A tailored course, built for your situation
Mastering ISO 42001 for General Managers in Automotive Remarketing
Build AI governance practices that align with operational leadership expectations and peer-reviewed decision frameworks.
$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 excluded from AI vendor reviews despite operational ownership of outcomes
The situation this course is for
AI governance decisions are being made without input from operational leaders who own downstream execution, leading to misaligned tools, rework, and delayed ROI.
Who this is for
Senior operational leader in asset-intensive industries who influences technology adoption but lacks formal governance authority
Who this is not for
Entry-level compliance staff, pure IT administrators, or technical auditors without line-of-sight to executive decision rhythm
What you walk away with
- Ability to lead ISO 42001 scoping discussions with internal stakeholders
- Documented justification framework for AI tool selection aligned with operational risk tolerance
- Clarity on how to position AI governance inputs during vendor evaluation cycles
- Confidence in shaping internal policy drafts that reflect real-world execution constraints
- Strategic leverage in cross-functional meetings where AI direction is set
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 and Its Role in Operational Leadership
Explore how ISO 42001 creates opportunities for non-technical leaders to influence AI governance. This module introduces the standard’s structure, its relationship to business outcomes, and why operational ownership matters in AI risk decisions.
12 chapters in this module
- What ISO 42001 means for non-technical leaders
- AI governance vs AI compliance: knowing the difference
- Why operational risk ownership grants decision leverage
- How ISO 42001 elevates cross-functional input
- Real-world examples from asset management firms
- Defining your sphere of influence in AI policy
- Mapping current workflows to governance gaps
- Identifying where input is currently excluded
- Benchmarking peer involvement levels
- Positioning governance as an enabler, not a gate
- Connecting AI use cases to business continuity
- Establishing leadership credibility in framework talks
Module 2. Scoping AI Systems Within Your Region
Learn how to define the boundaries of AI governance within your geographic and functional domain. This module walks through identifying active and planned AI-influenced processes, determining system classification, and documenting scope justification for internal alignment.
12 chapters in this module
- Locating AI-impacted workflows in remarketing
- Classifying systems by impact level
- Differentiating automation from AI decision support
- Creating a regional asset inventory
- Determining operational control boundaries
- Defining 'in scope' for audits and reviews
- Documenting assumptions for peer review
- Engaging IT without overstepping
- Aligning with corporate risk appetite
- Building consensus on scope ownership
- Handling shared systems with HQ
- Versioning scope documents for reuse
Module 3. Stakeholder Mapping for Influence Expansion
Identify key players in AI governance decisions and understand how to position yourself within their workflows. This module covers mapping technical, compliance, and executive stakeholders and designing communication pathways that elevate your input.
12 chapters in this module
- Who controls AI budget approvals
- Finding indirect influence points
- Understanding legal and compliance thresholds
- Navigating corporate ESG commitments
- Positioning risk insights as enablers
- Creating value narratives for leadership
- Building alliances with data owners
- Timing input for maximum receptivity
- Anticipating pushback from central teams
- Using precedent to justify involvement
- Documenting decision rationales
- Elevating issues without over-escalating
Module 4. Risk Assessment from an Operational Lens
Develop risk evaluation skills tailored to frontline realities. This module teaches how to assess AI risks using operational data, incident trends, and execution constraints rather than theoretical models.
12 chapters in this module
- Sources of operational AI risk
- Translating technical failures into business impact
- Using past incident data in risk scoring
- Assessing vendor reliability through operational history
- Evaluating explainability needs by use case
- Balancing speed and control in deployment
- Defining acceptable failure modes
- Incorporating workforce adaptability
- Measuring change readiness across locations
- Using maintenance logs as risk signals
- Linking AI outcomes to customer satisfaction
- Creating defensible risk registers
Module 5. Vendor Selection with Governance Authority
Gain confidence in leading or influencing AI vendor evaluations. This module provides a structured approach to scoring proposals using ISO 42001 principles, ensuring operational needs shape technical choices.
12 chapters in this module
- Defining evaluation criteria aligned with ISO 42001
- Weighting reliability over features
- Assessing vendor documentation practices
- Verifying third-party audit readiness
- Evaluating model transparency commitments
- Reviewing update and rollback processes
- Scoring change management maturity
- Testing support responsiveness
- Benchmarking against peer implementations
- Capturing evaluation rationale
- Influencing scoring without formal authority
- Creating audit-ready selection trails
Module 6. Policy Development That Sticks
Learn how to draft AI policies that gain buy-in and are actually followed. This module focuses on creating practical, enforceable guidelines rooted in real workflows, not theoretical ideals.
