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OPS4303 Mastering ISO 20000 for Data & AI Managers in Global Systems Integrators

$199.00
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A tailored course, built for your situation

Mastering ISO 20000 for Data & AI Managers in Global Systems Integrators

Build repeatable service management frameworks that scale across distributed AI delivery teams

$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.
AI projects stalling at handoff due to unclear service ownership and support boundaries

Who this is for

Senior technical manager in a global systems integrator, accountable for clean handoffs between AI development and ongoing service operations, ensuring compliance-ready delivery at scale.

Who this is not for

Individual contributors not involved in cross-team delivery handoffs, or practitioners focused solely on model accuracy without operationalization responsibilities.

What you walk away with

  • Define service boundaries in AI projects with ISO 20000-aligned documentation that passes internal review
  • Structure handover milestones that align data pipelines with ongoing support expectations
  • Produce client-facing service level agreements backed by traceable control mappings
  • Reduce rework by 30, 50% in post-deployment support onboarding phases
  • Position yourself as the internal subject matter reference for service management in AI transformation deals

The 12 modules (with all 144 chapters)

Module 1. Why ISO 20000 Matters in AI Project Lifecycle
Understand how service management standards apply to AI delivery from initiation to handover. Learn where gaps typically emerge between data science teams and operational support groups.
12 chapters in this module
  1. Mapping AI project phases to service management touchpoints
  2. Identifying common handover failures in machine learning deployments
  3. Client expectations for service assurance in AI-as-a-service
  4. How ISO 20000 complements data governance and model monitoring
  5. Service catalog design for explainable AI offerings
  6. Integrating incident management with MLOps pipelines
  7. Defining clear roles in AI service delivery teams
  8. Tracking service impact across model retraining cycles
  9. Benchmarking against peer AI service delivery frameworks
  10. Aligning service level agreements with model performance metrics
  11. Documenting service scope for internal audit readiness
  12. Connecting continuous delivery to support runbook ownership
Module 2. Service Strategy for AI Offerings
Develop a service strategy that aligns AI capabilities with business outcomes and client needs. Focus on value creation and long-term sustainability.
12 chapters in this module
  1. Defining value propositions for managed AI services
  2. Identifying stakeholder requirements in service design
  3. Balancing innovation speed with operational stability
  4. Service portfolio planning for AI transformation
  5. Financial modeling for AI service offerings
  6. Risk assessment in AI service delivery
  7. Client segmentation for tailored service levels
  8. Service lifecycle costing for AI solutions
  9. Demand forecasting for AI model support
  10. Pricing models for AI-as-a-service
  11. Service differentiation in competitive bids
  12. Strategic alignment with client digital roadmaps
Module 3. Service Design Principles for AI Systems
Design robust AI services that meet availability, scalability, and security requirements. Create blueprints that guide implementation.
12 chapters in this module
  1. Designing service level agreements for AI models
  2. Translating client SLAs into technical SLOs
  3. Capacity planning for AI inference workloads
  4. Availability requirements for real-time AI services
  5. Security considerations in AI service architecture
  6. Data sovereignty in distributed AI deployments
  7. Disaster recovery for AI model serving layers
  8. Documentation standards for AI service handover
  9. Version control for AI service configurations
  10. Change management processes for AI updates
  11. Testing strategies for AI service readiness
  12. Handover checklists between development and support
Module 4. Service Transition in AI Deployments
Manage the movement of AI services from development to operation. Ensure knowledge transfer and operational readiness.
12 chapters in this module
  1. Planning transitions for AI model deployments
  2. Knowledge transfer between data science and support teams
  3. Staging environments for AI service validation
  4. Deployment automation for AI pipelines
  5. Change evaluation in AI service updates
  6. Release management for AI model versions
  7. Post-deployment review processes
  8. Service acceptance testing for AI components
  9. Training support teams on new AI capabilities
  10. Documenting runbooks for AI operations
  11. Measuring transition success for AI services
  12. Feedback loops for continuous improvement
Module 5. Service Operation for AI Models
Deliver and manage AI services during live operations. Maintain stability and respond effectively to incidents.
12 chapters in this module
  1. Event management in AI model monitoring
  2. Incident response for AI model failures
  3. Problem management for recurring AI issues
  4. Request fulfillment for AI service access
  5. Access management for AI model endpoints
  6. Daily health checks for AI inference services
  7. Managing model drift alerts operationally
  8. Escalation paths for AI performance degradation
  9. Shift handovers in 24/7 AI support
  10. Vendor coordination for third-party AI dependencies
  11. Performance dashboards for AI operations
  12. Service reporting for AI uptime and accuracy
Module 6. Continual Service Improvement for AI
Apply CSI principles to enhance AI services over time. Use feedback and metrics to drive optimization.
12 chapters in this module
  1. Establishing CSI objectives for AI services
  2. Collecting feedback from AI service users
  3. Analyzing performance trends in AI models
  4. Identifying improvement opportunities in AI delivery
  5. Prioritizing improvements based on business impact
  6. Implementing changes in AI service offerings
  7. Measuring the impact of service improvements
  8. Benchmarking AI service performance
  9. Using retrospectives to improve AI delivery
  10. Knowledge management for AI service evolution
  11. Developing a culture of continuous improvement
  12. Aligning CSI with client business goals
Module 7. Business Relationship Management for AI Services
Build strong relationships with clients and stakeholders. Understand their needs and ensure service alignment.
12 chapters in this module
  1. Understanding client business models
  2. Managing client expectations for AI services
  3. Conducting service reviews with stakeholders
  4. Negotiating service level agreements
  5. Handling client complaints about AI performance
  6. Managing changing requirements during AI delivery
  7. Building trust with AI service consumers
  8. Communicating service improvements to clients
  9. Managing multi-client AI service portfolios
  10. Aligning AI services with client strategic goals
  11. Handling service termination discussions
  12. Transitioning clients to new AI capabilities
Module 8. Supplier Management for AI Ecosystems
Manage third-party vendors and tools used in AI services. Ensure quality and compliance.
12 chapters in this module
  1. Identifying critical suppliers in AI delivery
  2. Evaluating vendor proposals for AI components
  3. Negotiating contracts with AI technology providers
  4. Managing vendor performance for AI services
  5. Handling vendor disputes in AI projects
  6. Ensuring vendor compliance with service standards
  7. Managing onboarding of new AI vendors
  8. Exit strategies for underperforming AI suppliers
  9. Coordinating between multiple AI vendors
  10. Vendor risk assessment in AI ecosystems
  11. Auditing third-party AI model providers
  12. Maintaining vendor documentation for audits
Module 9. Capacity and Availability Management for AI
Ensure AI services meet required levels of performance and uptime. Plan for future capacity needs.
12 chapters in this module
  1. Defining availability requirements for AI models
  2. Measuring AI service uptime and reliability
  3. Capacity planning for AI inference loads
  4. Performance modeling for AI workloads
  5. Scaling strategies for AI services
  6. Resource optimization in AI deployments
  7. Cost-benefit analysis for AI infrastructure
  8. Monitoring AI service degradation
  9. Proactive capacity adjustments
  10. Disaster recovery for AI services
  11. Testing failover mechanisms
  12. Reporting on AI service availability
Module 10. Information Security Management for AI
Protect AI systems and data from security threats. Comply with relevant regulations and standards.
12 chapters in this module
  1. Identifying security risks in AI systems
  2. Implementing access controls for AI models
  3. Data protection in AI training pipelines
  4. Model security against adversarial attacks
  5. Secure deployment of AI services
  6. Incident response for AI security breaches
  7. Compliance with data privacy regulations
  8. Auditing AI system security controls
  9. Security awareness for AI teams
  10. Third-party risk in AI supply chains
  11. Encryption strategies for AI data
  12. Security testing for AI deployments
Module 11. Change, Configuration, and Release Management
Control changes to AI services and manage configurations effectively. Ensure smooth releases.
12 chapters in this module
  1. Change management processes for AI updates
  2. Evaluating risk of AI model changes
  3. Approving changes in AI service environment
  4. Configuration management for AI systems
  5. Version control for AI models and pipelines
  6. Release planning for AI capabilities
  7. Deployment automation for AI services
  8. Post-release validation for AI models
  9. Managing rollback procedures
  10. Change advisory board for AI services
  11. Tracking change success rates
  12. Continuous improvement in change processes
Module 12. Service Measurement and Reporting
Measure and report on AI service performance. Use metrics to demonstrate value and identify improvements.
12 chapters in this module
  1. Defining KPIs for AI services
  2. Tracking service level compliance
  3. Reporting on AI model performance
  4. Client satisfaction measurement
  5. Service availability metrics
  6. Incident resolution times
  7. Cost per AI service delivery
  8. Quality metrics for AI outputs
  9. Benchmarking against industry standards
  10. Executive dashboards for AI services
  11. Audit readiness through service reporting
  12. Continuous feedback from service metrics

