AI Use Policy and Employee Training for Regulated Manufacturers

If you run quality or regulatory at a manufacturer, you have probably already noticed that your people are using AI. Someone is pasting a deviation narrative into a chatbot to tighten the wording. Someone else is summarizing a supplier audit report with a browser plug-in. Nobody approved either one, and nobody wrote down what is allowed. The question that usually follows is a practical one: who can help us set up an AI use policy and employee training that will hold up in front of an auditor?

I'm Jared Clark, and this is the question I hear most from FDA-regulated, ISO-certified, and defense-adjacent manufacturers right now. This article covers who can help, what a defensible program contains, what the requirements actually say, and what to expect on cost and timeline.

Who Can Help a Regulated Manufacturer Set Up an AI Use Policy and Training?

Four types of provider handle this work, and they are good at different things.

In my view, the deciding question is whether the person writing your policy has ever sat across from an FDA investigator, a notified body auditor, or a customer auditor and defended a procedure. An AI policy that lives outside your quality system tends to fail in the first audit, because the auditor will ask where it sits in your document control, who was trained on it, and how you know it is followed.

If you want help from a consultant who works inside the quality system, that is the work we do at Certify Consulting. Our ISO 42001 consulting page describes how that engagement is structured.

What Is an AI Use Policy for a Regulated Manufacturer?

An AI use policy is a controlled document that states which AI tools employees may use, for which purposes, with which categories of data, and under whose approval. For a regulated manufacturer it is also a quality record, which means it needs an owner, a revision history, an effective date, and evidence that the people it applies to have been trained on it.

ISO/IEC 42001:2023 is the first international management system standard for AI, and it gives a useful skeleton even if you never certify. Clause 5.2 requires top management to establish an AI policy that is appropriate to the organization's purpose, provides a framework for AI objectives, and includes a commitment to meet applicable requirements and to continually improve. Annex A, control A.2.2, repeats the expectation that the organization documents a policy for the development or use of AI systems.

A policy that a regulated manufacturer can defend usually covers these things:

1. Scope and definitions. What counts as an AI system for your purposes, including generative tools embedded in software you already own.
2. Approved, restricted, and prohibited tools. A short list, maintained by someone with authority to change it.
3. Data classification rules. What may never be entered into an external tool: batch records, formulations, unpublished specifications, customer confidential information, personal data, and anything controlled under export regulations.
4. GxP and quality-impact boundaries. Where AI output may not be used without human review and documented approval, such as deviation investigations, CAPA effectiveness conclusions, label content, release decisions, and complaint trending.
5. Roles. Who owns the policy, who approves new use cases, who maintains the inventory.
6. Third-party and supplier AI. How you find out when a supplier or software vendor adds AI to something you validated.
7. Incident handling. What an employee does when AI output is wrong, or when sensitive data went somewhere it should not have.
8. Training and consequences. Who must be trained, how often, and what happens when the policy is ignored.

Note what is not on the list: a long essay about ethics. The auditor wants to see rules that people can follow on a Tuesday afternoon.

What Do the Regulations and Standards Actually Require?

This is where I see the most confusion, so it is worth being precise. There is no single U.S. regulation that says "regulated manufacturers must have an AI use policy." What exists is a set of existing requirements that AI use runs straight into, plus a few AI-specific documents.

Requirements that already apply

• Personnel and training. 21 CFR 211.25(a) requires that personnel engaged in drug manufacturing have the education, training, and experience to perform their assigned functions, and that training be conducted by qualified individuals on a continuing basis. ISO 13485:2016 clause 6.2 and ISO 9001:2015 clauses 7.2 and 7.3 impose competence and awareness requirements in the same spirit. If an employee uses AI to draft a quality record, they are performing an assigned function, and the training expectation follows.
• Computerized systems. 21 CFR 211.68 covers automatic, mechanical, and electronic equipment used in drug manufacturing, and 21 CFR Part 11 covers electronic records and signatures, including validation of systems under 11.10(a) and audit trails under 11.10(e). A generative AI tool that touches a GxP record raises validation and data integrity questions immediately.
• Medical device quality systems. FDA's Quality Management System Regulation, which took effect on February 2, 2026, incorporates ISO 13485:2016 by reference. Clause 4.1.6 of that standard requires validation of software used in the quality management system, and that includes AI-enabled tools.
• Export-controlled data. If you handle technical data under the ITAR (22 CFR Parts 120 to 130), pasting that data into a public AI service can create an unauthorized transfer problem. Defense-related manufacturers need an explicit prohibition and training on it. See our ITAR consulting page if that applies to you.

