A tailored course, built for your situation
Mastering ISO 20000 for Artificial Intelligence Engineers
Operational integrity through precise service delivery frameworks
$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.
Engineers implementing AI in regulated environments often face delayed sign-offs due to misaligned service expectations.
The situation this course is for
Even technically sound AI deployments stall when service boundaries aren’t clearly defined against compliance expectations. Without a shared framework, teams default to over-consulting senior leads, slowing delivery and diluting ownership.
Who this is for
Senior AI/ML engineers in consulting or audit-facing roles who are expected to deliver compliant, scalable systems but lack formal authority over service design decisions.
Who this is not for
Entry-level developers, non-technical auditors, or executives seeking board-level summaries. This is for hands-on engineers who own implementation and want decision rights to match.
What you walk away with
- Define and lock service level agreements without escalation
- Own change management thresholds for AI model updates
- Set incident classification criteria used across client teams
- Control the composition and mandate of the Change Advisory Board
- Approve service continuity plans without senior review
The 12 modules (with all 144 chapters)
Module 1. Introduction to ISO 20000 in AI-Driven Environments
Lays the foundation for applying ISO 20000 principles specifically within AI and machine learning workflows. Explores how service management integrates with model lifecycle governance.
12 chapters in this module
- AI system as a service definition
- Mapping model versions to service baselines
- Service catalog integration for AI outputs
- Regulatory overlap with ISO 20000 scope
- Client expectations in audit-ready delivery
- Stakeholder alignment without delays
- Incident vs anomaly classification rules
- Change control for model drift
- Service owner accountability model
- Documentation standards for AI services
- Integration with DevOps pipelines
- First-line response protocols
Module 2. Service Level Management for AI Components
Teaches how to define, negotiate, and enforce SLAs specific to AI services, ensuring they are measurable, testable, and audit-compliant.
12 chapters in this module
- Defining uptime for inference endpoints
- Latency thresholds for real-time models
- Accuracy decay as SLA breach trigger
- SLA tiering by client criticality
- Penalty clauses for missed KPIs
- Monitoring integration with SLA dashboards
- Escalation paths for SLA violations
- Model retraining as service remediation
- Client reporting on SLA adherence
- Third-party dependency disclosures
- Service credits and model updates
- SLA audit trail preservation
Module 3. Incident Management in Model Operations
Covers structuring incident response for AI systems, including classification, prioritization, and resolution workflows aligned with ISO 20000.
12 chapters in this module
- Defining incident vs drift
- Classification matrix for model failures
- Priority scoring with business impact
- Automated alerting integration
- Initial diagnosis playbooks
- Containment for biased outputs
- Escalation without delays
- Post-incident review structure
- Root cause tagging system
- Model rollback procedures
- Service continuity planning
- Audit-readiness of incident logs
Module 4. Change Management for AI Systems
Guides the design of change control processes tailored to AI deployments, ensuring compliance while maintaining velocity.
12 chapters in this module
- Defining change scope for AI models
- Standard change pre-approvals
- Emergency change workflows
- CAB composition rules
- Risk score for model updates
- Peer review integration
- Backout planning for deployments
- Change calendar coordination
- Automated compliance checks
- Documentation templates
- Post-change validation steps
- Audit trail for change logs
Module 5. Configuration Management for Model Assets
Establishes a configuration management system for AI models, versions, and dependencies that meets ISO 20000 requirements.
12 chapters in this module
- Model inventory definition
- Version control integration
- Dependency mapping for AI pipelines
- CMDB integration strategies
- Ownership tracking for models
- Baseline definition frequency
- Model lineage documentation
- Access control for CMDB
- Automated sync triggers
- Drift detection protocols
- Audit trail for configuration changes
- Recovery from CMDB corruption
Module 6. Service Continuity and Disaster Recovery
Builds robust continuity plans for AI services, ensuring resilience and compliance under ISO 20000.
12 chapters in this module
- Failure mode analysis for AI systems
- Recovery time objectives by service tier
- Model retraining SLAs
- Data pipeline fallbacks
- Failover for inference endpoints
- Manual override protocols
- Backup frequency for training data
- Geographic redundancy planning
- Simulation testing schedule
- Incident linkage to continuity plan
- Client communication during outage
- Audit evidence for recovery tests
Module 7. Supplier Management in AI Ecosystems
Manages third-party AI components and vendors within ISO 20000 compliance frameworks.
