A tailored course, built for your situation
Mastering OECD AI Principles for Revenue Operations Leaders
Build defensible AI governance frameworks rooted in international standards
$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 revenue operations and enablement leaders at AI-forward tech companies
Who this is not for
Individuals outside of operations or enablement roles, or those not engaging with AI governance decisions
What you walk away with
- Articulate the OECD AI Principles with specific implementation examples across governance, monitoring, and review cycles
- Reference actual case studies and regulatory interpretations when justifying AI oversight decisions
- Structure internal playbooks that survive leadership changes and scale across teams
- Respond to peer challenges with source-backed reasoning patterns tied to international consensus
- Design audit-ready documentation that anticipates follow-up lines of inquiry
The 12 modules (with all 144 chapters)
Module 1. Foundations of the OECD AI Principles
Understand the five OECD AI Principles and their role in shaping national and organizational policy. Walk through real-world adoptions by governments and firms.
12 chapters in this module
- Intent behind the OECD AI Principles
- How member countries implement the principles
- Mapping to corporate governance cycles
- Public vs private sector adaptations
- Role in post-deployment oversight
- How regulators cite the framework
- Common misinterpretations to avoid
- Version control and updates
- Relationship to national laws
- Benchmarking maturity levels
- Linking to board-level risk reporting
- Documenting alignment in audits
Module 2. Principle One: Inclusive Growth and Well-Being
Apply the first principle to revenue enablement programs and AI-driven coaching tools. Use case examples from SaaS firms.
12 chapters in this module
- Defining inclusive growth in enablement
- Measuring well-being in AI interventions
- Avoiding metrics that incentivize exclusion
- Tracking downstream impacts
- Stakeholder mapping for fairness
- Feedback loops with frontline teams
- Adjusting models based on input
- Public commitments to equity
- HR and sales incentive alignment
- Documenting equity considerations
- Review cadence design
- Reporting outcomes transparently
Module 3. Principle Two: Human-Centered Values
Implement AI in revenue operations with human oversight baked in. Learn from compliance-adjacent domains.
12 chapters in this module
- Defining human agency in AI systems
- Audit trails for override decisions
- Designing opt-out workflows
- Consent in internal tooling
- Bias detection in coaching outputs
- Right to explanation workflows
- Training on ethical escalation
- Documenting human review points
- Thresholds for manual intervention
- UI patterns for transparency
- Logging reviewer rationale
- Evaluating model drift impact
Module 4. Principle Three: Transparency and Explainability
Build clear documentation for AI-driven revenue forecasts and performance models. Use templates from regulated industries.
12 chapters in this module
- Minimum explainability standards
- Stakeholder-specific disclosures
- Versioned documentation practices
- Automated summary generation
- Audit trail completeness
- Plain language for non-experts
- Visualizing data flows
- Change logs for model updates
- Access controls for documentation
- Retention policies
- Cross-team indexing system
- Searchable playbook structure
Module 5. Principle Four: Robustness and Security
Apply security thinking to AI pipelines in revenue systems. Draw from SOC 2 and NIST CSF patterns.
12 chapters in this module
- Threat modeling for AI features
- Input validation standards
- Adversarial testing cycles
- Access review frequency
- Encryption in transit and at rest
- Logging anomalous behavior
- Fail-safe default modes
- Penetration testing integration
- Vendor risk in AI components
- Incident response planning
- Recovery from degraded models
- Model rollback procedures
Module 6. Principle Five: Accountability
Design clear ownership structures for AI systems in revenue operations. Align with compliance and legal expectations.
12 chapters in this module
- Defining accountable roles
- Sign-off workflows
- Escalation paths
- Cross-functional ownership
- Change approval chains
- Documenting rationale
- Audit readiness checks
- Periodic review schedules
- Handover documentation
- Succession planning for owners
- Performance metrics for oversight
- Reporting to senior leadership
Module 7. Mapping AI Principles to Internal Governance
Connect OECD guidance to existing revenue operations frameworks. Use mapping exercises from audit-approved firms.
12 chapters in this module
- Crosswalking to internal policies
- Identifying coverage gaps
- Prioritizing remediation
- Change management workflows
- Stakeholder alignment sessions
- Documentation versioning
- Governance committee inputs
- Risk register updates
- Control mapping templates
- Remediation tracking
- Audit trail integration
- Reporting to compliance teams
Module 8. Building Defensible AI Narratives
Create compelling, source-backed stories for leadership and auditors. Use patterns from successful regulatory engagements.
12 chapters in this module
- Opening with OECD alignment
- Structuring evidence packets
- Sequencing technical and policy points
- Preempting follow-up questions
- Citing implementation examples
- Using neutral third-party sources
- Avoiding overstatement
- Grounding claims in evidence
- Linking to past audit outcomes
- Tying to executive priorities
- Balancing transparency and risk
- Closing with action clarity
Module 9. Workforce Impact Assessments
Evaluate how AI tools affect revenue team roles and performance. Use frameworks from labor-forward jurisdictions.
12 chapters in this module
- Defining workforce impact scope
- Identifying at-risk roles
- Consultation requirements
- Transition planning
- Reskilling pathways
- Performance fairness reviews
- Feedback from affected teams
- Documentation standards
- Regulatory reporting
- Timeline for implementation
- Leadership communication
- Post-deployment review
Module 10. Vendor Selection and Oversight
Apply OECD principles to third-party AI tools used in enablement and operations. Use checklists from procurement leaders.
12 chapters in this module
- RFP inclusion criteria
- Due diligence questions
- Contractual obligations
- Audit rights negotiation
- Performance benchmarks
- Data handling commitments
- Transparency requirements
- Explainability expectations
- Security certification checks
- Compliance reporting
- Exit strategy planning
- Renewal review triggers
Module 11. Internal Training and Enablement
Scale understanding of AI governance across revenue teams. Use materials from firms with mature programs.
12 chapters in this module
- Defining core learning objectives
- Role-based training paths
- Microlearning formats
- Assessment design
- Leadership onboarding
- New hire integration
- Refresher cycles
- Feedback collection
- Metrics for effectiveness
- Updating materials
- Cross-team consistency
- Documentation access
Module 12. Continuous Monitoring and Improvement
Build systems to track AI governance performance over time. Learn from financial and healthcare compliance cycles.
12 chapters in this module
- Defining KPIs for governance
- Automated alerting
- Manual review cadence
- Incident logging
- Root cause analysis
- Trend identification
- Stakeholder reporting
- Process refinement
- Benchmarking against peers
- Audit preparation
- Leadership updates
- Public disclosure strategy
How this maps to your situation
- When launching new AI-driven revenue tools
- During internal audit cycles
- Before executive reviews
- After regulatory changes
Before vs. after
Before
Reactive responses to governance queries with limited source backing
After
Proactive, source-backed articulation of AI decisions using OECD standards
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 for integration into regular work cycles.
If nothing changes
Without structured reasoning, even sound AI governance choices may be overturned due to perceived lack of rigor.
How this compares to the alternatives
Unlike generic AI ethics courses, this program focuses on the OECD AI Principles with operational precision, providing templates and examples used in actual audits and leadership reviews.
Frequently asked
Who is this course for?
Revenue operations and enablement leaders working with AI-driven tools who need to justify governance choices with authoritative sources.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is the OECD AI Principles legally binding?
No, but they inform national laws and regulations and are widely cited in enforcement actions and audits.
$199 one-time. Approximately 3 hours per module, designed for integration into regular work cycles..
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