Data & AI

AI Governance Accelerator : From AI Ambition to Governed Execution

This 2-day accelerator turns AI governance from an abstract requirement into a practical operating model. Designed for executives, business owners, data/IT teams, legal, risk, compliance, audit and transformation teams, it creates a shared language for responsible AI and a clear roadmap for action.

Day 1 builds the strategic, regulatory and risk foundation: what AI governance means, why it protects business value and trust, how EU AI Act/GDPR expectations and standards shape decision-making, and how to classify AI use cases by value, impact, risk and accountability.

Day 2 converts that foundation into practical governance artefacts. Participants work on their own or provided AI use cases to design data-readiness checks, lifecycle controls, decision rights, RACI, policy gaps, vendor and agent controls, a readiness scorecard, an AURORA dashboard canvas and a 30/60/90-day implementation backlog.

The result is a concrete starter blueprint that gives participants visibility on how to implement (from business perspective) AI governance, not only why it is important.

Content

Day 1 - AI governance strategy, standards, trends and risk classification

  • AI governance as a system for value creation, risk control, trust, accountability and execution.
  • Why AI governance matters: business impact, compliance expectations, operational resilience and stakeholder confidence.
  • Market and future trends: EU AI Act readiness, ISO/IEC 42001, AI assurance, GenAI, agentic AI and vendor governance.
  • AI stance model: business impact, operational dependency, data sensitivity, external impact and governance intensity.
  • AI use-case classification, risk appetite, human oversight, transparency, explainability and audit evidence.
  • Standards and regulatory backbone: EU AI Act, GDPR, NIST AI RMF, ISO/IEC 42001, DAMA-DMBOK, COBIT, ISO 27001 and NIST CSF.
  • Workshop: classify 2-3 AI use cases and draft the governance stance.

Day 2 - Data governance, lifecycle, roles and starter operating model

  • Data governance for trusted AI: ownership, stewardship, metadata, lineage, data quality, privacy, security, retention, access control and auditability.
  • AI, model and agent lifecycle governance: intake, design, validation, deployment, monitoring, incident response and retirement.
  • Key governance processes: intake, risk review, approval, monitoring, evidence management, escalation and continuous improvement.
  • RACI, decision rights, governance bodies and escalation paths.
  • Policy-stack and control library: privacy, security, fairness, explainability, vendors, GenAI and agent controls.
  • AURORA readiness scorecard and dashboard canvas.
  • Workshop: build a 30/60/90-day starter backlog and executive pitch.

Learning Outcomes

By the end of the training, participants will be able to:

  • Explain what AI governance is and why it matters for value, trust, compliance and accountable execution.
  • Identify the key business building blocks and processes of an AI governance operating model.
  • Recognise market, regulatory and technology trends shaping AI governance implementation.
  • Connect AI ambition to business value, risk control, accountability and trust.
  • Classify AI use cases by business impact, operational dependency, data sensitivity, external impact and governance intensity.
  • Explain the role of data governance as a core control layer for trusted AI.
  • Define starter decision rights, RACI, governance bodies and escalation paths.
  • Identify policy gaps and map lifecycle controls from intake to retirement.
  • Create a starter readiness scorecard, AURORA dashboard canvas and 30/60/90-day implementation backlog.
  • Present a Board/CXO-ready governance starter pitch.

Training Method

Blended accelerator format combining executive framing, market and regulatory insight, short expert inputs, guided templates, practical group exercises, use-case clinics, peer review and a final Board/CXO-style presentation. The course can be delivered in person, remotely or in hybrid format. Participants receive practical templates and reusable course materials that can support follow-up implementation.

Certification
Certificate of Participation
Prerequisites

No advanced technical background is required. A general understanding of digital transformation, data, AI, governance, risk, compliance or business operations is recommended. Participants are encouraged to bring one or two AI use cases, data initiatives or governance challenges from their organisation.


Planning and location
Session 1
30/11/2026 - Monday
09:00 - 17:00
Session 2
01/12/2026 - Tuesday
09:00 - 17:00
Available Edition(s):

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Your trainer(s) for this course
DEMEESTER S.A. R.L.-S, Tom DEMEESTER
Tom DEMEESTER
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Tom Demeester is a seasoned leader in digital transformation, AI strategy, and business innovation with 20+ years of international experience across Europe, the US, and Asia. Harvard Business School (Executive Leadership) and Executive MBA Warwick. He built and scaled global portfolios incl. 90+ Microsoft Cloud/AI/D365 offerings, driving €100M+ revenue. Creator of AI Agent Discovery Track, aligning ROI and EU AI Act readiness. AWS AI Practitioner.