A tailored course, built for your situation
Influence Across More Business Units with NIST AI RMF
Master the framework that aligns AI governance across teams, regions, and product lines
$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.
Feeling confined to your immediate team despite having insight that matters across the organization
The situation this course is for
Technical experts often have the clearest view of AI risk, but lack the frameworks to translate that into enterprise-wide influence. Their recommendations stay siloed, decisions are duplicated across regions, and governance lags behind deployment velocity, all because influence doesn’t scale with title alone.
Who this is for
Senior technical practitioner in AI, data, or platform engineering who operates beyond their immediate team but lacks formal authority to shape cross-organizational practices
Who this is not for
Individuals looking for entry-level AI training or those focused solely on developer tooling without governance or risk components
What you walk away with
- Lead alignment on AI risk thresholds across non-direct teams
- Translate technical AI constraints into enterprise risk language for global stakeholders
- Orchestrate consistent governance adoption across multiple lines of business
- Become the go-to validator on AI risk decisions enterprise-wide
- Shape AI policy rollouts that stick across regions and reporting structures
The 12 modules (with all 144 chapters)
Module 1. NIST AI RMF Core Structure
Break down the framework into actionable layers that map to real organizational friction points in AI deployment.
12 chapters in this module
- Framework purpose and scope
- Four core functions overview
- Mapping roles to functions
- Identifying governance gaps
- Cross-unit communication lanes
- Risk threshold definitions
- Baseline vs tailored profiles
- Internal stakeholder mapping
- Decision escalation paths
- Documentation standards
- Integration with existing controls
- Versioning and review cycles
Module 2. Govern Across Regions
Adapt governance practices to regional risk appetites while maintaining central coherence.
12 chapters in this module
- Regional risk variance patterns
- Legal boundary mapping
- Local champion identification
- Central oversight models
- Incident response coordination
- Language and culture considerations
- Time zone aligned reviews
- Regional policy exceptions
- Audit trail consistency
- Escalation triage protocols
- Cross-region playbook sharing
- Feedback loop design
Module 3. Align Product Teams
Drive consistency in AI risk decisions across independent engineering units without central mandates.
12 chapters in this module
- Product team autonomy models
- Risk appetite calibration
- Pre-mortem facilitation
- Design phase checkpoints
- Tech stack divergence tracking
- Shared definition of harm
- Model boundary documentation
- Release gate criteria
- Cross-team review cadence
- Dispute resolution frameworks
- Metrics for alignment
- Feedback integration mechanisms
Module 4. Engage Executive Stakeholders
Frame AI risk in business terms that resonate with senior leadership outside technical domains.
12 chapters in this module
- Translating model risk to revenue impact
- Executive briefing structure
- Risk dashboard design
- Scenario planning workshops
- Board-level narrative crafting
- Budget justification templates
- Vendor risk summary formats
- M&A integration checklists
- Reputation risk modeling
- Incident communication plans
- Regulatory alignment signals
- Strategic option framing
Module 5. Scale Internal Adoption
Turn pilot implementations into organization-wide standards through peer-led momentum.
12 chapters in this module
- Early adopter identification
- Champion network design
- Training cascade models
- Success story documentation
- Incentive alignment tactics
- Metrics that drive adoption
- Feedback collection systems
- Governance ambassador roles
- Tooling integration points
- Knowledge base structure
- Version update protocols
- Retention through iteration
Module 6. Coordinate Across Functions
Integrate legal, security, compliance, and product teams around a shared AI risk posture.
12 chapters in this module
- Function-specific risk concerns
- Common language development
- Joint decision forums
- Cross-functional RACI design
- Dispute mediation protocols
- Shared artefact repositories
- Meeting rhythm alignment
- Escalation path clarity
- Role clarity exercises
- Boundary ownership rules
- Change notification systems
- Joint audit preparation
Module 7. Tailor for Industry Context
Adapt NIST AI RMF to financial services, healthcare, or industrial sectors with distinct risk profiles.
