A tailored course, built for your situation
Mastering ISO 42001 for General Managers in Global Services
Build AI governance maturity that scales with client trust and executive expectations
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
AI governance efforts getting lost in delivery timelines
The situation this course is for
Initiatives are launched with strong intent but fail to generate recognition because they lack standardized structure or executive-facing outputs. Teams repeat work, miss alignment opportunities, and governance remains invisible despite heavy lifting.
Who this is for
Senior services leader responsible for delivery quality and compliance convergence, navigating AI adoption across client portfolios
Who this is not for
Individual contributors focused only on technical AI implementation without governance or client escalation scope
What you walk away with
- Clear executive visibility on your AI governance initiatives
- Structured implementation roadmap for ISO 42001 aligned to services delivery cycles
- Client-facing documentation that demonstrates compliance maturity
- Internal recognition as a leader shaping responsible AI adoption
- Reusable artefacts for audit readiness and stakeholder reporting
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 and Its Strategic Role in Global Services
Establish a foundational understanding of ISO 42001 principles and how they align with client expectations, regulatory trends, and organizational risk posture in the services sector.
12 chapters in this module
- Overview of ISO 42001 standards and structure
- Key differences between ISO 42001 and legacy governance frameworks
- Mapping AI governance to client contract requirements
- Defining the scope of AI systems within service delivery
- Identifying stakeholders in AI governance implementation
- Understanding organizational roles and responsibilities
- Linking ISO 42001 to existing compliance programs
- Assessing organizational AI maturity level
- Benchmarking against industry peer adoption
- Integrating AI governance into client onboarding workflows
- Documenting AI system inventories for compliance tracking
- Establishing leadership accountability for AI governance
Module 2. Scoping AI Management Systems for Services Engagement Models
Learn how to define the boundaries and applicability of AI governance within diverse client engagements and delivery timelines.
12 chapters in this module
- Defining AI system scope across service lines
- Differentiating client-owned vs. provider-operated AI
- Creating governance boundary diagrams for client review
- Managing multi-vendor AI integration risks
- Establishing service-level expectations for AI performance
- Documenting AI use cases in client proposals
- Aligning governance scope with commercial agreements
- Identifying high-risk AI applications by client sector
- Building client-specific control baselines
- Using risk heat maps for engagement scoping
- Setting thresholds for AI model monitoring frequency
- Documenting exceptions and risk acceptances
Module 3. Leadership Commitment and Governance Structure Design
Structure leadership engagement and cross-functional oversight mechanisms that meet ISO 42001 requirements and internal expectations.
12 chapters in this module
- Articulating leadership commitment to AI governance
- Designing governance steering committees for client accounts
- Assigning AI governance roles within delivery teams
- Integrating AI oversight into existing leadership forums
- Establishing escalation paths for AI-related incidents
- Defining decision rights for AI deployment approvals
- Creating accountability matrices for AI lifecycle stages
- Documenting governance charters for client transparency
- Aligning AI priorities with business development goals
- Measuring leadership engagement in governance reviews
- Reporting AI governance status to senior management
- Maintaining leadership sign-off records for audits
Module 4. Risk Assessment and Treatment Planning for AI Systems
Implement a repeatable process to identify, assess, and mitigate AI-related risks within services delivery environments.
12 chapters in this module
- Identifying AI-specific risk factors in client operations
- Classifying risks by impact and likelihood for reporting
- Developing AI risk criteria with client stakeholders
- Conducting risk assessments for new AI deployments
- Evaluating bias and fairness in client-facing AI models
- Assessing data quality and provenance across systems
- Mapping privacy risks in AI data processing workflows
- Evaluating explainability and transparency readiness
- Prioritizing risk treatment actions by severity level
- Designing controls for high-risk AI use cases
- Documenting risk treatment decisions and follow-up
- Updating risk registers in response to client changes
Module 5. AI System Lifecycle Controls and Monitoring Procedures
Develop operational controls to govern AI systems throughout development, deployment, and decommissioning phases.
12 chapters in this module
- Establishing AI model development standards
- Defining data management practices for training sets
- Implementing model validation protocols pre-deployment
- Setting up AI performance monitoring dashboards
- Creating alerts for model drift and degradation
- Designing human oversight mechanisms for AI decisions
- Enabling model version tracking and rollback capability
- Implementing logging for model inputs and outputs
- Securing access to AI model infrastructure
- Establishing incident response for AI failures
- Planning for model retraining and updates
- Documenting decommissioning procedures for AI systems
Module 6. Performance Evaluation and Continuous Improvement
Build feedback loops to assess AI governance effectiveness and drive maturity improvements over time.
12 chapters in this module
- Setting KPIs for AI governance program success
- Measuring adherence to established control frameworks
- Tracking AI incident frequency and resolution times
- Assessing client satisfaction with AI governance
- Conducting internal audits of AI management systems
- Evaluating effectiveness of risk treatment plans
- Reviewing AI control performance quarterly
- Identifying opportunities for process automation
- Benchmarking governance maturity across engagements
- Using lessons learned to update governance policies
- Planning corrective actions for audit findings
- Reporting improvements to leadership forums
Module 7. Documentation and Evidence Management for Compliance
Create and maintain records that demonstrate ISO 42001 compliance during internal and client-led reviews.
