This course covers AI governance foundations, organizational accountability, AI capabilities and risks, and the application of ITIL to AI-enabled products and services. It explores governance controls, the ITIL AI Governance Improvement Model, and the implementation and continual improvement of governance practices. Topics include AI data governance, model validation and monitoring, incident management, third-party AI risks, and alignment with frameworks, regulations and standards.
Duration 3 Days – 21 hrs.
Objectives
- Explain AI concepts, capabilities, limitations and the need for AI governance.
- Describe governance principles, structures, roles, accountability and oversight.
- Identify AI opportunities, risks and aspects requiring governance, including data quality, provenance and appropriate data use.
- Identify governance gaps and appropriate controls, including transparency, human oversight, model validation and performance monitoring.
- Explain how ITIL supports governance across AI-enabled product and service lifecycles.
- Apply the ITIL AI Governance Improvement Model to assess, stress-test, define, design, implement and maintain governance improvements.
- Explain AI incident management, escalation, response and lessons learned.
- Connect organisational AI governance with relevant frameworks, regulatory requirements, standards and external obligations.
Target Audience
- Professionals responsible for organisational governance, policies, accountability and oversight.
- IT service management professionals involved in AI-enabled products and services.
- Professionals responsible for AI risks, controls, compliance, privacy and data protection.
- Business and functional leaders identifying AI adoption opportunities and organisational readiness.
- Professionals responsible for AI implementation, monitoring, incident management and continual improvement.
Prerequisites
- Basic understanding of organizational policies, processes and responsibilities.
- General awareness of artificial intelligence and its use in business.
- Familiarity with IT service management concepts and product or service lifecycles.
- Basic awareness of risk management, compliance and data protection.
Course Outline
DAY 1 – Foundations of AI and Governance
Module 1: The AI World and the Need for AI Governance
- Introduction to Artificial Intelligence and its evolution
- AI adoption across organizations and industries
- AI governance: key terms and concepts
- Why organizations need AI governance
- Business opportunities, challenges and responsible adoption
Module 2: What Is Governance?
- Governance principles, objectives and practices
- Organizational governance structures
- Decision-making authority, accountability and oversight
- Governance roles and responsibilities
- Policies, controls and reporting mechanisms
Module 3: What Is AI and What Can Be Governed?
- Types of AI and common applications
- AI capabilities, characteristics and limitations
- Organizational AI use cases and opportunities
- AI-related risks and challenges
- Identifying the aspects of AI that require governance
- Additional topic: AI data governance, including data quality, provenance and appropriate data use
DAY 2 – AI Risks, Controls and the ITIL Governance Approach
Module 4: When AI Governance Fails – Capabilities, Risks and Controls
- ITIL AI Capability Model
- AI risks, ethical considerations and organizational impact
- Transparency, accountability and human oversight
- Identifying governance gaps and control weaknesses
- Establishing appropriate governance controls
- Additional topic: Model validation, performance monitoring and managing AI model drift
Module 5: ITIL as a Governance Toolkit
- ITIL as an approach to governance
- Applying ITIL to AI-enabled products and services
- AI across product and service lifecycles
- AI-enabled automation across value chain activities
- Integrating AI governance with IT service management
- Aligning AI initiatives with business objectives
Module 6: AI Governance Improvement Model – Assess, Stress-Test, Define and Design
- Understanding the ITIL AI Governance Improvement Model
- Assessing AI governance readiness and current practices
- Stress-testing governance arrangements
- Identifying gaps, risks and improvement opportunities
- Defining governance requirements
- Designing appropriate governance adjustments
DAY 3 – Implementing, Improving, and Connecting AI Governance
Module 7: AI Governance Improvement Model – Implement and Maintain
- Implementing governance improvements
- Establishing policies, processes and procedures
- Governance orientation, ownership and stewardship
- Monitoring and assuring governance effectiveness
- Compliance, maintenance and continual improvement
- Applying the model through practical scenarios
- Additional topic: AI incident management, escalation, response and lessons learned
Module 8: AI in Your World – Unlock AI in Your Role, Industry and Organizational Function
- AI opportunities across industries and functions
- AI in business operations and decision-making
- Assessing organizational AI readiness
- Identifying relevant AI adoption opportunities
- Applying governance to real-world business scenarios
Module 9: Connecting AI Governance with Frameworks, Regulations and Standards
- Connecting ITIL AI governance with other frameworks
- Understanding relevant regulatory requirements
- AI compliance, privacy and data protection considerations
- Integrating governance standards and practices
- Managing third-party AI risks
- Aligning organizational governance with external obligations
