A tailored course, built for your situation
Mastering ISO 42001 for City Senior Chief Engineers
Build AI governance frameworks that elevate engineering leadership visibility
$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.
AI governance remains invisible despite heavy engineering lift
The situation this course is for
Teams build compliant AI systems, but the effort fades from executive memory once audit season ends. The work passes review but doesn’t position engineers as strategic leaders.
Who this is for
Senior engineering leader in commercial real estate or facilities management, accountable for system-wide AI governance and technical compliance, with influence across city-level operations and vendor integrations.
Who this is not for
Junior engineers, non-technical compliance staff, or practitioners focused solely on data privacy without engineering integration.
What you walk away with
- Demonstrate ISO 42001 compliance through engineering artifacts already in use
- Shape executive understanding of AI governance through clear system boundary definitions
- Turn internal audits into opportunities to showcase engineering leadership
- Produce governance documentation that persists beyond team changes
- Gain recognition from leadership for systems that run quietly and stay compliant
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 in the context of city-scale engineering
Establish the foundational relationship between AI governance requirements and existing infrastructure oversight. Learn how ISO 42001 complements current asset lifecycle management and system resilience standards.
12 chapters in this module
- Defining the scope of AI systems under city operations
- Mapping ISO 42001 clauses to engineering control points
- Aligning AI governance with facility performance benchmarks
- Integrating AI inventory requirements into asset registers
- Distinguishing AI systems from standard automation workflows
- Identifying AI use cases already live in building systems
- Establishing governance ownership across distributed teams
- Documenting legacy AI decision logic for audit readiness
- Linking AI outputs to energy and occupancy reporting
- Using facility performance data to inform governance tiers
- Assessing vendor-built AI systems against ISO 42001
- Creating a city-level AI system taxonomy
Module 2. Building AI governance into engineering leadership roles
Position AI governance as a core engineering function, not an add-on. Equip senior engineers to lead governance without relying on centralized compliance teams.
12 chapters in this module
- Defining leadership responsibilities under ISO 42001
- Embedding governance into engineering design reviews
- Developing standard playbooks for AI deployment
- Delegating control ownership without losing oversight
- Creating escalation paths for AI model drift
- Establishing metrics for AI system stability
- Documenting engineering decisions for audit transparency
- Training site engineers on AI governance basics
- Integrating AI reviews into preventive maintenance cycles
- Using incident logs to improve AI model monitoring
- Standardizing responses to AI system alerts
- Maintaining governance documentation during team turnover
Module 3. AI system inventory and boundary definition
Create a living inventory of AI systems across city operations, with clear boundaries and ownership. Ensure every system is accounted for and governed appropriately.
12 chapters in this module
- Identifying AI systems in HVAC optimization workflows
- Mapping AI-driven access control systems
- Cataloging predictive maintenance models in use
- Defining system boundaries for multi-vendor integrations
- Documenting data flows in AI-enabled security systems
- Assigning ownership for edge AI devices
- Tracking AI models used in energy forecasting
- Classifying AI systems by risk tier and impact
- Creating visual system boundary diagrams
- Versioning AI inventory documentation
- Updating inventories during system upgrades
- Integrating inventory updates into change management
Module 4. Establishing AI governance control frameworks
Implement controls that align with ISO 42001 requirements while fitting into existing engineering processes and tooling.
12 chapters in this module
- Integrating AI controls into existing compliance checklists
- Defining control ownership for AI model updates
- Setting thresholds for AI performance monitoring
- Creating audit trails for AI decision changes
- Documenting rationale for AI model selection
- Establishing version control for AI logic
- Linking AI controls to facility uptime metrics
- Automating control verification where possible
- Reviewing AI model inputs for data drift
- Setting up manual override procedures
- Validating AI outputs against expected ranges
- Maintaining control documentation during audits
Module 5. Risk assessment specific to AI in built environments
Adapt ISO 42001 risk assessment methods to the unique challenges of AI in physical infrastructure and facilities management.
12 chapters in this module
- Identifying safety risks in AI-driven systems
- Assessing impact of AI failure on occupant comfort
- Evaluating cybersecurity risks in edge AI devices
- Mapping AI dependencies across mechanical systems
- Prioritizing risks by operational criticality
- Documenting risk treatment decisions
- Integrating AI risk findings into safety reports
- Updating risk assessments after system changes
- Benchmarking AI risks against industry baselines
- Communicating risk posture to facilities leadership
- Using risk matrices for AI governance planning
- Linking risk treatment to capital planning cycles
Module 6. Integrating AI governance with facilities operations
Ensure AI governance practices are sustainable within ongoing operations, not just during audits or deployments.
