Will AI replace the (e)QMS? Or will it just make it better?

1. A bold claim - and what’s really behind it.

A provocative statement is currently circulating in the life sciences community: The eQMS has had its day. In a few years, it will be irrelevant. Artificial intelligence will take over quality management entirely - faster, more standardized, and more reliable than any document-based system.

Anyone working in the regulated medical device environment is familiar with such claims. Most often, they contain a grain of truth but are stylized into a revolution that is actually an evolution. This is the case here as well.

Because the actual problem that AI addresses is real. And it is older than any software.

2. The oldest problem with QMS: Knowledge resides in people’s minds.

Processes are documented. SOPs are versioned. Deviations are recorded. The classic eQMS delivers all of that. And yet, at the decisive moment, quality managers keep asking the same question: “What exactly needs to be done now?” The answer depends - at least for now - on people, not on the system.

A practical example: Temperature deviation in the warehouse overnight. Several batches are affected. What now? The experienced QM manager knows immediately: assess the impact, evaluate the risk, create a Non-Conformity Record (NCR), inform those responsible, and prepare a CAPA if necessary.  The new employee opens the SOP - and is faced with a multi-page process description that needs to be interpreted.

Result today: hours to days. Quality of response: depends on the person.
 
The silent loss of knowledge - an underestimated risk factor.

This dependency problem is currently being dramatically exacerbated by a demographic trend: an entire generation of experienced quality managers is retiring.

What these people take with them is not found in any SOP: the implicit judgment of whether a deviation is truly critical - the experience-driven, risk-based approach. The intuition for when a root cause only scratches the surface, and when it really hits the mark. The experience from decades of audits that knows which issues an auditor will find first.

Until now, this experiential knowledge has been structurally difficult to transfer. It is passed on in meetings, in informal conversations, through decades of working together. When the person leaves, the knowledge goes with them.

For the first time, AI can offer a real solution here - not as a substitute for human experience, but as an infrastructure for preserving and operationalizing experiential knowledge.

By systematically translating decision logic, evaluation patterns, and reaction chains into AI-supported process models - before the experts leave the company - implicit knowledge can be anchored in the system for the first time. Retrievable, scalable, auditable.

This is no small feature. It is a strategic response to one of the greatest risks in quality management over the next decade.

3. What can AI actually do in eQMS?

Specifically, the added value of AI in quality management can be boiled down to a few clearly defined benefits:
Contextualization of SOPs: AI understands the process context of a query and provides - instead of a 30-page SOP - the three relevant steps for that exact situation.

• Pre-structuring of deviations: Incidents are recorded, automatically assessed (risk class, regulatory requirements, affected goods), and pre-structured for documentation.
• Root cause analysis: The AI applies proven analytical methods (5 Whys, Ishikawa) and suggests plausible hypotheses for the cause - based on historical data and patterns.
• CAPA preparation: Proposed corrective actions are derived from the root cause, including assigned responsibility and proposed deadlines.
• Audit Readiness: Gap analyses against ISO 13485, MDR/IVDR, and other regulatory requirements - automatically and continuously.
• Change Management: During document revisions, the AI assesses the regulatory impact and suggests affected documents, tests, and evidence.

That’s impressive. And it’s actually feasible. But it happens within the eQMS - not in place of it.

4. The Regulatory Framework: What is AI allowed to do in the QMS - and what is it not allowed to do?

Especially in the MedTech context, this question is not academic but compliance-relevant. Three key factors are decisive here:

ISO 13485: Validation remains mandatory

Any software used within the quality management system - including AI-supported assistance - is subject to the validation requirements of ISO 13485. This means: No AI integration without a documented validation plan, requirements definition, acceptance criteria, and, if necessary, a validation run.

EU AI Act: Classification Based on Use Case

The EU AI Act distinguishes between risk classes. AI systems that support decisions in the regulated manufacturing process may be classified as high-risk AI - with corresponding requirements for transparency, documentation, and human oversight - but are not required to be.

Determinism as a Fundamental Principle

For GMP- and GxP-critical processes, for example, a clear principle applies: deterministic, rule-based models - no generative LLMs for decision-making (see draft of GMP Annex 22). This is not merely regulatory caution but technical common sense: anyone who cannot explain why the system arrives at a recommendation will face problems during an audit.

Human-in-the-loop is not an optional product feature. In MedTech quality management, it is a normative requirement -  and must be demonstrably implemented.

5. The SEQLY Perspective: Evolution, Not Revolution

What we see in our consulting practice: AI-supported assistance in the eQMS measurably accelerates processes, reduces sources of human error, and relieves QM teams of routine tasks. That is real added value.

But it requires one thing: a well-structured, validated QMS as a foundation.

Poor processes + AI = faster poor processes. Good eQMS + AI = systematically excellent quality processes.

Those who do not practice effective change management today, do not follow consistent CAPA logic, or do not maintain their SOPs in compliance with documentation requirements will not be saved by AI. AI is only as good as the knowledge on which it is based.

Conversely: Those who have a solid eQMS and specifically expand it with AI components will obtain a system that not only documents quality processes but actively manages them.

6. Comparison: Traditional eQMS vs. eQMS with AI Integration

7. The right question is not “replace or not.”

The framing “AI replaces the eQMS” is incorrect - and distracts from the truly crucial question: Is your eQMS AI-ready today? Have you systematically captured the knowledge of your experienced QM experts - before they leave the company?

Those who use the next 12 to 24 months to strengthen the foundation - consistently documenting process logic, making decision rules explicit, structuring the knowledge base - will be able to use AI as a true accelerator.

Those who wait for AI to solve the problem will realize: The tool was already there. The foundation was missing.

Would you like to know if your (e)QMS is AI-ready?

SEQLY supports MedTech manufacturers on their journey from regulatory foundations to the intelligent digitalization of quality management - from the validation concept to AI integration. Contact us.