Batch record review is slow, repetitive and important. A reviewer checks hundreds of entries for completeness, correct values, signatures, and anything that should have triggered a deviation. It is exactly the kind of work where AI can help, and exactly the kind where help has to be designed carefully, because the output feeds a release decision.
Where AI helps
The most useful work is triage. Checking every entry for completeness, flagging values outside limits, matching signatures against training records, spotting corrections without a reason, and comparing timings against what the process allows. With paper records, that starts with reading handwriting, which is now reasonably reliable but not perfect.
The goal is for the reviewer to spend their time on the entries that need judgement, rather than confirming the hundreds that do not.
Review by exception, and its risk
Review by exception, where people look only at what the system flags, is the natural end point and is already common with electronic batch records. With AI, it carries one specific risk: entries the AI should have flagged and did not are never seen by anyone.
So the critical measure is not how many good flags the AI raises. It is how many problems it misses. Testing must focus on that, using records with known problems, and monitoring in use should include periodic full review of a sample of unflagged records.
Who decides
The release decision stays with the qualified person, and the review decision with the reviewer. The AI should present flags with the evidence for each, and record what the reviewer did with them. A reviewer who dismisses a flag should give a reason, just as they would for any other observation.
The draft EU GMP Annex 22 discussions, which we cover in the Annex 22 control strategy, point the same way: controls that keep human judgement where it matters, and evidence that those controls work.
Electronic records first
If your batch records are still on paper, it is worth asking whether to apply AI to paper or move to electronic records first. An electronic batch record already enforces many checks at the point of entry, which removes much of what a reviewer would look for. AI then works on a cleaner, smaller problem. Our guides to EBR software and electronic batch record validation cover that route.
Starting small
Pick one product and one record type with steady volume. Run the AI alongside your current review for a period, without relying on it, and compare. Where did it flag something reviewers missed? Where did reviewers find something it missed? That comparison is the most honest evidence you will get, and it shows reviewers what the tool is good at before they are asked to trust it.
Validating it
Define the intended use narrowly: which record types, which checks, and what the reviewer still does. Build a test set from real records with known issues, including rare ones. Set acceptance criteria for missed problems before you look at results. And plan monitoring from day one. Our GxP AI validation page covers the full approach, and our MES validation work the systems around it.
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.