12 chapters in this module
- Incorporating AI checks into preventive maintenance
- Training facilities staff on AI system behavior
- Creating standard operating procedures for AI alerts
- Documenting AI system dependencies in work orders
- Using CMMS data to track AI system performance
- Aligning AI governance with energy management goals
- Integrating AI model updates into change control
- Establishing vendor SLAs for AI maintenance
- Measuring AI system uptime alongside other KPIs
- Reporting AI performance in operations dashboards
- Updating training materials after AI changes
- Auditing AI system logs during routine inspections
Module 7. Documentation practices for audit-ready AI governance
Create documentation that satisfies ISO 42001 requirements while being practical for engineering teams to maintain.
12 chapters in this module
- Structuring AI governance documentation for clarity
- Using visual diagrams to explain AI system flows
- Documenting rationale for AI model decisions
- Maintaining version history for AI logic changes
- Creating audit-ready package from engineering records
- Linking documentation to existing asset files
- Using standardized templates across sites
- Storing documentation in accessible locations
- Ensuring documentation survives team changes
- Aligning documentation with executive reporting
- Reducing redundancy in multi-system environments
- Automating documentation updates where possible
Module 8. Leadership communication on AI governance
Shape how senior leaders understand and value AI governance through targeted communication and visibility.
12 chapters in this module
- Translating AI governance into business outcomes
- Reporting on AI system stability and uptime
- Highlighting risk reduction from governance work
- Creating executive summaries from audit results
- Using facility performance data to show AI impact
- Presenting governance work in leadership forums
- Connecting AI efforts to ESG reporting goals
- Sharing lessons from AI incident responses
- Demonstrating cost avoidance through governance
- Positioning engineering as AI governance leaders
- Using peer benchmarks to show maturity gains
- Integrating AI updates into operational briefings
Module 9. Vendor management in AI governance
Extend governance to third-party AI systems and ensure vendors comply with ISO 42001 requirements.
12 chapters in this module
- Assessing vendor AI systems against ISO 42001
- Defining contractual obligations for AI updates
- Requiring transparency in AI model logic
- Establishing access rights for system audits
- Verifying vendor documentation completeness
- Setting performance expectations for AI models
- Monitoring vendor compliance over contract life
- Handling AI system handovers from vendors
- Auditing vendor-supported AI systems
- Managing cybersecurity requirements for cloud AI
- Enforcing data governance in vendor integrations
- Creating exit strategies for AI vendor contracts
Module 10. Continuous improvement in AI governance
Build feedback loops that improve AI governance over time, using real-world performance and audit findings.
12 chapters in this module
- Using audit findings to refine control design
- Tracking AI system incidents for patterns
- Updating governance after technology changes
- Benchmarking against ISO 42001 updates
- Incorporating lessons from peer organizations
- Measuring governance maturity over time
- Soliciting feedback from operations teams
- Aligning improvements with capital planning
- Using incident data to prioritize updates
- Sharing best practices across city teams
- Updating training materials after changes
- Documenting improvements for future audits
Module 11. Scaling AI governance across city operations
Extend successful AI governance practices across multiple sites and systems without sacrificing quality or oversight.
12 chapters in this module
- Standardizing AI governance across buildings
- Creating central oversight without micromanaging
- Delegating implementation to site engineers
- Ensuring consistency in documentation formats
- Using centralized dashboards for visibility
- Conducting cross-site governance reviews
- Sharing learnings between city teams
- Harmonizing AI policies with regional rules
- Managing governance during facility expansions
- Onboarding new buildings into AI frameworks
- Auditing distributed AI systems efficiently
- Maintaining governance during leadership transitions
Module 12. Sustaining AI governance through organizational change
Ensure AI governance persists beyond individual leaders or teams, becoming part of organizational muscle memory.
12 chapters in this module
- Embedding AI governance in engineering onboarding
- Creating playbooks that survive leadership changes
- Using documentation to maintain continuity
- Training backups on critical AI systems
- Preserving institutional knowledge in files
- Linking governance to performance reviews
- Recognizing teams for governance excellence
- Maintaining momentum during restructuring
- Protecting AI governance in budget cycles
- Updating policies during organizational shifts
- Ensuring executive sponsorship continuity
- Measuring long-term governance effectiveness
How this maps to your situation
- Initial ISO 42001 scoping and leadership alignment
- Building inventory and control frameworks
- Integrating governance into operations
- Sustaining compliance through leadership and team changes
Before vs. after
Before
AI governance work remains siloed and invisible outside audit cycles
After
Engineering leadership gains executive recognition for AI system stewardship
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 module, designed to be completed at your pace over 6-8 weeks.
If nothing changes
Without structured AI governance, engineering efforts remain invisible to leadership, risking underinvestment and reactive compliance.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to senior engineering leaders in facilities and built environments, with concrete methods for making AI governance visible and sustainable.
Frequently asked
Is this course focused on certification preparation?
No. This course is designed to implement ISO 42001 within city-level engineering teams, not to pass an exam. The focus is on practical governance integration.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me demonstrate value to executive leadership?
Yes. The course includes methods for translating technical AI governance work into leadership-visible outcomes.
$199 one-time. Approximately 3-4 hours per module, designed to be completed at your 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