AI tools can be used in GxP laboratories, but only with controls that match their intended use and the risk to product quality and data integrity. Drafting help and SOP search need lighter controls than anything that influences a GxP decision, which needs a defined intended use, validation scaled to risk, human review and records that meet ALCOA+ principles.
QC and QA teams already use AI assistants, often informally. The risk is not the tool itself but unvalidated use, confidential data sent outside the company and outputs nobody checked. Inspectors expect you to know where AI is used in your GxP processes and how it is controlled.
Where AI is used in GxP labs today
- Report and document drafting: first drafts of deviation reports, investigation summaries and SOP updates
- OCR and data capture: turning printed outputs, certificates of analysis or logbooks into electronic data
- Trend analysis: flagging patterns in environmental monitoring, stability or QC data
- Chromatography review assistance: software features that suggest peak integration or highlight unusual chromatograms for the reviewer
- SOP and knowledge search: chatbots that answer questions from approved procedures
The main data integrity risks
Confidential data leakage
Pasting batch records, specifications or investigation details into a public AI tool can send confidential and sometimes personal data to a third party, where it may be stored or used in ways you cannot control. Our guide to data privacy when using AI at work covers this in more depth.
Hallucinated or altered values
Generative AI can produce fluent text containing results, batch numbers, dates or references that are not in the source. In a GxP record, an invented value is a data integrity failure, however well the rest reads. OCR tools can also misread characters, such as 1 and 7 or 0 and 8.
Missing audit trail
Many general-purpose tools keep no record of who asked what, which model version answered and what was changed afterwards. If an output becomes part of a GxP record, you must show how it was produced and reviewed.
Model changes
Providers update cloud AI models, sometimes without notice, so a tool that passed your testing may behave differently next month.
Unvalidated use for GxP decisions
The most serious risk is scope creep: a tool approved for drafting starts being used to decide whether a deviation is critical or a result is out of specification, a use never assessed or validated.
Controls that work
- Define intended use. Record what each tool may and may not be used for, and whether its output can influence a GxP decision or record.
- Assess risk. Use your quality risk management process to judge impact on product quality, patient safety and data integrity.
- Approve tools. Keep a list of approved tools, preferring enterprise versions with contractual data protection over personal or free accounts.
- Scale validation to risk. Follow your computerised system validation approach, such as GAMP 5 Second Edition (2022) and a CSA-style focus on critical functions. Test with realistic data and define acceptance criteria.
- Keep a human accountable. A named, trained person reviews and approves every output that enters a GxP record, checking it against source data.
- Control access. Use individual accounts, not shared logins.
- Retain records where needed. Where prompts and outputs form part of a GxP record or decision, retain them under your record controls, with the model or version used.
- Manage change. Treat model or version updates as changes, with assessment and re-testing proportionate to risk.
These controls apply ALCOA+ principles to AI: outputs must be attributable to a reviewer, accurate against the source, and original records must stay available. For a step-by-step method, see how to validate an AI-assisted workflow.
Use case, risk and control
| Use case | Main risk | Key controls |
|---|---|---|
| Drafting deviation or investigation reports | Invented facts, confidential data leakage | Approved enterprise tool; reviewer checks every fact against source; final report approved under normal workflow |
| OCR of printed instrument outputs | Misread characters | Validation on representative documents; second-person or automated check against original; original retained |
| Trend analysis of EM or QC data | Missed or false signals | Validation against known historical events; human review of flags; defined escalation |
| Chromatography peak review assistance | Inappropriate integration | Validated as part of the CDS; audit trail; analyst and reviewer decide integration |
| SOP search chatbot | Outdated or wrong answer | Restrict to current approved SOPs; answers link to the SOP; staff follow the SOP, not the chat |
What regulators are saying
The picture is still developing. In 2025, a draft new EU GMP Annex 22 on artificial intelligence was published for public consultation, alongside proposed revisions to Annex 11 on computerised systems. The consultation draft took a cautious line on generative and continuously learning models in critical GMP applications. Other regulators, including the US FDA, have also issued draft guidance or discussion papers on AI.
Check the current status of these documents, because drafts can change significantly before adoption. Meanwhile, EU GMP Annex 11, US 21 CFR Part 11 and data integrity guidance from the MHRA, WHO and PIC/S already apply to computerised systems and records, AI or not. In India and the GCC, check expectations with CDSCO, SFDA or the relevant ministry of health.
Worked example: AI-drafted deviation reports in a Hyderabad QC lab
A pharmaceutical QC laboratory in Hyderabad writes around 30 deviation reports a month. Investigators spend hours turning notes into the standard template, so the QA head wants an AI assistant to produce first drafts.
QA first stops the use of personal accounts on public tools. The company licenses an enterprise assistant whose contract excludes customer data from model training, and IT restricts access to named users.
The written intended use: the tool turns investigator notes into a draft in the approved template. It may not classify the deviation, determine root cause, assess product impact or decide on CAPA. Those stay human decisions under the deviation SOP.
Before go-live, the team runs 20 closed deviations through the tool and compares each draft with the source notes. Two drafts contain a wrong batch number and one adds a root cause the notes did not support. In response, the procedure requires the investigator to tick off every batch number, date, result and reference against source records, and the reviewer to confirm this before approval. The final deviation report in the electronic QMS remains the GxP record. Model updates are logged under change control and the 20-case test is repeated after significant changes.
Common mistakes
- Allowing personal or free AI accounts for work involving GxP data
- Approving a tool for drafting and then letting it drift into decision-making
- Assuming a fluent output is an accurate one
- Treating vendor validation claims as your validation for your intended use
- Ignoring model updates after initial testing
- Not defining whether prompts and outputs are records
Key takeaway: In a GxP lab, AI is a tool used under your quality system, not outside it. Define intended use, approve the tool, scale validation to risk, keep a trained human accountable for every output that enters a record, and treat model updates as changes.
Frequently Asked Questions
Can AI be used in a GMP laboratory?
Yes, with controls. Define the intended use, assess the risk to product quality and data integrity, validate in proportion to that risk and keep a trained person responsible for reviewing outputs. Current data integrity and computerised system requirements still apply, and AI-specific guidance is developing, so check the latest regulatory position.
What is EU GMP Annex 22?
Annex 22 is a proposed new annex to the EU GMP Guide covering the use of artificial intelligence in the manufacture of medicines. A draft was published for public consultation in 2025, alongside revisions to Annex 11. Because it was a draft at the time of writing, check the European Commission or EMA websites for its current status.
Do AI prompts and outputs need to be kept as GxP records?
It depends on their use. If a prompt and output form part of a GxP record or support a GxP decision, they should be retained under your record controls, with the tool and version used. If AI only helps draft text that a person then rewrites and verifies, your procedure may define the final approved document as the record.
Is it safe to use an AI chatbot for deviation reports?
General-purpose AI assistants can help draft deviation reports, but only under controls. Use an approved enterprise tool rather than a personal account, avoid sending confidential data to unapproved services, check every fact against source records and keep root cause, impact and CAPA decisions with trained people. The approved report in your QMS remains the record.
Next steps
To build the validation and data integrity skills behind these controls, explore our Data Integrity & Computerised System Validation course, or start with Good Documentation Practices & Data Integrity Essentials. You can also speak to a counsellor.
For related reading, see AI tools for quality professionals: use cases, GAMP 5 software categories explained and deviation vs change control vs CAPA.
Looking for structured certification?
Explore Innoryx courses or speak to a counsellor about the right program for you.