A tailored course, built for your situation
Mastering ISO 42001 for Senior Client Strategy Practitioners
Build AI governance systems with decision rights embedded from day one
$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 client-facing strategy practitioner at global professional services firm, advising on governance, risk, and compliance for emerging tech deployments
Who this is not for
Entry-level consultants, internal IT teams, or engineers building AI systems without client advisory context
What you walk away with
- Own final sign-off on AI use-case eligibility for regulated sectors
- Define the scope boundaries for external AI audit engagements
- Approve client proposal language on AI compliance commitments
- Control the threshold for 'compliant enough' in cross-jurisdictional deployments
The 12 modules (with all 144 chapters)
Module 1. ISO 42001 Foundations for Client-Facing Strategy
Ground your governance advice in the actual structure of ISO 42001, focusing on clauses most invoked in client contracts and procurement reviews. Learn how each article translates to decision points you can claim in client engagements.
12 chapters in this module
- Understanding the purpose and scope of ISO 42001
- How ISO 42001 differs from prior AI ethics guidelines
- Mapping client risk appetite to clause 4.1
- Defining organizational context before engagement kickoff
- Identifying interested parties in multi-jurisdictional deals
- Setting boundaries for AI system oversight
- Determining leadership responsibilities upfront
- Establishing governance roles with client buy-in
- Documenting policy intent with legal enforceability
- Aligning internal standards with ISO 42001 requirements
- Integrating compliance into client onboarding
- Planning for continual improvement in client programs
Module 2. Claiming Decision Rights in AI Governance Design
Identify where you can assert ownership over core design choices, such as which AI systems require human override, who defines fairness thresholds, and when bias testing is considered sufficient, without escalating to senior review.
12 chapters in this module
- Recognizing decision ownership opportunities in clause 5
- Defining leadership commitment in client programs
- Establishing policy statements with enforcement power
- Assigning accountability for AI outcomes
- Ensuring resources are allocated appropriately
- Communicating AI principles to client teams
- Documenting governance decisions visibly
- Ensuring leadership leads by example
- Evaluating performance of governance framework
- Reviewing policies with client stakeholders
- Updating commitments based on feedback
- Tracking decision impact across engagements
Module 3. Setting Boundaries for AI System Scope
Control how AI systems are defined at the outset, what’s in, what’s out, so you prevent mission creep and scope expansion that erodes governance authority.
12 chapters in this module
- Determining applicability of ISO 42001 to specific use cases
- Defining system boundaries with legal precision
- Identifying internal and external processes
- Documenting data flows and dependencies
- Specifying human oversight requirements
- Mapping AI lifecycle stages clearly
- Setting thresholds for model retraining
- Establishing version control for AI systems
- Clarifying roles in development and deployment
- Ensuring transparency in system updates
- Managing changes to system scope
- Auditing boundary compliance over time
Module 4. Owning Risk Assessment and Treatment Criteria
Finalise what counts as acceptable risk in client AI initiatives, including how bias is measured, when mitigation is required, and what constitutes sufficient documentation.
12 chapters in this module
- Understanding AI risk assessment fundamentals
- Identifying potential harms from AI use
- Assessing severity and likelihood of impacts
- Involving stakeholders in risk evaluation
- Determining risk tolerance levels for clients
- Classifying risk categories appropriately
- Applying treatment options effectively
- Avoiding unnecessary risk avoidance
- Documenting risk decisions transparently
- Reviewing risk treatments periodically
- Updating assessments based on new data
- Ensuring consistency across client portfolios
Module 5. Final Approval on Human Oversight Requirements
Decide exactly when and how human intervention must occur in AI decision chains, and enforce those rules in client deliverables.
12 chapters in this module
- Understanding the need for human oversight
- Defining meaningful control over automated decisions
- Specifying intervention points in workflows
- Determining response times for human review
- Training personnel on intervention protocols
- Documenting oversight procedures clearly
- Testing human-in-the-loop effectiveness
- Ensuring real-time monitoring capability
- Updating oversight rules as needed
- Auditing compliance with oversight policies
- Balancing automation with accountability
- Communicating oversight requirements to clients
Module 6. Controlling Data Management Expectations
Set the standard for what data quality, provenance, and lineage documentation must be provided by clients or vendors before AI deployment proceeds.
12 chapters in this module
- Understanding data lifecycle management principles
- Specifying data collection methods and sources
- Ensuring lawful data acquisition practices
- Documenting data provenance and history
- Establishing data quality metrics
- Managing data lineage across systems
- Protecting personal and sensitive data
- Ensuring data retention policies are followed
- Handling data deletion requests properly
- Auditing data management processes
- Updating data policies based on feedback
- Communicating expectations to third parties
Module 7. Approving Model Development and Testing Standards
Own the criteria for what counts as a valid AI model, how it's trained, tested, and evaluated, so client teams can’t bypass governance under the guise of technical necessity.
