ITIL® AI Governance Version 5

What You Will Be Able to Do

• Distinguish AI governance from management, leadership, and regulation, and understand how each contributes to responsible AI use.
• Understand AI types and characteristics and how capabilities influence use cases, risks, and governance needs.
• Assess an AI use case through outcomes, costs, risks, service characteristics, sustainability, and applicable regulation.
• Identify AI risk categories, ethical principles, and countermeasures and connect them to governance requirements and controls.
• Use the ITIL AI Governance Improvement Model to assess and stress-test current governance, define requirements, design adjustments, implement improvements, and maintain them.
• Apply governance requirements in scenarios involving suppliers, third-party AI services, delivery, and operational environments.

Who This Course Is For

• Organizations and professionals adopting AI who need clearer accountability and controls around its use.
• Chief information officers and technology leaders accountable for AI oversight.
• Product managers responsible for AI-enabled digital products and services.
• Enterprise architects and IT business analysts shaping AI-enabled solutions.
• IT delivery managers and product-development leaders introducing AI into live use.
• Professionals responsible for AI risk, compliance, suppliers, and third-party arrangements.

Skills & Competencies

• Place an organization on the governance maturity spectrum and distinguish governance from management, leadership, and regulation.
• Map an AI system to the six capabilities of the ITIL AI Capability Model and identify the risk category introduced by each capability.
• Assess an AI use case through outcomes, costs, risks, service characteristics, sustainability, and regulation.
• Assess and stress-test a governance system using Contextual Risk Factors and risk-priority reasoning.
• Produce a governance design that covers decision boundaries, human oversight, escalation, evidence, and monitoring needs.
• Produce a practical implementation and maintenance plan, including a bridging mechanism with success and retirement criteria.
• Embed AI governance requirements into PRINCE2 stages and decision points and into DevOps delivery pipelines.
• Align internal AI governance with external standards and regulation, including ISO/IEC 42001 and the EU AI Act.

Module 1: The AI world and the need for AI governance

• The difference between having an AI policy and having real governance in place.
• How AI use is changing the workplace and what this means for organizations and professionals.
• Where an organization sits on the governance maturity spectrum and what good governance makes possible.
• Group activity: diagnose accountability and missing controls in an ungoverned AI deployment.

Module 2: What is governance?

• Governance, AI governance, management, leadership, and responsible AI use.
• The difference between corporate, IT, and AI governance and external regulation.
• How governance and management work together to create value.
• Group activity: decide whether a scenario is a governance or management issue.

Module 3: What is AI and what can be governed?

• AI types including Narrow, Generative, and Agentic AI, and when each is used.
• AI use cases across industries and organizational functions.
• AI risks including bias, privacy, and hallucination.
• Assessing AI value at use-case level through outcomes, costs, risks, sustainability, regulation, and governance concerns.

Module 4: When AI governance fails: capabilities, risks, and controls

• The ITIL AI Capability Model and how its capabilities differ.
• How capabilities shape boundaries, risk profiles, and control needs.
• AI risk categories, ethical principles, and countermeasures.
• The four AI governance perspectives and how they interact.
• How Shadow AI affects strategy and alignment.
• Group activity: map an AI system to the capability model and identify the risk introduced by each capability.

Module 5: ITIL as a governance toolkit

• How digital products and services enable value co-creation.
• The ITIL Value System and the Four Dimensions of Product and Service Management.
• The ITIL Product and Service Lifecycle Model and lifecycle activities.
• The ITIL Guiding Principles and how they guide decisions and behaviour.
• The ITIL Maturity Model, Continual Improvement Model, and Transformation Model.
• How AI can support lifecycle activities and enable automation.

Module 6: ITIL AI Governance Improvement Model: assess, stress-test, define, and design

• AI governance key terms and the four governance perspectives.
• Why IT governance alone is insufficient for AI.
• The four governance patterns and the steps in the ITIL AI Governance Improvement Model.
• Assessing and stress-testing the current governance system.
• Defining governance requirements and designing adjustments.
• Practical output: a justified governance design package ready for implementation.

Module 7: ITIL AI Governance Improvement Model: implement and maintain

• Turning agreed requirements into roles, workflows, tooling, training, controls, escalation, monitoring, and evidence.
• Implementing decision boundaries, human oversight, and Shadow AI controls in day-to-day work.
• Using bridging mechanisms while permanent capabilities mature.
• Maintaining governance through monitoring, auditability, evidence, stakeholder feedback, and continual improvement.
• How regulation, external standards, and third-party AI services shape governance and assurance.
• Practical output: an implementation and maintenance plan for one AI use case.

Module 8: AI in your world: role, industry, and organizational function

• How AI supports business outcomes and value co-creation.
• AI use cases across industries and organizational functions.
• How AI supports digital strategy, transformation readiness, and adaptive governance.
• The benefits and risks of AI adoption and how sustainability shapes value.
• Group activity: assess the context of an AI use case before deciding governance, controls, or oversight.

Module 9: Connecting AI governance with frameworks, regulations, and standards

• How ITIL, PRINCE2, DevOps, external standards, and regulation provide complementary support.
• How the ITIL AI Governance Improvement Model integrates with other frameworks and methods.
• Applying AI governance requirements within PRINCE2 stages and decision points.
• Applying AI governance within DevOps practices and continuous-delivery pipelines.
• Understanding the different roles played by frameworks, standards, and regulation.