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First internal team to ship a working NIST AI RMF implementation

$199.00
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A tailored course, built for your situation

First internal team to ship a working NIST AI RMF implementation

Build and operationalize AI governance in a way that gets real work promoted and recognized by senior leaders

$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.
Most AI governance efforts stall at policy drafting and never reach implementation

The situation this course is for

Teams spend months designing AI ethics principles but fail to connect them to actual workflows, audits, or product decisions. Without an implementation anchor, even strong ideas fade.

Who this is for

Senior IC in tech or data-driven marketing, working at pace with AI adoption and needing to demonstrate structured governance that enables, not blocks, innovation

Who this is not for

Entry-level practitioners looking for AI ethics theory, or those not involved in rollout decisions

What you walk away with

  • Own the first working NIST AI RMF implementation in your organization
  • Produce a documented risk tiering matrix aligned to business impact
  • Build stakeholder-reviewed playbooks for AI incident escalation and response
  • Deliver a governance package that survives leadership changes
  • Gain repeatable templates for model documentation and accountability mapping

The 12 modules (with all 144 chapters)

Module 1. Define NIST AI RMF scope within marketing AI workflows
Map the NIST AI RMF to current AI use cases in digital marketing, focusing on personalization engines and automated campaign systems.
12 chapters in this module
  1. Identify high-impact AI systems
  2. Map to NIST governance objectives
  3. Align with data lineage tools
  4. Define initial risk boundaries
  5. Engage legal for compliance overlap
  6. Document data provenance paths
  7. Set decision authority levels
  8. Prioritize transparency requirements
  9. Establish feedback collection points
  10. Build incident classification tiers
  11. Integrate with change management
  12. Finalize governance scope
Module 2. Establish cross-functional governance team
Form a lightweight council with representatives from marketing, data, legal, and AI engineering to co-own the NIST AI RMF rollout.
12 chapters in this module
  1. Identify core team members
  2. Define roles and responsibilities
  3. Set meeting cadence
  4. Agree on decision pathways
  5. Document quorum rules
  6. Create shared artifact repository
  7. Launch onboarding kit
  8. Build escalation paths
  9. Align on review cycles
  10. Standardize communication templates
  11. Integrate with existing workflows
  12. Track engagement metrics
Module 3. Map model inventory to NIST functions
Catalog active AI models in marketing stack and align each to Map, Govern, Measure, and Manage functions in NIST AI RMF.
12 chapters in this module
  1. List live AI models
  2. Assign model owners
  3. Classify use cases
  4. Link to risk impact
  5. Tag deployment stage
  6. Map to NIST subfunctions
  7. Assign control maturity
  8. Score documentation completeness
  9. Identify bias testing status
  10. Flag high-visibility models
  11. Review audit readiness
  12. Update inventory dashboard
Module 4. Develop risk tiering methodology
Build a consistent, defensible method to categorize AI systems by impact, visibility, and decision autonomy.
12 chapters in this module
  1. Define impact dimensions
  2. Set financial thresholds
  3. Classify customer harm levels
  4. Assess reputational exposure
  5. Determine explainability needs
  6. Score model autonomy
  7. Build scoring rubric
  8. Validate with legal team
  9. Pilot on three models
  10. Document rationale sources
  11. Refine thresholds
  12. Finalize tiering policy
Module 5. Produce AI accountability matrices
Create clear ownership maps showing who is responsible for development, monitoring, incident response, and updates.
12 chapters in this module
  1. List decision points
  2. Assign RACI roles
  3. Link to org structure
  4. Validate with engineering
  5. Clarify escalation paths
  6. Define handoff protocols
  7. Map to incident plan
  8. Integrate with HR policies
  9. Review with compliance
  10. Finalize sign-off process
  11. Publish internal directory
  12. Maintain update cycle
Module 6. Build model documentation templates
Design standardized forms for model cards, data lineage, performance thresholds, and retraining triggers aligned to NIST AI RMF.
12 chapters in this module
  1. List required fields
  2. Align with data team
  3. Source from engineering
  4. Define update triggers
  5. Link to version control
