A tailored course, built for your situation
Mastering ISO 42001 for Enterprise AI Governance Practitioners
A structured path to lead AI governance with confidence in complex healthcare environments
$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.
Who this is for
Senior governance practitioner in a regulated tech-health environment, leading cross-functional initiative coordination with influence but not direct authority
Who this is not for
Individuals looking for technical AI engineering skills or hands-on coding in machine learning frameworks
What you walk away with
- Visibility: Your AI governance work consistently surfaces in leadership conversations
- Framework fluency: Deploy ISO 42001 controls without slowing down delivery
- Reputation: Become the first internal reference for 'how we handle AI risk here'
- Repeatable artefacts: Templates and playbooks that compound across portfolio initiatives
- Strategic positioning: Shift from contributor to named owner of governance tracks
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 and Its Strategic Role in Healthcare AI
Lay the foundation for AI governance by exploring ISO 42001’s structure, intent, and alignment with healthcare-specific risks and enterprise goals.
12 chapters in this module
- Introduction to ISO 42001 and AI management systems
- How AI governance differs from traditional compliance frameworks
- Key stakeholders in healthcare AI decision-making
- Mapping AI risks to patient safety and regulatory exposure
- Why visibility matters for portfolio-level AI governance
- Enterprise trends driving adoption of formal AI standards
- Role of the Portfolio Manager in AI governance leadership
- Balancing innovation pace with accountability requirements
- First-mover advantage in emerging governance domains
- Case example: AI documentation that prompted executive follow-up
- Connecting ISO 42001 to broader digital health transformation
- Setting expectations for impact beyond compliance checklists
Module 2. Initiating the AI Governance Framework Within Existing Portfolios
Learn how to launch governance without new mandates by embedding ISO 42001 principles into active initiatives and routine reviews.
12 chapters in this module
- Identifying high-visibility AI initiatives in your current portfolio
- Assessing maturity gaps using ISO 42001 as a benchmark
- Integrating governance into existing project kickoffs
- Positioning controls as enablers, not blockers
- Aligning AI oversight with quarterly planning cycles
- Gaining buy-in from technical teams through clarity
- Documenting initial governance scope for leadership review
- Using existing artefacts to accelerate framework adoption
- Creating quick-win governance milestones
- Tracking early adoption signals across teams
- Leveraging Cerner integration points for AI transparency
- Avoiding common launch pitfalls in complex environments
Module 3. Stakeholder Mapping and Influence Without Authority
Build strategic visibility by identifying key decision influencers and designing communication that prompts engagement.
12 chapters in this module
- Who decides AI risk tolerance in healthcare settings
- Charting informal influence paths across technical teams
- Engaging clinical leads as AI governance partners
- Designing artefacts that prompt peer questions
- Timing outreach to align with budget cycles
- Framing governance as strategic enablement
- Building trust through consistency and clarity
- Using ISO 42001 to standardize cross-team expectations
- Reducing friction in vendor AI integration reviews
- Positioning yourself as the connective layer
- Documenting decisions to reduce rework
- Measuring influence through follow-up requests
Module 4. Control Design for Real-World AI Deployments
Translate ISO 42001 clauses into actionable control points aligned with actual AI workflows in healthcare systems.
12 chapters in this module
- Breaking down ISO 42001 control objectives by function
- Mapping controls to data provenance in AI pipelines
- Designing audit-ready decision logs for AI models
- Ensuring human oversight is documented and enforceable
- Managing model versioning and drift detection
- Addressing bias testing in clinical decision support
- Integrating controls into DevOps and CI/CD cycles
- Balancing automation with governance requirements
- Documenting exceptions with clear rationale
- Creating reusable control templates for future projects
- Linking controls to incident response playbooks
- Validating control effectiveness beyond checklists
Module 5. Documentation Architecture for Long-Term Governance
Design living documentation systems that maintain relevance as teams and technologies evolve.
12 chapters in this module
- Choosing the right level of detail for governance artefacts
- Structuring documentation for searchability and reuse
- Versioning governance assets across AI lifecycle phases
- Integrating documentation with project management tools
- Building templates that survive team turnover
- Using standard sections to accelerate reviews
- Embedding ISO 42001 language without overloading teams
- Linking artefacts to training and onboarding
- Automating updates from code and configuration changes
- Ensuring accessibility for non-technical reviewers
- Creating executive summaries that drive engagement
- Measuring documentation effectiveness through adoption
Module 6. Operationalizing AI Risk Assessments
Implement structured risk assessment workflows that produce actionable insights and executive confidence.
12 chapters in this module
- Defining scope for AI risk assessments in healthcare
- Identifying high-consequence AI use cases
- Engaging domain experts in risk evaluation
- Using ISO 42001 to structure assessment criteria
- Documenting risk decisions with traceable rationale
- Integrating risk outputs into portfolio planning
- Creating repeatable assessment templates
- Managing third-party AI vendor risks
- Aligning risk posture with organizational tolerance
- Reporting assessment outcomes to leadership
- Updating assessments based on performance data
- Avoiding analysis paralysis in fast-moving environments
Module 7. Cross-Functional Governance Integration
Embed AI governance into existing workflows across security, compliance, legal, and engineering teams.
