Artificial Intelligence has moved from experimentation to everyday business. With 78% of organizations now using AI in at least one business function, a surge of new regulation worldwide, and rising board-level exposure to AI-driven incidents, governing AI well has become a strategic and operational imperative. Failures of governance – whether bias in hiring tools, manipulated chatbots, opaque insurance algorithms, or unsafe autonomous systems – now translate directly into legal, financial, and reputational consequences at enterprise scale.
Crucially, this exposure is not limited to organizations building large-scale AI systems of their own. AI is now embedded in the everyday vendor tools that companies of every size already rely on – HR and recruitment platforms, marketing and CRM systems, finance and procurement software, customer service helpdesks, and productivity suites. Generative AI assistants such as ChatGPT, Copilot, and Gemini have become part of the daily workflow of most knowledge workers, often without formal oversight. And the rapidly emerging wave of agentic AI – systems that take autonomous actions on behalf of their users and organizations – will extend these risks further still. Under the EU AI Act, obligations on deployers of AI apply regardless of organizational size or whether an organization builds AI itself. Governance is no longer a concern reserved for those at the frontier; it is a baseline competency for any organization operating in an AI-enabled environment.
This engaging two-day training equips participants with a structured, theoretically grounded understanding of AI Governance, complemented by the practical guidance needed to apply it inside their own organizations. The course is anchored in the leading international frameworks and standards – the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001 and ISO/IEC 23894, the OECD AI Principles, and the EU High-Level Expert Group’s Ethics Guidelines for Trustworthy AI – and uses them as a coherent backbone for understanding how lawful, ethical, and robust AI is designed, deployed, and overseen across its lifecycle.
Building on this theoretical foundation, the course translates principles into concrete operating practice. Participants will work through core governance disciplines including risk identification and assessment, AI security and adversarial threat management, transparency and explainability, accountability and human oversight, audit and certification readiness, and the social and environmental impacts of AI systems. Each module combines structured instruction with real-world case studies, group discussion, polls, and short hands-on exercises – ensuring that the frameworks become usable tools rather than abstract references.
Designed for professionals navigating today’s complex AI landscape, the training also explores the interconnection between AI Governance and adjacent disciplines – particularly Data Governance, model risk management, cybersecurity, privacy, and change management. By the end of the two days, participants will be able to articulate a credible AI governance approach for their organization – whether they are structuring the safe and responsible adoption of generative AI and vendor-embedded AI across their workforce, or operating the controls and evidence required for high-risk AI systems they build or deploy. They will understand where AI Governance intersects with their existing data governance, privacy, security, and compliance functions, and will be able to confidently engage with executives, regulators, auditors, vendors, and technical teams.
Day 1 – Foundations, Ethical Pillars, and Core Risk Management
- Module 0 — Welcome & Orientation
- Module 1 — AI Primer
- Module 2 — Data Governance: The Foundation for Trustworthy AI
- Module 3 — Ethics & Principles
- Module 4 — Law & Regulation
- Module 5 — Risk Management
- Module 6 — AI Security
- Module 7 — Auditing Algorithms
Day 2 – Operationalisation, Accountability, and Societal Context
- Module 8 — Transparency & Explainability
- Module 9 — Accountability, Liability, and Human Oversight
- Module 10 — Governance Operating Model
- Module 11 — Audit & Certification
- Module 12 — Social Impact & Sustainability
- Module 13 — Stakeholder Engagement & Participatory Design
- Module 14 — Future of Work & “Getting to Value”
This course is designed for professionals who are accountable for, or actively contributing to, the governance, oversight, deployment, or assurance of AI within their organization – whether the organization is building large-scale AI systems, embedding AI features into its own products, or simply using generative AI assistants and AI-enabled vendor tools in day-to-day operations. It is equally relevant for large corporates standing up dedicated AI governance functions, for small and mid-sized organizations needing a proportionate and pragmatic approach to managing AI risk without dedicated specialist teams, and for those refining established practices in light of the EU AI Act, ISO/IEC 42001, and other emerging requirements. No prior technical background in machine learning is assumed; the course is calibrated for governance-minded practitioners, leaders, and assurance professionals. Specific roles that will find this course particularly beneficial include:
- Chief Data Officers and Chief AI Officers: Leaders who need to define AI strategy, set risk appetite, and stand up coherent governance operating models that connect executive oversight to day-to-day execution.
