A tailored course, built for your situation
Influence in Technical Decision-Making with NIST AI RMF
Turn AI governance expertise into peer-level authority on system design and implementation choices
$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.
Being technically sound isn’t enough if your recommendations don’t shape decisions
The situation this course is for
Strong engineers often see risks early but get overruled or ignored in design phases. Without structured influence, even the best insights stay reactive.
Who this is for
Senior data and AI engineers who are expected to contribute to governance but lack formal authority
Who this is not for
Entry-level practitioners, managers looking for team-wide compliance tools, or executives seeking board-level narratives
What you walk away with
- Lead design reviews with structured, framework-aligned reasoning
- Position yourself as the go-to person for NIST AI RMF interpretation
- Deflect misaligned implementations using pre-built response templates
- Build credibility across teams without formal authority
- Anticipate upstream decisions and insert input proactively
The 12 modules (with all 144 chapters)
Module 1. Understanding Influence Without Authority
Learn how technical practitioners gain decision leverage through clarity, consistency, and framework fluency rather than hierarchy.
12 chapters in this module
- Defining influence in engineering contexts
- Examples from AI infrastructure teams
- The role of NIST AI RMF as a neutral standard
- How peers accept input from non-leaders
- Identifying high-leverage decision points
- Mapping influence pathways in your org
- Common mistakes that reduce credibility
- Building trust before you need it
- Using frameworks to depersonalize feedback
- Positioning input as enablers, not blockers
- The timing of early vs late input
- Creating visibility without overstepping
Module 2. NIST AI RMF Structure and Roles
Break down the NIST AI RMF into actionable components relevant to data and infrastructure engineers.
12 chapters in this module
- Overview of NIST AI RMF core functions
- Mapping roles to engineering workflows
- Governance vs implementation responsibilities
- How mapping supports influence
- Using taxonomy to clarify scope
- Translating principles to code-level checks
- Integrating risk categories into design docs
- Linking decisions to accountability
- Documenting assumptions systematically
- Versioning control for framework use
- Cross-referencing with data lineage
- Maintaining alignment over time
Module 3. Anticipating Design Review Input
Develop the ability to predict where input is needed and prepare ahead of time.
12 chapters in this module
- Reading project specs for triggers
- Flagging high-risk patterns early
- Pre-building common response templates
- Using NIST AI RMF to justify intervention
- Knowing when to escalate vs advise
- Aligning with data stewards preemptively
- Mapping data flows to risk domains
- Assessing model purpose and impact
- Identifying unsafe deployment paths
- Documenting assumptions for traceability
- Creating reusable risk assessments
- Scaling input across projects
Module 4. Framing Recommendations with Authority
Craft input that gets adopted by using neutral language, verifiable standards, and peer-tested phrasing.
12 chapters in this module
- Starting with shared goals
- Using NIST AI RMF language objectively
- Avoiding blame in feedback
- Structuring comments around risk domains
- Referencing precedent from other teams
- Phrasing trade-offs neutrally
- Linking to compliance expectations
- Highlighting operational impact
- Offering alternatives, not just objections
- Using data to support claims
- Tying recommendations to uptime
- Balancing innovation and risk
Module 5. Building Repeatable Influence Playbooks
Create templates and workflows that compound your impact across projects and teams.
12 chapters in this module
- Capturing what works in governance
- Designing reusable checklists
- Template for risk assessment input
- Standard wording for common issues
- Versioning and change tracking
- Integrating with ticketing systems
- Sharing playbooks with peers
- Onboarding others to your approach
- Measuring influence over time
- Adapting to new frameworks
- Updating based on audit findings
- Scaling beyond individual projects
Module 6. Navigating Peer Resistance
Handle skepticism and pushback with confidence and structure.
12 chapters in this module
- Recognising valid counterpoints
- Differentiating ego from risk
- Asking clarifying questions
- Using NIST AI RMF as common ground
- Acknowledging constraints fairly
- Focusing on outcomes, not ownership
- Escalating disagreements constructively
- Documenting decisions and rationale
- Avoiding adversarial tone
- Staying collaborative under pressure
- Knowing when to stand firm
- Preserving relationships after debate
Module 7. Integrating with Vendor Selection
Apply NIST AI RMF to third-party tools and platforms before procurement decisions.
