AI Act Quality Management Systems QuickStart

QuickStart: Implementing a Compliant Article 17 Quality Management System for the EU AI Act

Under the EU Artificial Intelligence Act, providers of high-risk AI systems are legally required to implement and maintain a documented Quality Management System (QMS) as mandated by Article 17. This practical 1-day QuickStart course delivers a clear, step-by-step implementation roadmap based on the leading draft European standard prEN 18286, equipping you with the tools and confidence to build a compliant, product-centric QMS efficiently from day one.

Article 17 establishes one of the foundational obligations for high-risk AI providers. It requires a QMS that is deliberately product-centric and lifecycle-focused—unlike traditional organisation-wide standards such as ISO 9001—so it can address the unique challenges of AI systems, including model drift, data quality, continuous learning, and dynamic risks to health, safety, and fundamental rights.

In this intensive course, you will receive practical, in-depth guidance on how to create, implement, maintain, and continuously improve a compliant AI Quality Management System aligned with prEN 18286. The program focuses on the core elements you need to get started immediately and is designed for developers, engineers, IT and MLOps managers, Data Protection Officers (DPOs), risk managers, legal and compliance professionals, governance specialists, internal auditors, and AI practitioners.

By the end of the day, you will leave with a concrete, actionable roadmap to navigate the evolving AI regulatory landscape in 2026 and beyond—turning Article 17 compliance into a genuine strategic advantage for trustworthy, high-quality AI deployment across Europe.

DAY 1: The QMS Foundation

Time: 9.00am - 4.30pm

• Plan the governance structure (Board, Regulatory Compliance Officer, AI QSM Manager, Authorised Representative)
• AI Literacy requirements
• Risk classification scheme
• Regulatory compliance strategy
• Quality management system design, processes and documentation
• Technical documentation requirements
• Human oversight obligations
• Data governance and internal control requirements
• Logging, post-market monitoring and incident reporting
• Conformity assessment preparation - procedures vary by system category
• Sample rollout plan with accountability matrix

LEARNING OUTCOMES

By the end of this course, you will:

• Articulate the core requirements Clearly explain Article 17’s 13 mandatory QMS elements, why the AI Act deliberately requires a product-centric (not organisation-centric) approach, and how the QMS should be designed to reduce regulatory risk, speed up CE marking, and creates competitive advantage in the EU market.
• Define scope and leadership accountability for a specific high-risk AI system Draft a proportionate QMS scope statement for a sample AI product, assign clear roles and responsibilities using an accountability framework.
• Identify the AI lifecycle process model, needed controls, and sample traceability matrix Define a high-level process model linking the full AI lifecycle (data collection → design → deployment → post-market monitoring → decommissioning) to the QMS, highlighting AI-specific requirements such as data governance (Article 10), risk management (Article 9), model drift/change management, and serious-incident reporting (Article 73).
• Apply best practices for the most critical Article 17 elements Identify and prioritise procedures and activities and example templates for:
• Build an implementation roadmap Plan the development of practices against prEN 18286/Article 17, produce a prioritised 90-day action plan, consider resource constraints, and integration with existing operational practices.
• Leave with confidence and next-step clarity Articulate the path forward.

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