ITIL® AI Governance Training

Learn via : Virtual Classroom / Online

Duration : 3 Days

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ITIL® AI Governance Training

Description

ITIL® AI Governance (Version 5) training is designed to provide participants with practical guidance for governing AI responsibly, effectively, and at scale.

As AI increasingly becomes an integral part of organizational operations, the training provides a holistic understanding of AI governance by combining proven principles with practical, role-aligned guidance. It enables organizations to use, manage, and govern AI with clarity, accountability, and trust.

Throughout the training, participants will learn how to:

Establish clear oversight and accountability for AI across organizational functions
Balance innovation, ethics, and compliance without compromising speed or agility
Maintain a human-centric approach and ensure meaningful human control over AI outcomes
Scale AI responsibly to transform AI ambitions into measurable and sustainable value
Establish a common language across teams to align governance and strengthen trust
Accelerate the responsible adoption of AI across AI-related roles and functions

The program aims to support responsible AI adoption across the organization and enable different roles and functions involved in AI to operate within a common governance framework

Outline

ITIL and Governance Fundamentals

ITIL Fundamentals

Value co-creation through products and services
ITIL Guiding Principles
ITIL Product and Service Lifecycle Model
ITIL Value System (ITIL VS) and its components
ITIL Four Dimensions and their elements
ITIL Maturity Model
ITIL AI Maturity Model
ITIL Continual Improvement Model
ITIL Transformation Model

Governance Fundamentals

Differences between corporate governance, regulation, IT governance, and AI governance
Governance vs. management
Purpose, responsibilities, and decision-making scope
Interaction between governance and management
Governance across organizational contexts and functions
Digital product and service management
Software development and DevOps
Human Resources
Marketing and Finance
Operations
The role of governance within the ITIL Value System
Organizational direction and accountability
Control, risk management, and compliance
Opportunities and threats of governance
Distinguishing governance and management activities through scenarios

AI Fundamentals

Introduction to AI

Types of AI and their appropriate use
Generative AI (GenAI)
Agentic AI
Narrow AI
AI use cases across ITIL
Product and service management
Experience
Strategy and transformation
Key characteristics of AI
Learning
Autonomy
Opacity
Key AI risks
Bias
Privacy
Hallucination
AI in product and service lifecycle management
AI-enabled automation across value chain activities
Opportunities and threats of AI for organizations and professionals

AI Across Industries and Organizational Functions

AI and value co-creation
AI contribution to business outcomes
AI use cases across industries
Creating value with AI across organizational functions
AI across the digital strategy lifecycle
Strategy creation
Strategy execution
Monitoring
Continual adjustment
Strategic opportunities and threats of AI adoption
AI and transformation readiness assessments
Adaptive governance
AI and sustainability considerations
Alignment with relevant UN Sustainable Development Goals (SDGs)
Energy and water usage
Environmental impact and sustainability trade-offs

ITIL AI Capability Model – 6C Model

Introduction to the ITIL AI Capability Model
Differences between AI capabilities
AI capability behaviours
Typical use cases
Associated risks
How AI capabilities influence use cases and behaviour
Impact of AI capabilities on governance requirements
Governance control mechanisms
AI risk profiling using the ITIL AI Capability Model
Decision boundaries
Human hand-offs
Override mechanisms and experience signals
Refusal patterns and hand-off time
Combining multiple AI capabilities in real-world AI solutions
Impact on system behaviour and risk exposure
Identifying high-risk areas through scenario analysis
Defining proportionate governance controls

AI Risks and Ethical Principles

AI Risk Categories

Autonomy in decision-making
Transparency and explainability
Bias and fairness
Hallucinations and mistakes
Access-control misalignment
Data availability and quality
Legal, regulatory, and compliance risks
Operational and lifecycle risks
Third-party AI governance risks
Organizational readiness and skills
Unauthorized disclosure and data leakage
Model drift
Inappropriate autonomy

Ethical Principles for AI

Fairness
Transparency
Accountability
Beneficence
Non-maleficence
Autonomy and human agency
Ethical principles in AI design
Ethical principles in AI operations and decision-making

AI Risk Countermeasures

Human oversight
Explainability measures
Bias audits
Output validation
Access controls
Data-quality management
Audit trails
Lifecycle monitoring
Third-party controls
AI literacy activities
Linking risks to appropriate governance controls
Scenario-based AI risk-control reasoning

AI and Strategy

AI and strategic decision-making
AI-supported prioritization
Resource allocation
Alignment between strategy, value streams, and business outcomes
Shadow AI
Impact of Shadow AI on strategic alignment
Shadow AI risk exposure
Impact on organizational decision-making

AI Governance Key Concepts

Four AI Governance Perspectives

Decision Authority & Risk Management
Ethical Principles
Data Governance
Regulatory Compliance
Interdependencies between governance perspectives
Impact of AI capabilities on governance requirements
The growing need for effective AI governance
AI Governance Lifecycle
Governance and ethical principles for trustworthy AI-enabled products, services, and experiences
IT Governance vs. AI Governance
AI Governance and digital ethics
Shadow AI governance risks
Visibility gaps
Data leakage
Compliance exposure
Inconsistent decision-making
Unclear accountability
Characteristics of good AI governance
Fit-for-purpose governance
Adaptive and evolving governance
Integrated and holistic governance
Ethical and sustainable governance