12 chapters in this module
- Identifying required documentation for ISO 42001
- Creating master document register for AI governance
- Standardizing templates for policy and procedure writing
- Maintaining version control for governance documents
- Storing records in compliance with data retention laws
- Preparing documentation for client assurance requests
- Organizing evidence for third-party audits
- Using metadata to track document ownership and dates
- Creating index of controls and corresponding evidence
- Automating document generation from workflows
- Redacting sensitive information in shared deliverables
- Validating completeness of compliance dossiers
Module 8. Internal Audit and Compliance Verification Processes
Conduct audits to verify AI governance implementation and readiness for external assessments.
12 chapters in this module
- Planning internal audit schedules for AI governance
- Developing audit checklists aligned to ISO 42001 clauses
- Selecting sample AI systems for review
- Conducting interviews with AI system owners
- Verifying risk assessment documentation completeness
- Testing effectiveness of implemented controls
- Documenting audit findings and observations
- Prioritizing non-conformities for remediation
- Tracking corrective action progress to closure
- Reporting audit results to governance committees
- Preparing for external audit cycles
- Using audit insights to improve governance framework
Module 9. Client Assurance and Third-Party Review Readiness
Prepare for client-led assessments and external audits with confidence and consistency.
12 chapters in this module
- Understanding client assurance review expectations
- Preparing response packages for RFPs and due diligence
- Organizing documentation for fast retrieval
- Conducting mock audits for readiness validation
- Training spokespeople for compliance inquiries
- Addressing common client concerns about AI risks
- Demonstrating control effectiveness with evidence
- Responding to auditor findings and follow-ups
- Maintaining consistency across global engagements
- Using standardized narratives for client reporting
- Updating assurance materials post-audit
- Building reputation as a trusted compliance partner
Module 10. Training and Awareness for AI Governance Adoption
Drive organization-wide understanding and ownership of AI governance practices.
12 chapters in this module
- Assessing training needs across delivery teams
- Designing role-based training programs for staff
- Developing onboarding materials for new hires
- Creating microlearning content for busy practitioners
- Delivering workshops on AI risk identification
- Establishing AI governance certification paths
- Tracking training completion and competency levels
- Communicating updates to governance policies
- Sharing success stories from client engagements
- Building internal communities of practice
- Gamifying compliance learning experiences
- Evaluating training effectiveness through assessments
Module 11. Integration with Existing Compliance and Quality Frameworks
Align ISO 42001 implementation with other organizational standards and systems.
12 chapters in this module
- Mapping ISO 42001 to ISO 27001 controls
- Integrating with SOC 2 compliance efforts
- Aligning with COBIT governance objectives
- Connecting to enterprise risk management frameworks
- Harmonizing with ISO 9001 quality processes
- Linking AI governance to service delivery SLAs
- Using ServiceNow for control tracking and workflows
- Automating evidence collection across platforms
- Consolidating reporting across compliance domains
- Reducing duplication in audit preparation
- Creating unified dashboards for leadership review
- Driving cross-functional synergy in governance
Module 12. Sustaining and Scaling AI Governance Maturity
Establish long-term success patterns for growing AI governance maturity across the organization.
12 chapters in this module
- Measuring ROI of AI governance initiatives
- Identifying opportunities for automation and tooling
- Scaling successful pilots to other service lines
- Building business cases for governance investment
- Demonstrating value to client stakeholders
- Recognizing team contributions to compliance
- Updating governance framework with new regulations
- Staying current with ISO 42001 revisions
- Mentoring emerging leaders in AI governance
- Contributing thought leadership to industry forums
- Documenting playbooks for leadership transitions
- Ensuring continuity of governance practices
How this maps to your situation
- General Manager with oversight of AI-enabled services
- Operating under heightened efficiency pressure
- Positioned to influence client trust through governance
- Needing structured outputs for executive visibility
Before vs. after
Before
AI governance efforts remain execution-level tasks without structured outputs or leadership recognition
After
Visible leadership contributions with standardized documentation, client-ready narratives, and internal recognition as a strategic enabler
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 90 minutes per week over six weeks, flexible pacing with lifetime access.
If nothing changes
Without structured governance, AI initiatives may face client scrutiny, miss strategic alignment, and fail to generate recognition despite significant effort.
How this compares to the alternatives
Generic AI ethics courses focus on principles without implementation; this program delivers actionable steps, templates, and contextual guidance specific to services leadership and ISO 42001 adoption.
Frequently asked
Who is this course designed for?
General Managers and senior delivery leaders in professional services who are accountable for AI governance, compliance alignment, and client assurance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive any practical tools?
Yes, every module includes downloadable templates, checklists, and real-world examples tailored to services delivery contexts.
$199 one-time. Approximately 90 minutes per week over six weeks, flexible pacing with lifetime access..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours