A tailored course, built for your situation
M&A escalations routed to your desk first with NIST AI RMF
Become the trusted owner of high-stakes AI governance decisions in complex deals
$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 seen as a technical contributor instead of a decision owner in cross-functional AI governance
The situation this course is for
Skilled technologists often get bypassed in M&A and regulatory moments because they lack the structured governance language that leadership trusts. Work gets handed off to legal or compliance teams who speak the dialect of risk frameworks, even when they lack the technical depth.
Who this is for
Senior technology leader with platform expertise stepping into broader governance influence
Who this is not for
Entry-level engineers, purely tactical implementers, or those uninterested in owning cross-functional decision streams
What you walk away with
- First access to AI risk escalations from M&A due diligence teams
- Regulator-facing review packages you originate and own
- Board-prep materials that cite your assessments as source
- Escalations from peer engineering teams routed directly to you
- Clear, reusable templates for AI risk documentation under NIST AI RMF
The 12 modules (with all 144 chapters)
Module 1. Mapping deal timelines to NIST AI RMF readiness
Align AI risk assessments with merger cycles so your input becomes non-optional in due diligence.
12 chapters in this module
- Deal-phase triggers for AI risk review
- First-party vs third-party AI inventory
- Speed-to-response benchmarks by deal size
- Ownership handoff from legal to tech
- Integrating with data due diligence teams
- Timeline compression without quality loss
- Scoping AI sprawl in target environments
- Identifying regulator-triggering use cases
- Pre-deal AI control gap assessment
- Defining 'safe harbour' for legacy models
- Checklist for first-day AI stability
- Articulating risk tolerance in acquisition context
Module 2. Ownership of regulator-facing AI documentation
Produce review-ready artefacts that position you as the source of truth for external scrutiny.
12 chapters in this module
- Regulator inquiry response templates
- Model lineage for external audit
- Version control in compliance narratives
- Stakeholder mapping for AI disclosures
- Evidence hierarchy in AI audits
- Handling model drift under scrutiny
- Cross-border AI regulation mapping
- Justifying AI ethics board decisions
- Documenting model decay thresholds
- Incident response playbooks for AI
- Defining 'material change' for AI systems
- Escalation paths when regulators engage
Module 3. Influencing board-prep materials from technical depth
Transform engineering work into narrative inputs that shape leadership decisions.
12 chapters in this module
- Translating model risk into business terms
- Executive summaries that stick
- Risk heatmap design for leadership
- Narrative framing of technical debt
- AI incident probability bands
- Inclusion of AI risk in ERM reports
- Ownership of risk language in filings
- Pre-briefing coordination with legal
- Versioning of board materials
- Handling follow-up questions confidently
- Balancing optimism and realism in AI rollout
- Attribution of success and failure
Module 4. Peer team escalation intake and triage
Design a repeatable process for receiving and resolving AI risk issues from engineering teams.
12 chapters in this module
- Escalation pathway design
- Triage criteria for AI incidents
- Ownership boundary definition
- Routing logic for multi-team issues
- Documentation standards for handbacks
- Feedback loops to development teams
- SLA definition for risk resolution
- Automated triggers for manual review
- Version lock protocols during incidents
- Model rollback decision trees
- Post-mortem ownership and follow-up
- Knowledge transfer after resolution
Module 5. NIST AI RMF control mapping to technical systems
Translate framework requirements into system-specific configurations and checks.
12 chapters in this module
- Mapping 'Govern' to access controls
- Implementing 'Map' in data lineage
- Enforcing 'Measure' in model monitoring
- Automating 'Manage' decisions
- Control depth by model criticality
- Integrating with existing SOC 2 controls
- Versioning of control mappings
- Audit trail requirements for AI decisions
- Human-in-the-loop thresholds
- Threshold tuning for false positives
- Control ownership documentation
- Cross-system control consistency
Module 6. Building reusable AI governance playbooks
Create living documents that compound your impact across deals and teams.