- Data Governance Leads and Practitioners: Professionals who already operate data governance frameworks and need to extend their reach to encompass AI assets, model risk, and AI-specific evidence requirements.
- AI Governance Officers and Responsible AI Leads: Specialists building or maturing dedicated AI governance functions and needing a structured, defensible framework grounded in international standards.
- Risk, Compliance, and Internal Audit Professionals: Second- and third-line functions responsible for assessing AI risk, validating controls, and providing assurance over high-risk AI systems and their documentation.
- Legal and Privacy Counsel: Practitioners navigating the EU AI Act, GDPR intersections, Fundamental Rights Impact Assessments, and emerging liability frameworks.
- Model Risk Managers and Validation Functions: Professionals extending traditional model risk management methodologies to encompass machine learning and generative AI systems.
- Information Security and CISO Office Members: Security professionals expanding beyond traditional cybersecurity into AI-specific threats including adversarial machine learning, prompt injection, model extraction, and model supply chain risks.
- Product Owners, AI/ML Practitioners, and Solution Architects: Builders and designers of AI systems who need to embed governance, transparency, and human oversight into the AI lifecycle from intake to retirement.
- Procurement and Vendor Management Professionals: Those evaluating third-party AI providers and needing to assess conformity, certifications, and contractual safeguards.
- Business and Functional Leaders Using AI-Enabled Vendor Tools: HR, marketing, finance, customer service, and operations leaders whose teams already rely on AI features inside their procured SaaS platforms, generative AI assistants, and emerging agentic capabilities — and who need to understand their obligations as deployers of AI.
- Leaders in Small and Mid-sized Organizations: Owners, executives, and operations leads in companies that do not build AI themselves but increasingly depend on AI embedded in vendor tools, generative AI, and (soon) agentic systems — and need a proportionate, pragmatic approach to governance that scales to their size and resourcing.
- Executives, Board Members, and Audit Committee Members: Senior stakeholders who need a structured understanding of AI risk and governance to discharge their oversight responsibilities credibly.
Anyone preparing for AI governance certifications: The course provides comprehensive coverage of the concepts, frameworks, and practices that underpin most current AI governance certification programs and academic credentials in data
Senior Data Governance Expert
Mathias Vercauteren BV
Mathias Vercauteren is the founder and principal of DAIG Partners (Data & AI Governance Partners), a premium boutique advisory firm serving clients across Belgium, the Netherlands, Germany, Switzerland, the United Kingdom and the United States. He is the Global Project Manager for DAMA-DMBOK® 3.0 — the next edition of the most widely adopted body of knowledge in data management, co-developed of the Applied Data Governance Practitioner (ADGP®) certification from Dataversity, and is pursuing an Executive PhD in Data Governance at Antwerp Management School. Mathias has more than 16+ years of experience helping organizations design, launch, and mature data and AI governance programs that are pragmatic, sustainable, and aligned to executive priorities. His proprietary Data Governance Sprint™ and AI Governance Sprint™ methodologies have been delivered to clients across banking, insurance, healthcare, public sector, retail, utilities, manufacturing, and logistics. He is also a frequent speaker at the leading international conferences in the field — including Enterprise Data World, DGIQ, Data Modeling Zone, and the Data & AI Conference — and has delivered trainings and workshops for various companies and guest lectures for Vlerick Business School’s MBA program. Mathias is the author of the forthcoming book Data Governance Sprints (expected late 2026, Technics Publications) and AI Governance Sprint (2027, Technics Publications). He brings to this course a rare combination of deep practitioner experience, standards-level authority through his DMBOK 3.0 role, and academic rigour through his ongoing PhD research — translating complex regulatory and ethical frameworks into approaches that participants can apply on Monday morning.
Course Summary
Price: £995 + VAT (Vat only charged to UK residents)
Instructor: Mathias Vercauteren
Duration: 2 Days
Language: English
Certification: Yes – IRM UK Certificate of Completion
