A tailored course, built for your situation
Mastering ISO 42001 for Energy and Utility Sector Leaders
Build authoritative control frameworks for AI governance in regulated infrastructure environments
$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.
Audit evidence packages requiring last-minute sourcing under regulator cycles
Who this is for
Senior functional leader in energy and utilities at a global systems integrator, accountable for governance outcomes across multi-vendor AI deployments
Who this is not for
Entry-level auditors, software-only AI vendors, or practitioners outside regulated infrastructure sectors
What you walk away with
- Map ISO 42001 requirements directly to utility-sector AI use cases
- Produce complete control documentation within 1 sprint
- Anticipate regulator follow-ups on high-risk clauses
- Standardize evidence collection across distributed engineering teams
- Reduce rework in compliance cycles by 70%
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 42001 in Regulated Industries
Establish the core structure and intent of ISO 42001, tailored to energy and utility contexts. Understand how its AI governance clauses differ from generic compliance frameworks and why adoption is accelerating in infrastructure sectors.
12 chapters in this module
- Understanding the scope and applicability of ISO 42001
- Key differences between ISO 42001 and other AI governance standards
- Regulatory drivers behind adoption in energy utilities
- Mapping organizational boundaries for AI system oversight
- Defining AI system inventory and classification criteria
- Establishing governance roles and responsibilities
- Integrating ISO 42001 with existing compliance frameworks
- Assessing current posture against clause 4 requirements
- Documenting leadership commitment and policy intent
- Identifying high-risk AI use cases in utility operations
- Developing a timeline for implementation milestones
- Aligning with internal audit and risk management functions
Module 2. Clause 4 Deep Dive: Organizational Context and Scope
Explore the foundational clause of ISO 42001, focusing on how energy organizations define their AI governance boundaries. Learn to distinguish internal vs external stakeholders and document decision rights.
12 chapters in this module
- Defining the operational context of AI systems
- Identifying internal stakeholders and their influence
- Mapping external dependencies and third-party roles
- Setting clear scope boundaries for certification
- Documenting regulatory and contractual obligations
- Analyzing upstream and downstream data flows
- Classifying AI systems by impact level
- Creating a scope statement for audit readiness
- Establishing change control for scope updates
- Validating scope completeness with technical teams
- Integrating scope documentation with asset registers
- Preparing for auditor questions on exclusion claims
Module 3. Clause 5: Leadership and Governance Accountability
Examine how senior leaders establish AI governance tone and commitment. Focus on documenting decision-making authority and policy enforcement in utility-scale deployments.
12 chapters in this module
- Defining leadership roles in AI governance
- Documenting formal governance committee charters
- Establishing policy approval workflows
- Assigning accountability for risk decisions
- Integrating AI oversight into existing leadership forums
- Creating escalation paths for non-compliance
- Tracking policy exception approvals
- Measuring leadership engagement with governance metrics
- Conducting regular governance health assessments
- Aligning with C-suite reporting rhythms
- Updating governance documentation after leadership changes
- Preparing governance artefacts for external review
Module 4. Clause 6: Risk Assessment and Treatment Planning
Learn how to operationalize risk assessments specific to utility AI systems. Build repeatable processes for identifying, scoring, and mitigating AI-related risks.
12 chapters in this module
- Defining risk criteria aligned with sector norms
- Identifying AI-specific threat vectors
- Mapping risks to business impact scenarios
- Scoring likelihood and severity consistently
- Documenting risk treatment options
- Selecting appropriate controls for high-risk areas
- Assigning ownership for risk mitigation actions
- Integrating risk register with project delivery
- Tracking risk closure timelines
- Updating assessments after system changes
- Validating effectiveness of risk treatments
- Preparing risk narratives for auditor review
Module 5. Clause 7: Resource Management and Competency Development
Address human and technical resource needs for sustainable AI governance. Focus on capability building and knowledge retention in distributed teams.
