From AI Use Case to Validated GxP Tool

You may already see where AI could improve a process. The harder question is whether the evidence supports using it in regulated work.

GxP is the set of good-practice controls used to protect product quality, data integrity, and patients. In that setting, a claim that an AI model “works” tells us very little. We need to know which decision it can influence, who checks its output, and what happens when it fails.

Recent regulatory material gives this work a clearer shape. FDA and EMA list ten principles for good AI practice in drug development. They call for a clear use, sound data, risk-based checks, and lifecycle management. These principles help, but each organization must apply them to a real task.

Start with the decision

Think about three uses of the same technology. One tool drafts an investigation summary. Another ranks batches for review. A third gives evidence used in a release decision. Each use has a different risk because it can change different work.

The first task is to record how the current process reaches its decision. Who decides? Which evidence do they use? Where do errors appear? How does the process handle doubt or missing information? This baseline gives us something real to compare with the proposed AI-supported route.

FDA calls the AI's defined role its context of use. This means the job it does and the decision its output may shape. FDA's draft guide says the evidence should fit that role and the harm from a wrong result.

Risk management is part of every sound validation approach. AI projects should follow the same rules. The evidence and controls should match the harm a wrong output could cause. A drafting aid needs less evidence than a tool used for product release.

Let the boundary determine the evidence

A useful boundary names the users, process, data, model version, allowed output, and final decision owner. It also states which uses fall outside the plan. A test can look strong while saying little about real work when this boundary is vague.

The proposed EU GMP Annex 22 takes this view for pharmaceutical manufacturing. The draft covers intended use, training and test data, performance measures, human review, monitoring, and change control. It can help firms prepare. Its current status is draft.

The test plan should focus on errors that could change the regulated decision. Test missing data, unusual products, new equipment, weak images, prompt changes, supplier updates, and use outside the approved process. One average score can hide these cases.

I would also ask whether the proposed measure fits the claim. If the tool should speed up review, count both time and the errors reviewers still find. If it should improve decisions, first define the current decision quality. Then test the cases where a wrong answer could cause the most harm.

Approval creates ongoing work

Two recent inspections show why responsibility must stay visible. MHRA found AI-assisted replies with invented references, the wrong regulatory framework, and generic text. In one case, errors raised review time from about four hours to more than 20.

An FDA warning letter gave another example. A manufacturer used AI to help write specifications, procedures, and production records without adequate cGMP review. FDA said an authorized quality-unit member must review and clear such AI output if production resumes. This was one case with many other serious faults. The named human owner remains responsible for the record.

The process owner defines the work and baseline. The technical owner explains the system and data. The validation owner tests the failures that matter. The quality unit approves the controlled route.

Keeping an AI process in a validated state requires ongoing evaluation. The owner should monitor changes in input data, performance, model version, supplier, and use. Clear limits should trigger an investigation, pause, or revalidation before drift becomes a quality problem. Choosing suitable monitoring methods is part of a sound validation process.

Before anyone writes the validation plan, I would ask seven questions:

Takeaways

• Define the decision before choosing the test method.
• Compare the proposed use with the current process and its failures.
• Match the evidence to the approved boundary and the harm from an error.
• Name the people who review, approve, monitor, and control change.
• Keep draft rules in their true scope and status.

Good validation starts with a clear goal and test plan. This gives quality, process, and technical experts a shared focus for what to examine and reduces the risk of work drifting from the agreed plan. It can expose a weak use while the design is still easy to change. It can also give a strong use a sound path into controlled work.