12 chapters in this module
- Evaluating vendor AI claims
- Mapping features to risk domains
- Asking the right due diligence questions
- Using NIST AI RMF in RFPs
- Benchmarking against peer tools
- Identifying hidden limitations
- Documenting vendor risk profiles
- Aligning with security teams
- Negotiating controls pre-contract
- Building internal scorecards
- Tracking vendor compliance changes
- Planning for exit paths
Module 8. Shaping Internal Policy Development
Move from implementing policy to helping design it using real-world data and precedent.
12 chapters in this module
- Identifying gaps in current policies
- Proposing updates based on incidents
- Using NIST AI RMF to justify changes
- Gathering peer input systematically
- Drafting policy language clearly
- Aligning with legal and compliance
- Testing policy feasibility
- Running small-scale pilots
- Measuring adoption and impact
- Revising based on feedback
- Documenting exceptions and waivers
- Archiving outdated versions
Module 9. Leading Cross-Functional Workshops
Run sessions that align engineering, data science, and product teams around AI risk.
12 chapters in this module
- Setting workshop goals
- Selecting participants strategically
- Preparing NIST AI RMF materials
- Facilitating without authority
- Managing dominant voices
- Capturing decisions visibly
- Linking outcomes to roadmap
- Following up on action items
- Measuring workshop effectiveness
- Repeating with new teams
- Scaling facilitation skills
- Creating workshop templates
Module 10. Documenting Impact for Career Growth
Show influence in ways that are visible and valued by leadership.
12 chapters in this module
- Tracking prevented incidents
- Quantifying time saved in rework
- Capturing peer testimonials
- Linking input to system stability
- Creating summary reports
- Sharing wins without self-promotion
- Aligning with performance goals
- Requesting feedback on input
- Building a portfolio of contributions
- Positioning for leadership roles
- Mentoring others in influence
- Extending reach across orgs
Module 11. Sustaining Influence Over Time
Maintain credibility and relevance as AI systems and teams evolve.
12 chapters in this module
- Updating playbooks regularly
- Tracking framework changes
- Staying ahead of industry shifts
- Reconnecting with stakeholders
- Avoiding influence fatigue
- Rotating responsibilities fairly
- Onboarding successors
- Recognising diminishing returns
- Re-evaluating high-touch processes
- Automating routine input
- Focusing on next-level risks
- Scaling impact through systems
Module 12. Final Implementation and Integration
Put everything together with a real-world simulation and final playbook delivery.
12 chapters in this module
- Choosing a live project to apply to
- Running a pre-mortem using NIST AI RMF
- Drafting influence strategy
- Engaging stakeholders proactively
- Hosting a mock design review
- Incorporating feedback
- Finalising documentation
- Measuring outcome success
- Sharing lessons learned
- Updating personal playbook
- Planning for next cycle
- Celebrating peer recognition
How this maps to your situation
- Before a major AI system design review
- When reviewing vendor proposals for AI tools
- After a policy gap is exposed in production
- When building internal governance playbooks
Before vs. after
Before
Input gets debated, deferred, or overridden in technical decisions.
After
Recommendations are adopted as standard practice across teams.
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, designed for working professionals to complete at their own pace over 6-8 weeks.
If nothing changes
Without structured influence, even accurate technical insights can be ignored, leading to rework, risk exposure, and missed opportunities to shape systems.
How this compares to the alternatives
Unlike generic AI ethics courses, this program focuses specifically on how engineers can exert influence in real design decisions using the NIST AI RMF as a leverage tool, without needing managerial authority.
Frequently asked
Who is this course for?
Senior data and AI engineers who want to shape technical decisions through governance influence.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this if I'm not in a leadership role?
Yes, this course is designed specifically for individual contributors who want to lead through influence, not title.
$199 one-time. Approximately 3 hours per module, designed for working professionals to complete at their own pace over 6-8 weeks..
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