The integration of Artificial Intelligence in the Quality Management System is no longer an experimental initiative but a structural decision for manufacturers, importers, laboratories and distributors operating in regulated environments. Companies managing supplier qualification, technical documentation review, regulatory surveillance and complex regulatory databases are facing increasing operational pressure. Integrating AI tools into the Quality Management System allows organisations to improve efficiency, reduce manual workload and strengthen traceability, provided that implementation is properly governed and aligned with European regulatory requirements.

Since the progressive application of Regulation (EU) 2024/1689 (AI Act), fully applicable from 2026, companies using Artificial Intelligence within internal decision-making or compliance processes must ensure risk management, transparency and effective human oversight. This directly affects Quality Management Systems under ISO 9001, ISO 13485 and regulated frameworks such as Regulation (EU) 2017/745 (MDR) and Regulation (EU) 2017/746 (IVDR). AI can no longer be deployed informally; it must be integrated, documented and auditable within the Quality Management System.

Scope and applicability

The integration of Artificial Intelligence in the Quality Management System particularly concerns medical device manufacturers under MDR, IVD manufacturers under IVDR, importers and distributors with reinforced documentation and traceability obligations, laboratories managing critical suppliers and organisations handling large volumes of technical files and regulatory data. Not all AI tools fall under high-risk classification according to the AI Act. However, when AI contributes to decisions that influence compliance, safety, supplier approval or documentation validation, companies must document its use, validate outputs and maintain structured human supervision.

Key regulatory developments: AI, quality and compliance

The European regulatory framework introduces essential elements impacting AI integration in the Quality Management System. Companies must implement a documented risk management approach for AI tools, ensure traceability of outputs and maintain human oversight in all critical decisions. This means that using generative AI or automated analysis tools for supplier evaluation or technical documentation review requires formal procedures, version control, validation protocols and clear allocation of responsibilities. Organisations affected will need to incorporate AI usage into document control procedures, internal audits and supplier qualification workflows.

Operational implications within the Quality Management System

Supplier qualification

Artificial Intelligence can support preliminary analysis of supplier documentation, including ISO certificates, declarations of conformity, test reports and historical performance records. AI tools may assist in identifying inconsistencies, expired certificates or patterns of non-conformity. Nevertheless, the Quality Management System must define objective acceptance criteria, ensure final human validation, record decisions and periodically evaluate the performance of the AI tool itself. AI should function as an advanced analytical layer, not as a substitute for regulatory responsibility.

Technical documentation review

Within MDR and IVDR environments, AI may facilitate cross-checking of General Safety and Performance Requirements, verification of updated harmonised standards, detection of obsolete references and generation of preliminary compliance matrices. This can significantly reduce the time spent on initial document screening. However, regulatory accountability remains with the manufacturer. Integration of AI in this area requires robust document control, structured validation and inclusion of AI performance monitoring within the internal audit programme.

Regulatory surveillance and post-market monitoring

Regulatory intelligence and post-market surveillance are areas with substantial automation potential. Properly configured AI systems can monitor official sources such as the Official Journal of the European Union, national authorities, guidance documents and regulatory updates, classify changes by relevance and update internal compliance databases accordingly. The main risk in this domain is over-reliance. The Quality Management System must include periodic review by qualified regulatory professionals to confirm relevance and applicability of automated alerts.

Regulatory database management

Supplier databases, certification records, audit findings and non-conformity logs can benefit from AI-supported indexing, duplicate detection, automated expiry alerts and predictive trend analysis. Integrating Artificial Intelligence in the Quality Management System for database management enhances traceability and visibility across departments. However, architecture design must ensure data protection, cybersecurity safeguards and alignment with AI Act requirements for documentation and accountability.

Risks and common implementation errors

Companies integrating AI tools without structured governance may face regulatory exposure, audit observations under ISO 13485, non-compliance findings from notified bodies or loss of documentation traceability. A frequent mistake is deploying AI solutions without updating the Quality Manual, document control procedures and risk management files. Another risk lies in using non-auditable tools that do not allow traceability of inputs, outputs and human validation. Under the AI Act, lack of documentation may constitute a compliance failure even if the technical result appears correct.

Executive summary

Artificial Intelligence in the Quality Management System represents a structural evolution in regulated industries. Properly implemented, AI enhances supplier qualification processes, improves technical documentation review efficiency and strengthens regulatory surveillance and database management. However, integration must comply with Regulation (EU) 2024/1689 (AI Act), MDR and IVDR, ensuring traceability, documented risk management and effective human supervision.

Aseconsa supports companies in designing, validating and implementing hybrid Quality Management Systems that combine Artificial Intelligence tools with expert human validation. Organisations seeking to modernise their compliance framework without compromising regulatory control can benefit from a structured implementation roadmap aligned with European legislation.

For companies planning to integrate AI into their Quality Management System, early action reduces risk, improves audit readiness and creates measurable operational efficiency while maintaining full regulatory accountability.

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