Establishing confidence in an AI assisted GMP workflow
The suitability of an AI tool for a GMP activity depends on its intended use, the consequences of an incorrect output and the controls applied to its operation. A general demonstration of capability provides limited assurance about a specific workflow.
An application used to help draft training material presents a different set of considerations from one used to identify defects, interpret analytical data or support a decision affecting batch disposition. The assessment needs to reflect that distinction.
The starting point should be a clear description of the task, the permitted inputs, the expected output and the decisions that may rely on it. This provides a basis for determining the evidence and oversight required.
For a drafting application, I would want the review process to verify technical statements, references and applicability to the actual operation. Fluent wording can make an unsupported conclusion appear convincing. The reviewer needs access to the underlying evidence and sufficient knowledge to challenge it.
For applications influencing process or product decisions, the assessment also needs to address performance under representative conditions, known limitations and the response to uncertain or unexpected results. The suitability of the data used to develop or assess the system warrants particular attention.
Human review should have a defined purpose. The person performing it needs the competence, information and time to identify material errors, together with authority to reject an output. An approval step adds limited assurance if those conditions are absent.
Changes to the model, configuration, input data or surrounding workflow may affect performance. The arrangements for detecting, assessing and controlling those changes should be established before routine reliance develops.
Data protection, confidentiality, access controls and retention also need to be considered in relation to the information being processed.
In my view, confidence comes from demonstrating that the application performs an adequately defined task within understood limits, with appropriate verification and accountability.
The practical question is what evidence would justify relying on this output for this purpose, and what would cause that reliance to be withdrawn.
Background and scope
EU GMP Annex 11, particularly Principle and sections 1, 4, 10 and 11; MHRA Inspectorate, Use of AI for GXP inspection responses, 29 June 2026. The MHRA article specifically concerns inspection responses; the broader workflow discussion is practical risk-based commentary.
European Commission EudraLex Volume 4