A tailored course, built for your situation
Mastering ISO 20000 for AI-Driven Decision Ops Leaders
Build influence across lines of business by productizing AI with service excellence
$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 technical leader in banking or financial services driving AI productization across marketing, risk, or operations teams
Who this is not for
Entry-level analysts, tool-specific administrators, or practitioners focused only on model accuracy without deployment scope
What you walk away with
- Structure AI deployments as formal services compliant with ISO 20000 requirements
- Map decisioning pipelines to service lifecycle stages for audit and continuity
- Articulate service-level commitments that other departments trust and adopt
- Lead cross-functional reviews using standardized service documentation
- Scale one-off AI pilots into bank-wide service offerings with defined ownership
The 12 modules (with all 144 chapters)
Module 1. Foundations of Service Excellence in AI Delivery
Establish the link between AI decisioning systems and ISO 20000 service principles, emphasizing repeatability and operational trust.
12 chapters in this module
- What service excellence means in AI contexts
- Difference between project and service mindset
- Core pillars of ISO 20000 for digital outputs
- Service culture in financial institutions
- Ownership models for AI service delivery
- Service catalog integration for AI features
- Lifecycle expectations for AI services
- Inputs from data science teams
- Outputs consumed by business units
- Governance touchpoints across stages
- Tracking service maturity over time
- Benchmarking against internal standards
Module 2. Service Strategy for AI-Driven Marketing
Align AI marketing initiatives with long-term service goals, ensuring business relevance and scalability.
12 chapters in this module
- Identifying scalable use cases
- Mapping customer journey stages
- Service value propositions
- Demand forecasting for AI services
- Portfolio segmentation
- Strategic alignment with marketing goals
- Resource planning for rollout
- Stakeholder impact analysis
- Risk appetite and service limits
- Approval workflows for launch
- Capacity modeling for peak loads
- Exit criteria for underperforming services
Module 3. Service Design for Decision Ops Pipelines
Translate AI workflows into formally designed services with documented SLAs, responsibilities, and controls.
12 chapters in this module
- Translating ML models into service specs
- Designing input data contracts
- Output format standardization
- SLA definition for inference latency
- Error handling protocols
- Monitoring integration points
- Change control thresholds
- Compliance crosswalks
- Documentation templates
- Review board submission format
- Design validation checklist
- Handoff between data and ops teams
Module 4. Service Transition Planning
Orchestrate the movement of AI services from development to production with minimal friction.
12 chapters in this module
- Release planning for AI models
- Test environment validation
- Data drift detection setup
- Model version tracking
- Rollback procedures
- Deployment checklist
- Stakeholder notification plan
- Training for support teams
- Knowledge transfer sessions
- Service acceptance criteria
- Post-launch review schedule
- Feedback loop integration
Module 5. Service Operation in Live Environments
Maintain AI services in production with uptime, quality, and compliance awareness.
12 chapters in this module
- Incident response for model failures
- Performance threshold monitoring
- Alert triage process
- Service desk integration
- User support protocols
- Scheduled maintenance windows
- Capacity adjustments
- Model retraining triggers
- Downtime communication plan
- Escalation paths
- Monthly service reviews
- Continuous improvement inputs
Module 6. Service Continual Improvement
Embed feedback and metrics into AI services to enhance performance and adoption over time.
12 chapters in this module
- Collecting user satisfaction data
- Performance trend analysis
- Adoption rate tracking
- Root cause of service issues
- Improvement proposal pipeline
- Prioritization framework
- Change implementation plan
- Impact measurement
- Benchmarking across lines of business
- Annual review cycle
- Retirement planning
- Lessons learned repository
Module 7. ISO 20000 Compliance for AI Workflows
Ensure AI decisioning systems meet ISO 20000 requirements through structured documentation and process alignment.
12 chapters in this module
- Clause 4: Context of organization
- Clause 5: Leadership commitment
- Clause 6: Planning actions
- Clause 7: Support resources
- Clause 8: Operational control
- Clause 9: Performance evaluation
- Clause 10: Improvement actions
- Mapping AI pipelines to clauses
- Audit preparation checklist
- Internal review cadence
- External assessor expectations
- Gap remediation process
Module 8. Service Ownership and Accountability
Define clear roles and responsibilities for AI services across teams and functions.
12 chapters in this module
- Service owner definition
- RACI matrix construction
- Decision authority boundaries
- Cross-team escalation paths
- Change advisory board role
- Budget ownership
- Performance reporting
- Stakeholder engagement plan
- Vendor coordination rules
- Escalation protocols
- Success metrics
- Accountability documentation
Module 9. Service Reporting and Visibility
Create transparent, actionable reports that demonstrate the value and reliability of AI services.
12 chapters in this module
- KPI selection for AI services
- SLA performance dashboards
- Executive summary format
- Operational review decks
- Incident post-mortem structure
- Compliance reporting
- Adoption heatmaps
- Risk exposure tracking
- Budget vs actuals
- Forecast accuracy rate
- Customer feedback highlights
- Quarterly business review pack
Module 10. Cross-Functional Integration
Expand influence by connecting AI services to enterprise-wide systems and practices.
12 chapters in this module
- Integrating with IT service management
- Linking to enterprise architecture
- Data governance alignment
- Security policy coordination
- Privacy impact assessments
- Legal and compliance touchpoints
- Finance and procurement processes
- Vendor management integration
- Enterprise risk frameworks
- Regulatory reporting systems
- M&A readiness checks
- Business continuity alignment
Module 11. Scaling AI Across Business Units
Leverage proven service patterns to expand AI adoption beyond initial use cases.
12 chapters in this module
- Replication blueprint
- Adaptation checklist
- Localization requirements
- Governance delegation model
- Central vs local ownership
- Training cascade plan
- Support model design
- Pilot expansion roadmap
- Adoption acceleration tactics
- Change management plays
- Success story documentation
- Enterprise roadmap input
Module 12. Sustaining AI Service Excellence
Ensure long-term success and resilience of AI services across leadership cycles and technological shifts.
12 chapters in this module
- Leadership transition plan
- Knowledge retention strategies
- Succession planning
- Institutional memory systems
- Technology refresh planning
- Vendor lock-in mitigation
- Open standards adoption
- Internal advocacy programs
- External recognition opportunities
- Industry benchmarking
- Future capability roadmap
- Legacy service retirement
How this maps to your situation
- When launching a new AI-driven customer offer
- Before an internal audit cycle begins
- While preparing for executive review
- After a service failure or outage
Before vs. after
Before
AI projects are siloed, inconsistently adopted, and treated as temporary experiments.
After
AI capabilities are standardized, trusted, and reused across multiple departments as formal services.
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, or 36 total hours, with self-paced access.
How this compares to the alternatives
Unlike generic AI governance courses, this program focuses on service operationalization under ISO 20000, making it actionable for practitioners leading cross-functional AI adoption in complex organizations.
Frequently asked
Is this course relevant for someone in financial services?
Yes. It’s tailored for leaders like you driving AI decisioning in regulated environments where service consistency and compliance matter.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me expand influence beyond my current team?
Yes. The course teaches how to structure AI work so other departments see it as reliable, reusable, and worth adopting.
$199 one-time. Approximately 3 hours per module, or 36 total hours, with self-paced access..
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