12 chapters in this module
- Starting with behavioral observations
- Writing policies operators will follow
- Linking rules to existing procedures
- Using incident post-mortems as input
- Avoiding over-compliance pitfalls
- Phasing adoption by risk tier
- Securing early adopter champions
- Documenting exceptions and waivers
- Aligning with corporate compliance teams
- Updating policies based on feedback
- Measuring compliance through action
- Archiving outdated versions clearly
Module 7. Training and Change Adoption Planning
Design training that drives real adoption of AI governance practices. This module teaches how to assess readiness, identify resistance points, and deliver role-specific guidance.
12 chapters in this module
- Diagnosing change readiness levels
- Identifying peer-to-peer learning opportunities
- Designing micro-training for field staff
- Tailoring messaging by role type
- Using supervisors as advocates
- Creating job aids for high-frequency tasks
- Planning rollout timing around operations
- Measuring comprehension through practice
- Reducing cognitive load in instructions
- Incorporating local feedback loops
- Tracking adoption without surveillance
- Celebrating early wins visibly
Module 8. Internal Audit Preparation and Participation
Prepare for and lead internal audits confidently. This module covers how to gather evidence, respond to findings, and use audits as influence-building opportunities.
12 chapters in this module
- Distinguishing internal from external audits
- Building audit packs proactively
- Selecting representative samples
- Preparing teams for inquiry
- Anticipating auditor questions
- Responding to findings constructively
- Using audit results to justify investment
- Tracking corrective actions to closure
- Sharing outcomes across regions
- Improving audit efficiency over cycles
- Documenting process improvements
- Creating reusable audit templates
Module 9. Management Review and Strategic Input
Position yourself as a strategic contributor in leadership reviews. This module shows how to frame governance updates as business enablers and secure ongoing support.
12 chapters in this module
- Crafting concise review summaries
- Highlighting risk reduction outcomes
- Connecting AI governance to financials
- Using metrics leadership trusts
- Proposing next-phase initiatives
- Balancing transparency with confidence
- Addressing unspoken leadership concerns
- Positioning wins as team achievements
- Requesting resources without urgency
- Aligning roadmap with corporate goals
- Measuring long-term maturity growth
- Documenting review discussions
Module 10. Continual Improvement Through Feedback
Implement a feedback-driven improvement cycle for AI governance. This module teaches how to gather insights from operations, analyze trends, and implement changes that stick.
12 chapters in this module
- Designing non-punitive reporting
- Collecting near-miss observations
- Analyzing root causes without blame
- Prioritizing improvements by impact
- Testing changes at pilot scale
- Scaling what works across regions
- Documenting lessons formally
- Updating training and policies
- Recognizing contributors publicly
- Measuring improvement effectiveness
- Integrating with existing quality loops
- Creating improvement playbooks
Module 11. Preparing for Certification and Beyond
Understand what external certification entails and how to prepare without compromising operational integrity. This module guides you through readiness without overhauling proven workflows.
12 chapters in this module
- Understanding certification scope options
- Assessing third-party auditor fit
- Preparing documentation packages
- Conducting pre-audit self-checks
- Coaching teams for interview readiness
- Managing auditor access respectfully
- Responding to nonconformities
- Planning for surveillance audits
- Using certification as a credibility tool
- Maintaining compliance post-certification
- Sharing success without boasting
- Evolving beyond minimum requirements
Module 12. Sustaining Influence Across Cycles
Learn how to maintain and grow your role in AI governance over time. This module focuses on institutionalizing practices, mentoring peers, and adapting to new technologies.
12 chapters in this module
- Embedding governance in onboarding
- Mentoring emerging leaders
- Updating playbooks with new insights
- Sharing successes across regions
- Adapting to new AI capabilities
- Revisiting risk assessments regularly
- Refreshing policies proactively
- Maintaining stakeholder maps
- Tracking industry developments
- Positioning Copart as a thought leader
- Balancing innovation and control
- Leaving a governance legacy
How this maps to your situation
- When launching new AI tools across regions
- Before major vendor contract renewals
- During internal audit preparation cycles
- When corporate mandates new governance standards
Before vs. after
Before
Reacting to AI governance decisions made without your input, struggling to influence vendor choices, and lacking structured frameworks to justify operational concerns.
After
Proactively shaping AI governance in your region, leading vendor evaluations with confidence, and building recognized expertise that draws peers into your orbit.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters total)
- 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 completion within 12 weeks with flexible pacing.
If nothing changes
Continuing to be excluded from AI decisions that directly impact operational performance, resulting in misaligned tools, rework, and diminished leadership relevance.
How this compares to the alternatives
Unlike generic AI ethics courses or technical compliance trainings, this program is built specifically for operational leaders who must influence AI governance without formal authority, giving you practical tools to shape decisions where it matters most.
Frequently asked
Is this course technical?
No, it's designed for non-technical leaders. It focuses on influence, decision participation, and policy shaping, not coding or model development.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this if my corporate team leads AI governance?
Yes. The course teaches how to position input effectively, even without formal ownership, using ISO 42001 as a shared framework.
$199 one-time. Approximately 3 hours per module, designed for completion within 12 weeks with flexible pacing..
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