How this maps to your situation

  • Client onboarding with AI service assurance
  • Post-deployment support handover
  • Internal audit preparation
  • RFP responses requiring service framework alignment

Before vs. after

Before
AI delivery teams operate in silos, with unclear ownership between development and ongoing support, leading to rework and audit findings.
After
Integrated service delivery framework ensures clean handovers, audit-ready documentation, and consistent client expectations across AI engagements.

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 90 minutes per module, designed to be completed over 4, 6 weeks with practical implementation between modules.

If nothing changes
Projects continue to face delays at transition points, support teams struggle with undocumented AI assets, and missed opportunities to position as service management subject matter expert in AI transformations.

How this compares to the alternatives

Generic ITIL training lacks AI-specific context; internal enablement programs often miss standardized frameworks. This course provides actionable ISO 20000 application within AI delivery workflows used at top systems integrators.

Frequently asked

Is this course relevant if I don’t lead full IT service management?
Yes. It focuses on applying ISO 20000 principles specifically to AI project handoffs and operational alignment, even if you're not formally responsible for full ITSM.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me in client proposals?
Yes. You’ll gain frameworks and templates that demonstrate mature service delivery design in AI transformation bids.
$199 one-time. Approximately 90 minutes per module, designed to be completed over 4, 6 weeks with practical implementation between modules..

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
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