12 chapters in this module
- Sector-specific harm categories
- Regulatory mapping strategies
- Third-party dependency risks
- Supply chain exposure points
- Incident severity benchmarks
- Historical failure analysis
- Customer trust thresholds
- Brand risk correlations
- Insurance considerations
- Liability exposure modeling
- Sector-specific controls
- Benchmarking against peers
Module 8. Integrate with Existing Frameworks
Fuse NIST AI RMF with ISO 27001, SOC 2, and other compliance programs already in place.
12 chapters in this module
- Control overlap analysis
- Evidence reuse strategies
- Audit alignment tactics
- Policy harmonization methods
- Cross-framework mapping tables
- Unified reporting formats
- Single source of truth design
- Compliance efficiency gains
- Framework dependency tracking
- Change propagation planning
- Multi-standard certification paths
- Vendor assessment alignment
Module 9. Manage Third-Party AI Risk
Extend governance beyond internal teams to vendors, partners, and open-source contributions.
12 chapters in this module
- Vendor risk classification
- Contractual control enforcement
- Third-party audit rights
- Open source contribution policies
- Model provenance tracking
- Data leakage prevention
- API security standards
- Penetration testing expectations
- Incident response coordination
- Exit strategy planning
- Subprocessor oversight
- Transparency requirement design
Module 10. Build Repeatable Playbooks
Turn one-off decisions into reusable governance assets that compound across engagements.
12 chapters in this module
- Decision pattern identification
- Artefact templating
- Version control practices
- Contextual adaptation rules
- Approval workflow design
- Stakeholder feedback loops
- Lessons learned integration
- Common exception tracking
- Playbook maintenance cycles
- Searchable knowledge design
- Onboarding acceleration
- Cross-context applicability
Module 11. Lead Through Influence
Exert leadership without formal authority by becoming the trusted interpreter of AI risk.
12 chapters in this module
- Credibility building tactics
- Active listening frameworks
- Consensus facilitation methods
- Political landscape mapping
- Alliance formation strategies
- Neutral framing techniques
- Data-backed persuasion
- Stakeholder motivation analysis
- Conflict de-escalation
- Change sponsorship models
- Influence metric tracking
- Legacy resistance navigation
Module 12. Sustain Enterprise Change
Ensure AI governance improvements endure leadership changes and shifting priorities.
12 chapters in this module
- Institutional memory design
- Leadership transition planning
- Successor development
- Cultural embedding tactics
- Metric continuity
- Policy update governance
- External validation strategies
- Benchmarking participation
- Industry contribution planning
- Thought leadership development
- Ecosystem engagement
- Long-term roadmap alignment
How this maps to your situation
- When rolling out AI governance across multiple product lines
- Preparing for cross-regional AI audits
- Onboarding new business units to centralized AI policies
- Leading AI risk alignment without direct authority
Before vs. after
Before
AI governance decisions are fragmented across teams, regions, and functions, leading to inconsistent risk posture and redundant effort.
After
A unified, scalable approach to AI governance drives alignment across the enterprise, with you as the central node of influence and continuity.
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 3-4 hours per week over 12 weeks, with self-paced access to all materials.
If nothing changes
Without structured influence, AI risk decisions remain reactive and siloed, exposing the organization to regulatory, operational, and reputational harm, while your potential impact stays locked within immediate teams.
How this compares to the alternatives
Unlike generic AI ethics courses or tool-specific trainings, this program focuses on the NIST AI RMF as a lever for enterprise-wide influence, equipping you to lead beyond your org chart with proven frameworks and real-world playbooks.
Frequently asked
Is this course technical or strategic?
It's both: grounded in technical rigor but framed for strategic influence across non-technical stakeholders.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover AI Act or other regulations?
The core is NIST AI RMF, with integration guidance for AI Act, ISO 42001, and sector-specific requirements.
$199 one-time. Approximately 3-4 hours per week over 12 weeks, with self-paced access to all materials..
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