Building AI Governance

• Management teams and executive leadership seeking to build and oversee organisational AI strategies.
• Risk, compliance, and legal leaders responsible for navigating complex regulations like the EU AI Act and GDPR.
• Business and operational leaders tasked with translating high-level AI strategy into actionable roadmaps and practical execution.

A comprehensive executive programme designed to equip management teams with the strategic insights, governance frameworks and regulatory knowledge needed to develop effective AI governance and a high-impact AI strategy, followed by a practical workshop.

Upon completion of this training, participants will be able to:

• Understand the fundamentals of AI governance and design tailored governance frameworks aligned with organisational strategy, risk appetite and regulatory requirements.
• Navigate complex regulatory landscapes, including the EU AI Act and GDPR, to ensure organisational compliance and mitigate legal, operational and reputational risks.
• Develop and implement a coherent AI strategy that translates a high-level vision into actionable roadmaps, leveraging Luxembourg’s innovation ecosystem, AI sandboxes and funding mechanisms.
• Apply ethical AI principles and risk management practices to support responsible AI deployment while maintaining a competitive advantage, and create essential governance artefacts.

MODULE 1: AI Governance Fundamentals & Global Context
Core AI Concepts & Landscape

• Foundations of AI, machine learning, and generative AI – capabilities, limitations, and practical applications
• AI maturity models and organisational readiness assessment frameworks
• Global AI governance landscape: EU, US, China, and emerging regulatory standards
• Key regulatory frameworks and implications for organisational strategy

Interactive Activity: AI Readiness Self-Assessment

• Participants use an assessment tool to evaluate their organisation's current AI maturity level
• Group discussion on findings and strategic implications

MODULE 2: Designing & Implementing AI Governance Frameworks
Governance Architecture & Organizational Design

• Building and structuring AI governance committees (composition, roles, decision rights)
• Designing governance decision-making frameworks for AI project approval, deployment, and monitoring
• Establishing organisational policies: data quality, model validation, performance monitoring, and audit trails
• AI Risk Management & Governance Controls
• Identifying and assessing AI-specific risks: bias, explainability, security, transparency, and model drift
• Building risk mitigation strategies aligned with organisational risk appetite and tolerance
• Implementing governance controls and KPIs for ongoing risk monitoring

Hands-On Exercise: Governance Framework Design Workshop

• Small group work: participants design a governance committee structure for a sample organisation
• Create a decision-making matrix for AI project approval (risk categories, escalation paths)
• Present frameworks to peers; receive feedback and refine based on real-world scenarios

MODULE 3: Regulatory Compliance & Risk Management
Duration: 80 minutes
EU AI Act

• Risk-based classification system (prohibited, high-risk, general-purpose, limited-risk)
• Mandatory compliance requirements for high-risk AI systems and prohibited practices
• Transparency requirements, bias testing, and documentation obligations
• Implementation timelines, transition periods, and organisational readiness planning

GDPR & Data Privacy in the Context of AI

• Data subject rights in AI systems (access, portability, erasure, algorithmic decision-making)
• Data Protection Impact Assessments (DPIA) for AI implementations
• Privacy-by-design principles and cross-border data compliance

Case Study Exercise: AI Act Risk Classification

• Participants analyse 3-4 real AI use cases (recruitment, credit scoring, healthcare diagnostics)
• Classify each under EU AI Act framework (risk tier, compliance obligations)
• Identify required controls, documentation, and compliance timelines
• Discuss organisational readiness and resource requirements

MODULE 4: Ethical AI & Strategic Implementation
Ethical AI & Responsible Practices

• Bias detection, root causes, and mitigation strategies across AI lifecycle
• Explainability and interpretability: building trust through transparency
• Ethical frameworks, stakeholder trust-building, and organisational culture transformation

AI Strategy & Roadmap Development

• Translating organisational vision into coherent AI strategy and initiatives
• Luxembourg's innovation ecosystem: 3-sandbox model, incubators, and regulatory sandboxes
• Funding mechanisms: Luxinnovation, EU funding (EIC Accelerator, DIGITAL Europe, Horizon Europe)
• Building a realistic 12-24 month execution roadmap with milestones and KPIs

Interactive Exercise: AI Strategy & Roadmap Workshop

• Small groups draft a 12-month AI strategy for a sample industry (finance, healthcare, manufacturing)
• Identify 3-4 key AI initiatives aligned with business goals and governance frameworks
• Map initiatives to regulatory compliance, risk management, and resource requirements
• Present roadmaps and discuss implementation challenges; peer feedback and refinement

MODULE 5: Hands-On Workshop – Building AI Governance Artifacts & MVP
Workshop: Drafting Essential Governance Documents

• AI Governance Policy & Charter: roles, responsibilities, decision frameworks
• AI Risk Assessment Template: organisational risk taxonomy and mitigation controls
• Data Protection & GDPR Compliance Checklist for AI projects
• AI Vendor & Model Evaluation Framework: selection criteria and governance gates
• Participants work in small groups; use templates and refine for their organisations

Closing Activity: Action Planning & Commitments

• Participants identify 2-3 immediate next steps for implementing governance in their organisations
• Create 90-day action plans with accountability and key milestones

A laptop and access to a GenAI chat (chatgpt, claude..) is required