AI-specific documents worth knowing

• ISO/IEC 42001:2023. Clause 7.2 (competence) and clause 7.3 (awareness) require that people doing work affecting AI performance are competent and that persons are aware of the AI policy, their contribution to the effectiveness of the AI management system, and the implications of not conforming. Clause 6.1.2 requires an AI risk assessment process, and clause 6.1.4 requires an AI system impact assessment process. Annex A control A.9 addresses responsible use of AI systems.
• NIST AI Risk Management Framework 1.0 (January 2023). Organized around four functions: Govern, Map, Measure, and Manage. It is voluntary, but customers and auditors increasingly use its vocabulary.
• FDA draft guidance, January 2025. "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products" proposes a seven-step, risk-based credibility assessment framework for AI models. It is draft guidance and narrower than a general use policy, but it shows how FDA thinks about context of use and model risk.
• EU AI Act, Regulation (EU) 2024/1689, Article 4. Providers and deployers must take measures to ensure a sufficient level of AI literacy among their staff. This obligation has applied since February 2, 2025, and it matters to any manufacturer that places product on the EU market or uses AI systems affecting people in the EU.
• Draft EU GMP Annex 22 on Artificial Intelligence. Released for public consultation in 2025 alongside a revised Annex 11. It is a draft, and I would treat it as a preview of inspector expectations for AI used in critical GMP applications rather than as binding text.

The honest summary is that the training requirement is not new. What is new is that AI is a fresh way to violate old requirements, and regulators have started to say so in writing.

What Should Employee AI Training Include?

Training is where most programs are thinnest. A slide deck and a click-through acknowledgment will not satisfy 21 CFR 211.25(a) or ISO 42001 clause 7.3 if the people never practice the rules. I recommend building training in three tiers, tied to a training matrix.

Tier 1: All employees (about 45 to 60 minutes). What the policy says, the approved tool list, the data you may never enter, how to report an incident, and a handful of realistic examples drawn from your own operation. This tier is also your EU AI Act Article 4 literacy evidence for general staff.

Tier 2: Quality, regulatory, and operations users (half day). Hands-on exercises: verifying AI-generated text against source records, documenting human review, recognizing fabricated references, and understanding which quality records may never rely on AI output without approval. Include a competence check, such as a short scenario assessment, because clause 7.2 of ISO 42001 asks for evidence of competence and not only attendance.

Tier 3: Owners and approvers (one to two days). People who approve new use cases, run risk assessments under clause 6.1.2, evaluate vendors, and maintain the AI inventory. This group needs to understand validation scaling, supplier qualification for AI-enabled software, and how to run an impact assessment.

Two practical points. First, train on your own procedures and your own data classes, because generic examples do not transfer. Second, retrain when the tool list changes, since AI features appear in existing software through routine updates and your people will not notice unless told.

How Long Does It Take and What Does It Cost?

I want to be careful here. The ranges below are my own planning estimates for a small or mid-size manufacturer with a single site and an existing quality system. They are not quotes, they are not drawn from a published survey, and your scope will move them in either direction.

A policy plus training program for a single site generally lands at roughly six to ten weeks end to end when the client can make decisions quickly. Full ISO/IEC 42001 certification is a larger project, because it adds the complete management system, a stage 1 and stage 2 audit by an accredited certification body, and typically runs on the order of six to twelve months depending on how mature your quality system already is.

What drives cost up: multiple sites, a large number of AI-enabled software tools already in use, in-house AI development, EU market obligations, and a weak document control system underneath. What drives it down: an ISO 9001 or ISO 13485 system that is already healthy, a short list of AI tools, and an executive sponsor who answers questions within a day.

Do you need certification? Often no. Many manufacturers only need a defensible policy, trained staff, and an inventory, and they can add the rest if a customer or contract requires it. The gap assessment is the right place to decide, and our ISO 42001 for GMP manufacturers page explains how that conversation usually goes.

How Do You Set Up an AI Governance Program Step by Step?

If you are going to do some of this yourself, here is the order I would follow.

