Most conversations about AI in regulated life sciences start with accuracy. How often is the model right? What is the error rate? Has it been benchmarked? These are fair questions. They are also, in most cases, the wrong place to start.
The question that decides whether an AI use is safe, and whether it will survive an inspection, is simpler. When the AI produces something, what happens next? Does a qualified person review it and decide, or does the output go straight into a record, a release or a submission?
Two uses, same model
Take one model and put it to work in two ways.
In the first, it reads a batch record and flags entries that look incomplete or out of limits. A reviewer looks at every flag, decides what each one means, and also checks a sample of the records the model did not flag. The reviewer signs.
In the second, the same model reads the same batch record and marks it as reviewed. Nobody looks at the records it passed.
The model is identical. Its accuracy is identical. The risk is completely different. In the first case, a model error is caught by a person whose job is to catch it. In the second, a model error becomes a released batch.
This is why regulators keep coming back to the same ideas. FDA's 2025 draft guidance on AI in regulatory decision-making describes model risk as a combination of model influence and decision consequence. The discussions around the draft EU GMP Annex 22 talk about human oversight and the role the AI plays in the decision. The words differ. The idea is the same: risk lives in the decision, not in the model alone.
Why accuracy-first thinking goes wrong
Starting with accuracy leads teams into two traps.
The first is chasing a number that cannot be high enough. No model is right every time. If the plan depends on the model being right, the plan fails the first time it is wrong, and everyone involved knew that would happen eventually.
The second trap is subtler. A highly accurate model can make human review worse. When a reviewer sees ninety-nine correct outputs in a row, the hundredth gets less attention. Review that was real at the start becomes a signature over time. The better the model, the more deliberately you have to design against this.
Design the decision, then test the model
A better order of work looks like this.
First, write down exactly what the AI does and what happens to its output. Not "supports quality decisions" but "drafts deviation summaries that the deviation owner edits and approves". This is the intended use, and it is the foundation for everything else.
Second, decide who makes the GxP decision, and make that decision visible. The record should show what the AI produced, what the person changed, and that the person approved it. If the system cannot show this, the human decision is invisible, and an inspector will reasonably ask whether it happened.
Third, test the model against that use. What error rate is acceptable given that a person reviews every output? What kind of error would a reviewer be likely to miss? Those are the errors to test hardest.
Fourth, watch the review itself. If reviewers accept almost every AI output unchanged, find out why. It may be a very good model. It may be review that has quietly stopped.
What this means for buying AI
Quality leaders evaluating AI tools can use this as a filter. Ask any vendor three questions.
What does the AI produce, and is it a draft or a decision? Can I see, in the record, what the AI did and what my person did? What happens when the AI is switched off?
A vendor with good answers has thought about regulated use. A vendor who answers with accuracy figures has thought about the model.
The point
AI will be part of how regulated work gets done. That is no longer a forecast. The companies that use it well will not be the ones with the most accurate models. They will be the ones that decided, clearly and in writing, where the AI stops and the qualified person starts, and then built systems that make that line impossible to blur.
Start there. The accuracy conversation is much easier once that line is drawn.
For the practical detail, see what to validate when your validation software uses AI.
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.