A tailored course, built for your situation
Direct influence on AI governance decisions with ISO 42001
A tailored course for senior practitioners shaping AI policy where it starts
Who this is for
Senior service delivery leader in global consulting or systems integration, accountable for governance outcomes in AI-enabled engagements
Who this is not for
Individual contributors focused on technical implementation only, or compliance analysts without decision-shaping scope
What you walk away with
- Recognized source of truth in cross-functional AI governance discussions
- Ready-made reasoning and examples for peer influence during framework debates
- Ability to shape vendor selection criteria using ISO 42001 control logic
- Increased visibility in strategic AI initiatives across client and internal programs
- Confident articulation of governance trade-offs during high-stakes delivery reviews
The 12 modules (with all 144 chapters)
- Emergence of ISO 42001 as governance differentiator
- Influence points in AI delivery lifecycle
- Decision ownership vs compliance checking
- Client expectations on AI accountability
- Vendor claims vs certified assurance
- How the firm peers are positioning ISO 42001
- Governance influence without formal authority
- Strategic timing of framework mentions
- Internal buy-in for client-facing positions
- Benchmarking against peer firms
- Common missteps in early adoption
- From observer to agenda setter
- Clause 4 context and your client footprint
- Leadership commitment in delivery rhythm
- Roles in AI governance structure
- Risk-based thinking in sprint planning
- Competence evidence in team composition
- Awareness in onboarding flows
- Communication in escalation paths
- Resource planning for audits
- Process approach in delivery frameworks
- Improvement triggers in retrospectives
- Corrective actions in incident logs
- Audit readiness in reporting cycles
- Vendor self-attestation red flags
- Evidence types for certification claims
- Third-party audit scope gaps
- Mapping vendor responses to clauses
- Weighting controls by client risk
- Reference checks with audit trails
- Pilot agreements with exit clauses
- Data provenance in AI training sets
- Model monitoring commitments
- Transparency in bias testing
- Human oversight mechanisms
- Right to explanation in design
- Model documentation as control evidence
- Bias assessment frequency thresholds
- Data lineage for audit trails
- Version control in production models
- Human review touchpoints
- Adversarial testing protocols
- Incident escalation playbooks
- Model drift detection standards
- Explainability techniques by use case
- Third-party model risk scoring
- Retraining validation cycles
- Decommissioning criteria
- Translating controls to business impact
- Risk appetite alignment across functions
- Timing governance discussions pre-RFP
- Client communication on AI ethics
- Incident response coordination
- Regulatory expectation mapping
- Insurance implications of certification
- Board-level message distillation
- Crisis simulation participation
- Lessons from past AI incidents
- Stakeholder influence mapping
- Preparing escalation paths
- Statement of Applicability patterns
- Control implementation playbooks
- Audit evidence collection workflows
- Client-facing assurance summaries
- Internal training modules
- Vendor questionnaire templates
- Risk assessment frameworks
- Incident post-mortem formats
- Policy exception tracking
- Compliance dashboard design
- Stakeholder update rhythms
- Lessons learned repositories
- Client request response timelines
- Scoping boundaries for audits
- Evidence sufficiency thresholds
- Third-party validation options
- Certification vs self-declaration
- Insurance requirement mapping
- Regulatory alignment arguments
- Past audit findings as proof points
- Gap analysis positioning
- Remediation roadmap framing
- External auditor expectations
- Follow-up cycle management
- EU AI Act high-risk classification
- NIST AI RMF integration points
- Sector-specific guidance tracking
- Enforcement precedent watching
- Global divergence mapping
- Safe harbor arguments
- Audit trail completeness
- Human oversight adequacy
- Transparency obligation levels
- Redress mechanism design
- Monitoring frequency benchmarks
- Record retention standards
- Role-based control ownership
- Competence assessment frameworks
- Hiring criteria for AI roles
- Training plan integration
- Certification tracking
- Mentorship program design
- Knowledge transfer rituals
- Cross-functional rotation plans
- Performance review alignment
- Succession planning
- Team audit readiness drills
- External speaker curation
- Industry-specific risk profiles
- Client size adjustment factors
- Cultural sensitivity in governance
- Language localization needs
- Legal regime differences
- Audit expectation calibration
- Vendor ecosystem variation
- Team structure adaptation
- Timeline compression options
- Budget-constrained implementations
- Hybrid model deployment
- Lessons from global peers
- Scope reduction negotiation
- Evidence sufficiency debates
- Audit timing conflicts
- Resource constraint arguments
- Client customization demands
- Third-party dependency risks
- Legacy system integration
- Budget overrun mitigation
- Stakeholder expectation gaps
- Reputation risk balancing
- Short-term vs long-term trade-offs
- Escalation path clarity
- Continuous improvement triggers
- Control review frequency
- Change impact assessment
- Stakeholder feedback loops
- Benchmarking against peers
- Lessons from incident data
- Audit finding trend analysis
- Client satisfaction inputs
- Team turnover planning
- Leadership transition support
- External validation cycles
- Public positioning opportunities
How this maps to your situation
- When leading vendor selection for AI platforms
- During client audit preparation cycles
- When shaping internal AI governance policy
- Before strategic account reviews with leadership
Before vs. after
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 3 hours per module, designed to fit around delivery leadership responsibilities.
How this compares to the alternatives
Generic AI governance courses offer broad overviews. This course is tailored for delivery leaders who need to influence decisions, not just understand frameworks. It focuses on practical levers of control, peer-tested arguments, and artefacts that build lasting credibility.
Frequently asked
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.