  6. Add bias testing results
  7. Include drift thresholds
  8. Embed explainability scores
  9. Attach audit trails
  10. Standardize naming
  11. Publish template
  12. Train stakeholders
Module 7. Implement AI incident response workflow
Operationalize detection, classification, and resolution protocols for AI model failures or unintended behavior.
12 chapters in this module
  1. Define incident types
  2. Set detection mechanisms
  3. Classify severity levels
  4. Assign response teams
  5. Create intake forms
  6. Define escalation paths
  7. Set SLAs for resolution
  8. Build post-mortem templates
  9. Integrate with ticketing
  10. Test mock incidents
  11. Document lessons learned
  12. Update playbook quarterly
Module 8. Conduct internal NIST AI RMF readiness audit
Run a full-scope review to assess current state maturity across all NIST AI RMF functions and subfunctions.
12 chapters in this module
  1. Schedule audit window
  2. Assign auditors
  3. Distribute checklists
  4. Collect evidence
  5. Score maturity levels
  6. Identify gaps
  7. Prioritize fixes
  8. Engage owners
  9. Track remediation
  10. Report findings
  11. Archive documentation
  12. Plan follow-up
Module 9. Deliver executive briefing package
Prepare concise, evidence-based materials for senior leaders showing progress, risk posture, and next steps.
12 chapters in this module
  1. Define executive needs
  2. Summarize key metrics
  3. Highlight risk trends
  4. Show incident response
  5. Present maturity score
  6. Link to business goals
  7. Anticipate questions
  8. Build visual dashboard
  9. Test with peers
  10. Rehearse delivery
  11. Distribute pre-read
  12. Gather feedback
Module 10. Launch cross-team awareness campaign
Drive understanding and adoption of NIST AI RMF across marketing, data, and AI teams through workshops and resources.
12 chapters in this module
  1. Identify audience segments
  2. Design training format
  3. Build presentation decks
  4. Produce short videos
  5. Create FAQ document
  6. Set up office hours
  7. Run pilot session
  8. Collect feedback
  9. Iterate content
  10. Launch org-wide
  11. Track attendance
  12. Measure knowledge gain
Module 11. Integrate with vendor review process
Embed NIST AI RMF criteria into third-party AI tool evaluations and procurement decisions.
12 chapters in this module
  1. Map to procurement flow
  2. Define evaluation criteria
  3. Assign reviewers
  4. Build scoring sheet
  5. Require documentation
  6. Assess transparency
  7. Evaluate bias testing
  8. Check for appeal paths
  9. Review update policies
  10. Score explainability
  11. Finalize approval workflow
  12. Document exceptions
Module 12. Establish governance sustainment rhythm
Create recurring cycles for review, update, and reporting to keep NIST AI RMF implementation alive and relevant.
12 chapters in this module
  1. Set review cadence
  2. Assign owners
  3. Build checklist
  4. Schedule audits
  5. Update documentation
  6. Report to leadership
  7. Solicit feedback
  8. Track incident data
  9. Measure compliance
  10. Update training
  11. Share success stories
  12. Refine process

How this maps to your situation

  • When launching first AI governance initiative
  • After executive mandate to adopt NIST AI RMF
  • During vendor AI tool onboarding
  • After AI incident or near miss

Before vs. after

Before
AI governance feels abstract, stuck in policy debates, and disconnected from real product decisions.
After
You lead the first working NIST AI RMF implementation, with documented processes, stakeholder alignment, and a template others reuse.

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 week over 12 weeks, with flexible pacing and lifetime access.

If nothing changes
Without a clear implementation path, AI governance remains theoretical, missing the chance to demonstrate leadership and earn high-visibility recognition.

How this compares to the alternatives

Generic AI ethics courses teach principles but not implementation. Competitor bootcamps focus on compliance checklists. This course delivers the only field-tested path to ship a working NIST AI RMF rollout that gets noticed by senior sponsors.

Frequently asked

Is this course technical or strategic?
It's practitioner-focused, concrete steps to implement governance in real workflows, not theory or high-level concepts.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Do I need approval to implement this?
No, start small with your team and scale using the playbook. Designed for ICs driving change bottom-up.
$199 one-time. Approximately 3 hours per week over 12 weeks, with flexible pacing and lifetime 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
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