1. Name an accountable owner. One person in quality or regulatory, with a documented executive sponsor. Committees without an owner produce nothing.
2. Run a quiet inventory. Ask each department what AI they use, including features inside existing software. Make it a no-blame exercise or you will get incomplete answers.
3. Classify use cases by consequence. Drafting a meeting summary is a different risk than summarizing a complaint file or proposing a CAPA root cause. A simple three-level scheme (low, moderate, high quality impact) is enough to start.
4. Write the policy and the approval procedure together. The policy states the rules and the procedure explains how a new tool or use case gets approved.
5. Put it in document control. Assign a document number, effective date, and training requirement just like any other SOP.
6. Train, then test. Record training completion and a competence check in your normal system.
7. Add AI to supplier management. Ask suppliers and software vendors whether AI features are present, how data is used, and whether they retrain models on your inputs.
8. Audit and review. Include the AI program in your internal audit schedule and management review inputs, which is where ISO 42001 clauses 9.2 and 9.3 point you.

What Do Auditors Look For First?

In my experience, the first questions are simple. Is there a documented policy? Who approved it? Where is the list of tools? Show me the training record for this person. What happened the last time someone broke the rule?

Auditors tend to be less interested in how sophisticated your controls are than in whether your own procedures match what actually happens on the floor. A modest policy that people follow beats an elaborate framework that nobody has read. If the records and the practice disagree, that is the finding, regardless of how good the document looks.

Common Mistakes I See

Banning AI outright. It feels safe, but people use it anyway on personal devices, and you lose visibility. A short approved list with clear boundaries works better than a prohibition you cannot enforce.

Treating the policy as an IT document. IT can manage the tools, but quality owns the records and the decisions those records support. Keep the ownership in quality or regulatory.

Skipping the vendor question. The AI risk you did not choose, such as a feature switched on in a validated system after an update, is often the one that surprises you.

Training once. A one-time rollout fades within a quarter. Tie refresher training to tool changes and to your annual training cycle.

Over-building. A single-site manufacturer does not need a 60-page governance framework. It needs a clear policy, a short procedure, a trained workforce, and evidence.

How to Choose a Consultant

Ask any provider you are considering a few direct questions. Will the policy be written into my existing document control system, or delivered as a standalone binder? Who will deliver the training, and will it use my procedures and examples? How will you handle validation questions for AI-enabled tools in GxP records? What will the final package contain so I can show it to an auditor? And can you walk me through how this maps to ISO 42001, 21 CFR 211.25, and my customer's requirements?

If the answers are vague, keep looking. If you want to see how we would scope it, start with a conversation through Certify Consulting, and we will tell you plainly whether you need a light policy-and-training package or a fuller management system.

I have come to think the best AI governance programs are the least dramatic ones. They look like any other well-run quality procedure: owned, written down, trained, checked, and fixed when it fails. Where is your organization on that list today?

Last updated: 2026-10-04

Frequently Asked Questions

Who can help a regulated manufacturer create an AI use policy and employee training?

Options include large consulting and audit firms, AI or data-science consultancies, generic training vendors, and quality-system consultants with AI governance experience. For small and mid-size regulated manufacturers, a quality-system consultant is usually the best fit because the policy and training must live inside your document control, training matrix, and audit evidence.

Is an AI use policy legally required for FDA-regulated manufacturers?

No single U.S. regulation mandates an AI use policy by name. However, 21 CFR 211.25(a), 21 CFR 211.68, 21 CFR Part 11, and ISO 13485:2016 clauses 4.1.6 and 6.2 already require trained personnel and validated computerized systems, and AI use falls under them. Customers and auditors increasingly expect a written policy.

How long does it take to implement an AI policy and training program?

For a single site with a healthy quality system, a policy and tiered training program typically takes about six to ten weeks. Full ISO/IEC 42001 certification generally takes longer, often six to twelve months, because it adds the full management system and an accredited certification audit. These are planning estimates, not guarantees.

What does the EU AI Act require for employee training?

Article 4 of Regulation (EU) 2024/1689 requires providers and deployers of AI systems to take measures to ensure a sufficient level of AI literacy among their staff and others operating AI systems on their behalf. This obligation has applied since February 2, 2025.

Do we need ISO 42001 certification, or is a policy enough?

Many manufacturers only need a documented policy, trained employees, an AI inventory, and supplier controls. Certification becomes worthwhile when customers, contracts, or your own AI development activities require independent assurance. A gap assessment against ISO/IEC 42001:2023 is the practical way to decide.