12 chapters in this module
- Playbook structure for AI risk
- Template versioning strategy
- Ownership of updates and revisions
- Integration with onboarding
- Searchable repository design
- Cross-functional access controls
- Change tracking for compliance
- Linking to control frameworks
- Embedding in CI/CD pipelines
- Automated playbook triggers
- Feedback integration from users
- Metrics for playbook effectiveness
Module 7. Cross-functional credibility in AI risk discussions
Earn consistent deference from legal, compliance, and engineering through precision and clarity.
12 chapters in this module
- Speaking compliance language accurately
- Citing framework clauses correctly
- Confidence without overstatement
- Handling pushback from auditors
- Negotiating scope with legal teams
- Presenting to non-technical leaders
- Balancing speed and rigor
- Owning uncertainty transparently
- Setting escalation thresholds
- Building coalitions across functions
- Maintaining neutrality in disputes
- Documenting decisions for audit
Module 8. AI risk communication across technical levels
Deliver messages that land with engineers, managers, and executives without distortion.
12 chapters in this module
- Tiered messaging strategy
- Data drift explanation for sales teams
- Model risk for product managers
- Incident comms to customer support
- Executive summaries of technical risk
- Visuals for non-technical audiences
- Handling media inquiry prep
- Internal comms during AI incidents
- Cross-team alignment sessions
- Translating NIST into plain English
- Avoiding fear-based narratives
- Reinforcing psychological safety
Module 9. Vendor AI risk assessment ownership
Lead third-party AI risk reviews with authority and consistency.
12 chapters in this module
- Questionnaire design for vendors
- Scoring model for AI risk
- Onboarding integration points
- Contractual risk allocation
- Evidence expectations from vendors
- Audit rights negotiation
- SLA alignment with risk tier
- Incident response coordination
- Exit planning for AI services
- Continuous monitoring setup
- Performance vs risk tradeoffs
- Documentation standards for vendor files
Module 10. AI incident response leadership
Take command during AI failures with structured, calm, and effective response.
12 chapters in this module
- Incident classification framework
- Immediate containment steps
- Cross-functional war room setup
- Communication chain of command
- Data preservation protocols
- Regulatory reporting thresholds
- Customer impact assessment
- Public statement coordination
- Internal blameless review
- Remediation roadmap
- Postmortem facilitation
- Lessons integration into playbooks
Module 11. Ownership of AI ethics review cycles
Lead structured evaluations of fairness, bias, and societal impact.
12 chapters in this module
- Bias detection by data type
- Fairness metrics selection
- Stakeholder impact interviews
- Use case acceptability frameworks
- Sunset criteria for models
- Red teaming process design
- Ethics board documentation
- Handling edge case disputes
- Transparency vs IP balance
- Community impact assessments
- Escalation to legal for precedent
- Versioning of ethics decisions
Module 12. Sustaining AI governance maturity
Build systems that keep your organization resilient as AI evolves.
12 chapters in this module
- Maturity model application
- Quarterly governance health check
- Training update cycles
- Framework evolution tracking
- Internal audit collaboration
- Benchmarking against peers
- Investment case for tooling
- Succession planning for roles
- Documentation debt management
- Incentive alignment for compliance
- Scaling governance with AI adoption
- Leadership reporting cadence
How this maps to your situation
- Responding to M&A due diligence requests
- Handling regulatory inquiries about AI systems
- Contributing to executive-level risk discussions
- Resolving peer team escalations on AI risk
Before vs. after
Before
AI risk work flows to legal or compliance teams, even when they lack technical depth. You're consulted occasionally, but not positioned as the owner.
After
M&A teams, regulators, and peer engineering leads bring AI escalations directly to you. You originate the documents, own the narrative, and set the pace.
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 full access to all materials upon enrollment.
If nothing changes
Continuing to be bypassed for high-visibility AI governance roles, even when your technical expertise is critical to getting it right.
How this compares to the alternatives
Generic AI governance courses focus on theory and broad principles. This course delivers specific, actionable playbooks tailored to senior practitioners stepping into trusted decision roles during M&A, regulatory reviews, and peer escalations.
Frequently asked
Who is this course designed for?
Senior technology leaders stepping into governance influence, especially in AI risk, M&A, and regulatory contexts.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive practical templates?
Yes , every module includes downloadable templates and worked examples you can adapt immediately.
$199 one-time. Approximately 3 hours per module, with full access to all materials upon enrollment..
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