12 chapters in this module
- Vendor SLA negotiation
- Third-party model audit rights
- Compliance verification steps
- Penalty enforcement mechanisms
- Data sovereignty clauses
- Subcontractor oversight rules
- Exit strategy for vendor lock-in
- Performance monitoring integration
- Contract review checklist
- Risk rating for suppliers
- Incident responsibility matrix
- Audit trail for vendor actions
Module 8. Capacity Management for AI Workloads
Optimizes resource planning for AI systems to meet service demands without overprovisioning.
12 chapters in this module
- Workload forecasting methods
- Model inference demand patterns
- Scaling triggers for infrastructure
- Cost-performance tradeoff analysis
- Stress testing protocols
- Bottleneck identification
- Resource elasticity design
- Auto-scaling rule definition
- Capacity reporting for clients
- Trend analysis for future needs
- Peak load simulation
- Audit readiness of capacity logs
Module 9. Information Security Management Integration
Aligns AI service delivery with security controls required under ISO 20000 and related standards.
12 chapters in this module
- Data classification for AI training sets
- Access control for model outputs
- Encryption in transit and at rest
- Authentication for API endpoints
- Penetration testing for models
- Threat modeling for inference APIs
- Logging requirements for access
- Incident linkage to security events
- Vendor security compliance
- Privacy-preserving techniques
- Audit trail for security events
- Security policy enforcement
Module 10. Relationship Management with Stakeholders
Strengthens collaboration between AI engineers and business units to ensure service expectations are clear and met.
12 chapters in this module
- Stakeholder identification matrix
- Expectation alignment sessions
- Service review meeting structure
- Feedback loop integration
- Conflict resolution protocols
- Communication cadence planning
- Escalation path definition
- Client onboarding checklist
- Service improvement recommendations
- Stakeholder satisfaction tracking
- Reporting on service value
- Audit readiness of stakeholder logs
Module 11. Service Reporting and Performance Monitoring
Implements effective reporting systems that demonstrate compliance and performance of AI services.
12 chapters in this module
- KPI definition for AI services
- Dashboard design principles
- Automated report generation
- Client-facing summary templates
- Internal performance reviews
- Trend analysis techniques
- Benchmarking against peers
- Audit trail for reports
- Real-time alert integration
- Service improvement tracking
- Executive summary structure
- Regulator-facing documentation
Module 12. Continual Improvement in AI Service Delivery
Embeds a culture of ongoing enhancement aligned with ISO 20000 principles.
12 chapters in this module
- Lessons learned process
- Root cause analysis methods
- Improvement initiative tracking
- Change impact assessment
- Feedback integration mechanics
- Process optimization techniques
- Automation opportunities
- Cost-benefit analysis
- Stakeholder buy-in strategies
- Pilot testing improvements
- Rollout planning
- Audit readiness of improvement logs
How this maps to your situation
- AI model deployment in regulated sectors
- Client audit preparation for service delivery
- Cross-functional team leadership in AI projects
- Vendor and third-party oversight in machine learning systems
Before vs. after
Before
Decisions on service boundaries, incident response, and change control require multiple approvals and slow down delivery.
After
You own the call on framework decisions, SLAs, change boards, incident thresholds, without needing senior sign-off.
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 working professionals. Total time: 36 hours over 6-8 weeks.
If nothing changes
Without ownership of service framework decisions, engineers remain implementers, not leaders. Missed opportunities for influence, delayed deployments, and continued dependency on others for operational control persist.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for AI engineers who need to command service frameworks, not just understand them. No other course grants direct decision ownership in ISO 20000 contexts.
Frequently asked
Is this course relevant for AI engineers outside of consulting?
Yes. While designed with consulting engineers in mind, the decision frameworks apply to any AI professional owning service delivery in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me gain authority without a title change?
Yes. The course equips you with the documented rationale and implementation patterns to claim ownership of key service decisions.
$199 one-time. Approximately 3 hours per module, designed for working professionals. Total time: 36 hours over 6-8 weeks..
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