12 chapters in this module
- Understanding the priorities of adjacent functions
- Aligning AI governance with security review cycles
- Integrating with privacy programs and data governance
- Working with legal on AI liability and disclosures
- Coordinating with clinical validation teams
- Timing governance checkpoints with release schedules
- Creating shared ownership models for AI oversight
- Reducing duplication through integrated artefacts
- Building trust through early and consistent engagement
- Handling conflicting requirements across teams
- Designing governance that scales with team growth
- Measuring integration success through reduced friction
Module 8. Vendor and Third-Party AI Oversight
Establish clear expectations and review processes for external AI components and integrations.
12 chapters in this module
- Assessing vendor AI capabilities against ISO 42001
- Defining minimum governance requirements for RFPs
- Evaluating third-party model documentation quality
- Managing black-box AI components in clinical systems
- Creating vendor assessment scorecards
- Integrating vendor oversight into procurement workflows
- Documenting due diligence for regulatory review
- Handling AI model updates from external providers
- Building right-to-audit clauses into contracts
- Ensuring continuity during vendor transitions
- Reducing integration risk through standardization
- Tracking vendor compliance over time
Module 9. Audit and Regulatory Readiness
Prepare for internal and external reviews with documentation and processes that pass scrutiny the first time.
12 chapters in this module
- Anticipating auditor questions on AI governance
- Structuring evidence to demonstrate compliance
- Using ISO 42001 to organize audit materials
- Documenting control effectiveness with examples
- Preparing for regulator inquiries on AI decisions
- Creating audit response timelines and workflows
- Training teams on audit communication protocols
- Identifying common audit findings in AI systems
- Building continuous monitoring into governance
- Reducing audit prep time through living artefacts
- Demonstrating improvement over time
- Maintaining independence in self-assessments
Module 10. Scaling Governance Across the Portfolio
Extend governance practices across multiple initiatives while maintaining consistency and reducing overhead.
12 chapters in this module
- Identifying patterns across AI projects for reuse
- Creating standardized onboarding for new teams
- Developing tiered governance based on risk level
- Automating routine governance checks
- Using dashboards to monitor portfolio health
- Building internal training programs for AI governance
- Maintaining quality during rapid expansion
- Avoiding governance bottlenecks in delivery
- Sharing best practices across project teams
- Updating governance based on lessons learned
- Measuring scalability through team feedback
- Ensuring consistency without stifling innovation
Module 11. Measuring the Impact of AI Governance
Define and track meaningful metrics that demonstrate value and justify continued investment.
12 chapters in this module
- Choosing metrics that resonate with leadership
- Tracking reduction in AI-related incidents
- Measuring time saved in audit and review cycles
- Assessing improvements in model documentation
- Evaluating stakeholder trust through surveys
- Linking governance to faster time-to-market
- Demonstrating compliance efficiency gains
- Using metrics to prioritize governance initiatives
- Balancing qualitative and quantitative measures
- Reporting impact in business terms
- Avoiding vanity metrics in governance reporting
- Iterating on measurement based on feedback
Module 12. Sustaining Governance Through Change
Ensure long-term success by designing governance that survives leadership changes and organizational shifts.
12 chapters in this module
- Designing governance for resilience
- Documenting institutional knowledge systematically
- Building redundancy into key roles
- Updating governance in response to regulatory changes
- Adapting to new AI technologies and use cases
- Maintaining engagement during leadership transitions
- Embedding governance into onboarding and training
- Using ISO 42001 as a continuity anchor
- Creating feedback loops for continuous improvement
- Balancing stability with agility
- Recognizing and rewarding governance contributions
- Planning for the next evolution of AI standards
How this maps to your situation
- AI governance in healthcare IT environments
- Portfolio-level coordination without direct authority
- Regulatory and compliance expectations in digital health
- Cross-team integration in large enterprise settings
Before vs. after
Before
Overlooked contributions to AI governance, buried in technical details with limited executive reach
After
Recognized leadership in shaping AI accountability, with work consistently elevated into strategic discussions
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: 90 minutes per week for 12 weeks, with flexible pacing and self-assessment checkpoints.
If nothing changes
Remaining invisible despite high-impact work means missed opportunities for influence and career growth, while peers who formalize their approach gain recognition and expanded scope.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to healthcare AI governance, with specific tools for portfolio managers operating in regulated environments. It avoids theoretical overviews in favor of actionable architecture and real-world implementation.
Frequently asked
Is this course technical or managerial?
It's designed for practitioners who bridge both worlds, focused on governance structure, visibility, and influence, not coding or infrastructure.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get promoted?
By making your contributions more visible and repeatable, it positions you as a strategic leader, which often precedes formal advancement.
$199 one-time. 90 minutes per week for 12 weeks, with flexible pacing and self-assessment checkpoints..
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