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AI Coordinator (Level 1) Training Course

AI coordination for trusted quality and compliance

AI coordination for trusted quality and compliance

COURSE OVERVIEW 

Artificial Intelligence (AI) creates significant business value, but it also introduces new and complex risks. Traditional IT governance approaches are often insufficient to address the black‑box nature of AI systems, regulatory uncertainty and lifecycle‑wide quality challenges. 

The AI Coordinator (Level 1) training introduces a risk‑based AI quality framework aligned with current and upcoming regulations, international standards and cross‑industry best practices. Participants learn how to manage AI quality across the entire AI lifecycle, reduce risks and establish trustworthy and compliant AI operations. 

The practical application of the framework is illustrated through a detailed, real‑world case study.

WHAT IS AN AI COORDINATOR? 

An AI Coordinator ensures that AI systems are developed and used in a safe, secure, compliant, ethical and reliable way. This role connects organisational governance, regulatory requirements and technical implementation. 

The training explains the target maturity level organisations must achieve to avoid AI risks, manage AI quality effectively and maintain trustworthy AI operations. All key aspects influencing high‑quality and trustworthy AI are addressed, from organisational readiness to technical implementation and oversight. 

COURSE DETAILS

Duration: 18 training units / 2 days 

Language: English 

Training format: Modular further training 

Certification: AI Coordinator TÜV SÜD certificate issued by TÜV SÜD Academy 

BENEFITS OF THIS AI COORDINATOR TRAINING COURSE

After completing this course, participants will be able to: 

  • Apply internationally recognised methods, regulations and best practices for AI quality 
  • Manage AI quality across the entire AI lifecycle
  • Identify, assess and mitigate AI‑related risks
  • Address compliance and regulatory requirements with confidence
  • Avoid common pitfalls in AI adoption and deployment
  • Apply AI effectively and responsibly in practice

Upon successful completion, participants receive formal proof of their expertise, enabling their organisation to demonstrate the responsible and competent use of AI. 

 

  • Course outline

    The training covers organisational, regulatory and technical aspects of AI quality, including: 

    • Fundamentals of AI quality 
      • Broad definition of Artificial Intelligence 
      • Key challenges in AI adoption
      • AI risks and potential impacts on brand and reputation
      • Risk‑based AI quality framework 
    • Overview of a comprehensive AI quality framework 
      • Six pillars of AI quality: safety, security, legal compliance, ethics, performance and sustainability 
    • Risk assessment and organisational maturity 
      • Conducting AI risk assessments
      • Applying an organisational AI maturity framework
      • Evaluating current and target maturity levels
    • Organisational readiness and governance
      • Overview of ISO/IEC 42001
      • Organisational context and governance expectations
      • Compliance with current regulations, standards and international regulatory developments
      • AI adoption strategy, skills and competencies
      • Case study: developing an organisational maturity profile
    • Technical foundations and lifecycle management
      • AI reference architecture
        • Data quality, AI models and AI training
        • Integration, execution and flow of control
        • Testing, controls and infrastructure requirements
      • AI and data lifecycles, stages, quality gates and interactions
    • Validation, security and quality management
      • Lifecycle‑wide AI risk assessment
      • Validation and verification methods, including AI explainability tools
      • Cybersecurity aspects: AI security, attacks and defence
      • Planning an AI quality management system
      • Developing an AI quality adoption roadmap 

WHO SHOULD ATTEND?

This training is intended for professionals involved in the development, governance or deployment of AI systems, including: 

  • Managing directors and IT managers
  • AI and software engineers, data analysts
  • Compliance and quality managers
  • Consultants and AI experts across all industries 

Prerequisites:

  • No formal prerequisites are required.
  • Basic knowledge of common machine learning algorithms is recommended. 

READY TO BECOME AN AI COORDINATOR? 

Gain the expertise to manage AI quality, reduce risks and support trustworthy AI adoption in your organisation. 

Register now

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