The QC lab is an obvious target. Chromatography review, stability trending, OOS investigations and method transfer absorb enormous amounts of analyst time, much of it spent reading data that looks exactly like the data they read yesterday.
It is also the most sensitive place in the building to put a machine, because the lab is where most data integrity findings in this industry have originated. That history shapes what is sensible here.
Where it genuinely helps
- Chromatogram review triage. Flagging peaks that need a second look — poor resolution, unexpected shoulders, integration that drifted from the method. The analyst still reviews; the system decides the order and highlights what is unusual.
- Stability trending. Spotting a drift toward a specification limit several timepoints before it would trigger a formal alert. A well-matched problem: lots of numbers, slow-moving signal, costly if missed.
- Finding similar past investigations. "This OOS pattern on this column has happened four times, three of them traced to the same mobile phase batch."
- Method transfer comparison. Comparing two sites' data for systematic differences that a human eye averages out.
- Drafting investigation narratives from the analyst's findings, in the format the SOP requires.
- Audit trail review support. Surfacing the entries that deserve attention from thousands that do not — see audit trail review.
What must stay with the analyst
Integration decisions. Where a peak starts and ends changes the result. This is the single most scrutinised judgement in a QC lab and the one with the longest history of regulatory concern. A system may propose integration; an analyst accepts it, and the record shows who did.
Whether a result is valid. Invalidating a result requires a scientific justification by a qualified person. No automation.
OOS root cause and the decision to retest. The retest decision is exactly where inspectors look hardest, for good reason.
Release. Not a borderline case — a named person certifies.
The pattern is consistent with everything else in regulated AI: the system proposes and prioritises, a qualified person decides, and the decision carries a name. See roles and responsibilities.
Why this area needs extra care
Most published data integrity findings in this industry trace back to laboratory systems — shared logins, audit trails switched off, trial injections run before the reportable result. An inspector arriving at a lab that has introduced AI will reasonably ask whether the new system makes selective reporting easier or harder to detect.
The answer should be "harder", and you should be able to show it. If your tool surfaces every injection including the discarded ones, that is a data integrity improvement you can demonstrate. If it only ever sees the reportable run, it has inherited the blind spot.
Build the question into your requirements rather than discovering the answer later. See data integrity and the recurring lab findings.
Validating it
The lab system itself is almost certainly already validated. The AI layer is a change to it, which means an impact assessment and testing proportionate to what the layer does — triage and prioritisation sit much lower on the risk scale than anything touching a reported result.
Write the intended use tightly: "prioritises chromatograms for review and highlights atypical features; does not integrate peaks, assign results or determine validity" is the kind of sentence that keeps the risk assessment, the testing and the inspection conversation all short.
Method in GxP AI validation, scoping in the intended use statement, and platform context in LIMS validation.
Where to go next
Explore GxP Copilot for AI-native validation, TraceDraft for source-traceable clinical documentation, or book a demo to see either on your own data.