What is the Influence across more business units course about?
You're delivering AI solutions that meet technical standards, but they're not being adopted widely across service units. Without a common framework like ISO 20000, scaling impact means constant re-explanation and fragmented buy-in.
What situation is the Influence across more business units for?
You're delivering AI solutions that meet technical standards, but they're not being adopted widely across service units. Without a common framework like ISO 20000, scaling impact means constant re-explanation and fragmented buy-in.
Who is the Influence across more business units course for?
Senior AI Engagement Partner working at a global systems integrator, focused on embedding AI into client operations with measurable governance.
What do you take away from the Influence across more business units course?
Lead cross-functional AI integrations using ISO 20000 as a shared delivery language Gain visibility and trust from operations leaders outside core AI or tech groups Structure repeatable engagement models that work across financial services, healthcare, and supply chain units Position AI initiatives as service improvements, not just tech projects Shape service delivery roadmaps in early stages, not just project execution phases.
How does this map to your situation?
When expanding AI from tech teams to business units Before launching enterprise-wide AI adoption During integration of AI into existing service portfolios When responding to requests for broader AI governance.
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.
What does the Influence across more business units cover on delivery and format?
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, with flexible pacing over 6-8 weeks.
How does this compare to the alternatives?
Generic AI governance courses focus on principles, while this course delivers actionable service integration frameworks used by leading enterprises adopting ISO 20000.
Closely related courses: Influence across more business units, Influence Across More Operational Units.
More answers: what you get with every course, refund policy, all help answers.
A tailored course, built for your situation
Influence across more business units with ISO 20000
A tailored course to expand your impact as an AI Engagement Partner
The situation this course is for
You're delivering AI solutions that meet technical standards, but they're not being adopted widely across service units. Without a common framework like ISO 20000, scaling impact means constant re-explanation and fragmented buy-in.
Who this is for
Senior AI Engagement Partner working at a global systems integrator, focused on embedding AI into client operations with measurable governance
Who this is not for
Entry-level practitioners, auditors focused solely on compliance checks, or teams only interested in ISO certification as a checkbox
What you walk away with
- Lead cross-functional AI integrations using ISO 20000 as a shared delivery language
- Gain visibility and trust from operations leaders outside core AI or tech groups
- Structure repeatable engagement models that work across financial services, healthcare, and supply chain units
- Position AI initiatives as service improvements, not just tech projects
- Shape service delivery roadmaps in early stages, not just project execution phases
The 12 modules (with all 144 chapters)
- Service strategy for AI use cases
- Aligning AI pilots with service design
- Identifying service owners in AI delivery
- Documenting service level requirements
- Integrating AI risks into service assurance
- Defining service scope for AI components
- Timing engagement cycles with review gates
- Linking AI KPIs to service metrics
- Stakeholder mapping by function
- Identifying compliance touchpoints
- Using service portfolio logic
- Planning for service retirement of AI models
- Translating model drift to service failure
- Service continuity and AI fallback plans
- Change management for AI updates
- Incident response with model logs
- Problem management for AI outages
- Service request workflows for AI access
- Capacity planning with inference loads
- Availability targets for AI APIs
- Security management in model serving
- Supplier management for third-party AI
- Configuration items for AI pipelines
- Asset lifecycle tracking for models
- Standardizing discovery workshops
- Rapid service assessment tools
- Client-specific adaptation guides
- Stakeholder communication templates
- Cross-unit rollout checklists
- Service transition playbooks
- Change advisory board prep kits
- Post-implementation service reviews
- Feedback loops from service desks
- Service improvement backlogs
- Benchmarking against ISO 20000
- Gap analysis reporting formats
- Localizing service definitions
- Managing regional compliance variations
- Time zone considerations for support
- Language and documentation standards
- Regulatory alignment strategies
- Vendor coordination across borders
- Global service desk integration
- Distributed change approval flows
- Centralized monitoring frameworks
- Regional service ownership models
- Escalation path harmonization
- Performance benchmarking across regions
- Model validation in service testing
- Bias assessment integration
- Explainability in service documentation
- Data lineage for service audits
- Version control in service releases
- Model monitoring as service health
- Retraining triggers and approvals
- Model access governance
- Ethics review handoffs
- Stakeholder notification protocols
- Model decommissioning steps
- Archival of AI service records
- Service cost attribution for AI
- Uptime impact of AI decisions
- Service quality improvements
- User satisfaction metrics
- Operational efficiency gains
- Risk reduction from automation
- Compliance assurance metrics
- Change success rate trends
- Service availability benchmarks
- Customer journey enhancements
- Process simplification outcomes
- Cross-service synergy examples
- Identifying early adopter units
- Success story documentation
- Internal evangelism strategies
- Training material development
- Service catalog inclusion tactics
- Marketing AI as service features
- Feedback integration mechanisms
- Adoption milestone tracking
- Recognition for service champions
- Roadshow planning for new units
- Scaling support capacity
- Budgeting for service expansion
- Assessing integration complexity
- Identifying interface owners
- Defining integration test cases
- Data exchange protocols
- Error handling design
- Performance expectations
- Monitoring integration health
- Support handoff procedures
- Change coordination frameworks
- Documentation standards
- Ownership transition plans
- Post-integration review criteria
- Modular service design
- Parameterization for clients
- Client onboarding workflows
- Service customization limits
- Configuration baselines
- Deployment automation scripts
- Service validation checklists
- Handover to managed services
- Renewal readiness preparation
- Usage-based billing models
- Performance tuning options
- Support tier alignment
- Shared terminology guides
- Process mapping workshops
- Joint ownership definitions
- Cross-functional agreement templates
- Escalation path clarity
- Decision rights documentation
- Review meeting structures
- Status reporting formats
- Risk register collaboration
- Action item tracking
- Process improvement forums
- Audit preparation coordination
- Assessing new business fit
- Adapting service models
- Building domain-specific knowledge
- Identifying key stakeholders
- Customizing communication plans
- Aligning with business rhythms
- Integrating with legacy systems
- Managing data sensitivity levels
- Demonstrating quick wins
- Building credibility incrementally
- Scaling from pilot to full rollout
- Establishing long-term roadmaps
- Tracking service evolution
- Updating engagement models
- Knowledge transfer strategies
- Successor development
- Stakeholder relationship nurturing
- Lessons learned integration
- Benchmarking against peers
- Innovation pipeline management
- Service retirement planning
- Reputation management tactics
- Thought leadership development
- Personal influence metrics tracking
How this maps to your situation
- When expanding AI from tech teams to business units
- Before launching enterprise-wide AI adoption
- During integration of AI into existing service portfolios
- When responding to requests for broader AI governance
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, with flexible pacing over 6-8 weeks.
How this compares to the alternatives
Generic AI governance courses focus on principles, while this course delivers actionable service integration frameworks used by leading enterprises adopting ISO 20000.
Frequently asked
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.