12 chapters in this module
- Understanding model development lifecycle
- Specifying training data requirements
- Ensuring representativeness of datasets
- Evaluating model performance metrics
- Testing for bias and fairness systematically
- Validating model accuracy and reliability
- Documenting model development process
- Ensuring reproducibility of results
- Reviewing model updates before deployment
- Establishing model monitoring procedures
- Handling model drift and degradation
- Auditing model development practices
Module 8. Decision Rights Over AI System Deployment
Control when and how AI systems go live, including what evidence must be provided to demonstrate readiness for production use.
12 chapters in this module
- Understanding deployment lifecycle stages
- Specifying pre-deployment checklist items
- Reviewing system documentation thoroughly
- Validating testing and evaluation results
- Ensuring compliance with governance policies
- Obtaining necessary approvals before launch
- Communicating deployment plans to stakeholders
- Monitoring initial performance closely
- Addressing issues during early operation
- Updating deployment procedures as needed
- Auditing deployment compliance regularly
- Ensuring rollback capability exists
Module 9. Owning Post-Deployment Monitoring Criteria
Define how performance is tracked after launch, including drift detection, feedback loops, and incident reporting, so you maintain control beyond go-live.
12 chapters in this module
- Understanding the importance of monitoring
- Setting performance thresholds for alerts
- Tracking model drift and degradation
- Collecting user feedback systematically
- Reporting incidents promptly and accurately
- Investigating root causes effectively
- Updating models based on monitoring data
- Ensuring transparency in reporting
- Reviewing monitoring processes periodically
- Improving monitoring over time
- Auditing compliance with monitoring rules
- Communicating insights to stakeholders
Module 10. Final Say on AI Incident Response Plans
Decide how breaches, biases, or failures are classified, escalated, and resolved, without needing alignment from adjacent teams.
12 chapters in this module
- Understanding incident response fundamentals
- Identifying potential AI-related incidents
- Classifying incident severity levels
- Establishing response teams and roles
- Developing response playbooks
- Testing response procedures regularly
- Reporting incidents to internal stakeholders
- Notifying affected parties appropriately
- Conducting post-incident reviews
- Updating response plans based on findings
- Ensuring legal and regulatory compliance
- Auditing response effectiveness
Module 11. Controlling Third-Party Audit Boundaries
Define what external auditors can assess, how evidence is collected, and what findings are considered actionable, without deferring to legal or compliance.
12 chapters in this module
- Understanding third-party audit requirements
- Selecting qualified auditors independently
- Defining scope of audit engagements
- Providing necessary documentation
- Observing audit processes directly
- Evaluating audit findings critically
- Responding to recommendations appropriately
- Ensuring follow-up on corrective actions
- Maintaining independence from auditors
- Auditing auditor performance periodically
- Updating audit criteria based on experience
- Communicating audit outcomes to leadership
Module 12. Maintaining Governance Authority Through Renewals
Ensure your governance decisions persist across contract cycles, renewals, and team changes, by building self-sustaining processes that survive turnover.
12 chapters in this module
- Understanding the need for continual improvement
- Monitoring governance performance regularly
- Evaluating effectiveness of controls
- Identifying areas for enhancement
- Implementing corrective actions promptly
- Updating policies based on lessons learned
- Ensuring knowledge transfer across teams
- Documenting improvements systematically
- Reviewing governance framework annually
- Aligning with evolving standards
- Communicating updates to stakeholders
- Sustaining governance culture over time
How this maps to your situation
- Client proposal development
- AI system scoping and approval
- External audit preparation
- Regulatory readiness assessment
Before vs. after
Before
Advised on AI governance with limited authority to finalize standards or stop non-compliant deployments
After
Owns final approval on AI use-case eligibility, audit boundaries, and client proposal language
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 over six weeks, or one intensive weekend
How this compares to the alternatives
Generic AI ethics courses provide principles but no decision rights; internal firm training often lacks ISO 42001 specificity; public webinars skip implementation. This course delivers exact levers to own in client-facing strategy work.
Frequently asked
How is this different from general AI ethics training?
It focuses on decision ownership within ISO 42001, not just concepts. You’ll gain specific claim points over client-facing deliverables.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me influence client contracts?
Yes, each module maps to artefacts like proposals, audit boundaries, and governance summaries you control.
$199 one-time. 90 minutes per week over six weeks, or one intensive weekend.
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