay human and audited. AI can assemble a submission, reconcile records, or draft a first version; it does not get to make the call that a regulator or a clinician owns. Build the guardrail, the log and the maker-checker step before you build the tool.