12 chapters in this module
- Assessing current team competencies in AI governance
- Identifying skill gaps in technical and compliance roles
- Developing training plans for key personnel
- Documenting resource allocation for AI systems
- Establishing vendor oversight requirements
- Creating knowledge transfer protocols
- Maintaining documentation standards across regions
- Ensuring language accessibility of governance materials
- Tracking certification and training completion
- Evaluating effectiveness of competency programs
- Integrating onboarding with governance expectations
- Updating resource plans for new deployments
Module 6. Clause 8: AI System Lifecycle Controls
Implement governance controls across the full AI lifecycle from design to decommissioning. Focus on utility-specific challenges like legacy integration and safety margins.
12 chapters in this module
- Defining AI system lifecycle phases
- Establishing governance checkpoints at each stage
- Documenting data provenance and lineage
- Verifying model validation procedures
- Monitoring performance degradation over time
- Implementing change management for model updates
- Conducting impact assessments before deployment
- Ensuring human oversight mechanisms are active
- Capturing decommissioning criteria and triggers
- Maintaining audit trails for model decisions
- Integrating with incident response workflows
- Reviewing lifecycle compliance during audits
Module 7. Clause 9: Performance Monitoring and Reporting
Design monitoring systems that provide actionable insights into AI governance performance. Learn to create reports that satisfy both technical and executive audiences.
12 chapters in this module
- Defining key performance indicators for AI systems
- Establishing data collection methods for metrics
- Creating automated dashboards for governance teams
- Reporting on compliance status to leadership
- Conducting regular management reviews
- Identifying trends in control failures
- Benchmarking performance against peer organizations
- Integrating monitoring with continuous improvement
- Updating reporting formats based on feedback
- Preparing monitoring evidence for auditors
- Handling exceptions and deviation reports
- Ensuring monitoring continuity during outages
Module 8. Clause 10: Nonconformity and Corrective Action Management
Develop robust processes for identifying and resolving AI governance issues. Focus on root cause analysis and preventing recurrence in mission-critical systems.
12 chapters in this module
- Defining nonconformity criteria for AI systems
- Establishing incident reporting workflows
- Conducting root cause analysis using sector methods
- Assigning ownership for corrective actions
- Tracking resolution timelines and effectiveness
- Integrating with existing quality management systems
- Documenting lessons learned from incidents
- Updating controls based on failure patterns
- Validating corrective action implementation
- Preparing incident summaries for external review
- Managing communication during public incidents
- Applying corrective actions across similar systems
Module 9. Clause 11: Internal Audit and Assurance Processes
Build internal audit capabilities that ensure ongoing compliance with ISO 42001. Focus on planning, execution, and follow-up specific to AI systems.
12 chapters in this module
- Planning annual internal audit schedules
- Selecting qualified auditors for AI systems
- Developing audit checklists based on ISO 42001 clauses
- Conducting field audits of AI operations
- Documenting audit findings and observations
- Presenting results to governance committees
- Tracking closure of audit recommendations
- Integrating audit findings with risk assessments
- Evaluating effectiveness of audit processes
- Preparing internal audit documentation for certification
- Coordinating with external audit timelines
- Improving audit methods based on feedback
Module 10. Clause 12: Certification Readiness and External Audit Preparation
Prepare for external certification audits with confidence. Learn how to organize documentation, train teams, and respond to auditor inquiries.
12 chapters in this module
- Understanding certification body requirements
- Preparing documentation for external review
- Conducting pre-assessment readiness checks
- Training teams on auditor interaction protocols
- Responding to auditor findings and questions
- Addressing minor and major nonconformities
- Scheduling surveillance audits
- Maintaining certification over time
- Updating documentation after organizational changes
- Preparing for recertification cycles
- Leveraging certification for client trust
- Communicating certification status externally
Module 11. Cross-Industry Application: Energy and Utility Use Cases
Apply ISO 42001 principles to real-world energy and utility scenarios. Explore examples from grid management, customer service automation, and predictive maintenance.
12 chapters in this module
- AI for grid stability and load forecasting
- Customer-facing chatbots in energy services
- Predictive maintenance for transmission systems
- Automated billing and dispute resolution
- AI in outage response coordination
- Environmental monitoring with computer vision
- Workforce safety with AI-assisted supervision
- Demand-side management algorithms
- