Summary
- The MHRA has identified AI-generated inspection responses containing false references, unsuitable frameworks, and inadequate remediation.
- The regulator is not banning AI use, but expects submissions to be accurate, verifiable, technically reviewed, and signed off by accountable people.
- The guidance turns enterprise AI governance into a practical regulated workflow issue for life sciences companies.
MHRA has warned regulated organisations that artificial intelligence tools must not be used to obscure weak inspection responses, invent guidance, or replace accountable technical review.
The regulator’s Inspectorate has published an update on the use of AI for GxP inspection responses after seeing examples of AI-generated material submitted to compliance teams. AI can help organisations articulate complex technical issues, improve consistency, speed up routine drafting, and support better regulatory outcomes when used properly.
The Inspectorate has also seen the other side. The update refers to responses containing non-existent MHRA guidance, inappropriate regulatory frameworks, and material that appeared to avoid rather than address serious deficiencies. In one case, an AI-generated response to a serious patient safety deficiency contained inaccuracies and references that did not exist, increasing review time and delaying resolution.
The regulator’s position is practical rather than ideological. It is not trying to police the mere use of AI in drafting. It expects submissions to be factually accurate, verifiable, technically reviewed by experienced people, signed off by someone with authority and accountability, supported by evidence, and appropriate to the specific regulatory context.
The pilot problem reaches compliance
Enterprise AI governance is often discussed in abstract terms: model risk, human oversight, explainability, and accountability. The MHRA’s update shows what those words mean in a regulated workflow. A life sciences company responding to an inspection is not producing generic office copy. It is providing evidence of corrective and preventive action, root cause analysis, patient safety controls, and compliance with highly specific requirements.
That is exactly the kind of environment where poorly supervised generative AI can create risk. A fluent response may look polished while failing to address the deficiency. A hallucinated citation can waste regulator time. A generic corrective action plan can conceal the absence of real operational understanding. In regulated industries, that is not a productivity gain; it is a governance failure.
The MHRA is offering organisations the option to disclose AI use in responses to compliance teams. Disclosure is not mandatory, but the regulator says transparency, human verification, and approval can indicate a more mature quality culture. Regulators are not necessarily demanding AI prohibition; they are demanding evidence that AI assisted work is controlled.
Many enterprise AI projects stall before production for the same reason. The problem is often not whether the technology can draft text, summarise documents, or generate plausible recommendations. It is whether the organisation can prove that the output is accurate, relevant, reviewed, and safe to rely on in a real operational context.
Life sciences companies will need to treat AI assisted compliance work as part of their quality system. That means audit trails, reviewer competence, evidence management, prompt and output handling, and clear accountability for final submissions. The same logic will spread into other regulated sectors, including financial services, utilities, aviation, defence, and healthcare.
The MHRA’s message extends beyond inspection paperwork. AI can reduce administrative burden, but it does not transfer responsibility away from the organisation using it. In regulated work, the person signing off the response still owns the claim, the evidence, and the consequences.