ITIL AI Governance Model, Improvement Model and Governance Patterns

ITIL AI Governance Models

ITIL AI Governance Model
ITIL AI Governance Improvement Model
Assessment
Design
Implementation
Maintenance
Assurance
Using the ITIL AI Capability Model to assess AI suitability, risks, and controls
Integration with other frameworks and methods

AI Governance Patterns

Directive
Guided
Federated
Autonomous
Decision authority
Assurance and oversight
Impact of governance patterns on AI adoption

Assessing and Stress-Testing AI Governance Readiness

Assessing current AI governance
Ethical principles and governance decision areas
Authority and oversight
Control mechanisms
Escalation and accountability
Governance pattern analysis
Governance components and characteristics
Governance maturity indicators
Stress-testing governance against AI scenarios
Identifying governance breaking points
Decision velocity
Scale
Integration
Risk tolerance
Stakeholder engagement
Accountability
Data governance
Supplier exposure
Compliance
Selecting appropriate governance patterns for AI use cases

Designing AI Governance Requirements

Determining governance requirements for AI use cases
Using the ITIL AI Capability Model
AI risks and ethical considerations
Governance requirements based on autonomy levels
Decision criticality
Regulatory exposure
Organizational readiness
Stakeholder impact
Decision authority
Human oversight
Control mechanisms
Shadow AI governance controls
Current and planned AI use cases
Supplier-provided AI solutions
Predictable future AI use cases
Categorizing AI-enabled systems according to governance needs
Sustainability considerations
Environmental impact
Energy and water usage
Supplier commitments
Monitoring evidence
Lifecycle impacts

Implementing AI Governance Adjustments

Implementing decision boundaries
Human oversight and governance controls
Preventive controls
Detective controls
Corrective controls
Designing human oversight according to autonomy levels
AI risk categories and countermeasures
Shadow AI governance controls
Acceptable-use policies
Approved AI tool catalogues
AI use registers
Access controls
Monitoring
Staff guidance
Escalation routes
Policy updates
Governance bodies and roles
Workflows
Tooling
Training
Supplier arrangements
Delivery integration
Applying ethical and governance principles to AI-enabled products and services
Applying a risk matrix: likelihood × impact

Maintaining, Assuring and Improving AI Governance

ITIL AI Governance Maturity Model
Maturity indicators from Emerging to Stewardship
Auditability
Traceability
Documentation
AI governance assurance
Observability signals
Metrics
Logs
Traces
Experience signals
Monitoring AI governance performance
Measuring governance effectiveness
Continual improvement of governance controls
Responding to changes in AI use
Regulatory changes
Supplier changes
Stakeholder expectations
Changing operating conditions
Using monitoring and evaluation results to improve AI governance

Governance Orientation, Stewardship and Continual Improvement

Stewardship vs. control
Ethical responsibility
Performance management
ITIL Continual Improvement Model
Control-oriented AI governance
Stewardship-oriented AI governance
Stakeholder feedback loops
Continual improvement of AI governance
Impact of governance orientation on long-term AI outcomes
Building and maintaining stakeholder trust

Compliance, Regulation and External Alignment

Current global AI regulations and acts
National AI regulations and acts
Impact of AI regulations on organizations
Regulatory requirements and AI governance
Impact of regulations on governance design and controls
Governance assurance and decision-making
Third-party AI services
AI vendors and governance requirements
Third-party risk management
Applying ethical AI principles and external governance requirements
Compliance controls
Assurance evidence

Practical Application of AI Governance

Scenario Context and Governance Baseline

Understanding organizational and industry context
Organizational function context
Digital maturity
AI adoption level
Stakeholders
Constraints and drivers
Identifying uncertain or missing information

AI Use Case Assessment

Assess and Stress-Test Governance

Determining the current governance pattern
Assessing governance maturity
Governance gap analysis
Identifying weaknesses and risk exposure
Stress-testing and identifying breaking points

Determining AI Governance Requirements

Designing and Implementing AI Governance Adjustments

Designing appropriate governance controls
Decision boundaries
Human oversight
Escalation routes
Implementation pathways
AI risk countermeasures
Shadow AI governance
Third-party AI governance
Sustainability requirements
AI governance in DevOps and continuous delivery environments

Ensuring Governance Effectiveness

Holistic and End-to-End Application

Applying the ITIL AI Governance Model end-to-end
Assessment and stress-testing
Governance requirements
Design
Implementation
Monitoring
Improvement
Structures and organizational culture
Skills and technology
Data
Partners and suppliers
Workflows and value streams
Stakeholder trust
Evaluating trade-offs between ethical principles, business objectives, delivery flow, operational performance, sustainability, governance effectiveness, and stakeholder trust

Prerequisites

Participants are recommended to have a basic understanding of ITIL concepts, corporate governance principles, and general AI technologies and use cases.

There are no mandatory technical prerequisites for attending the training.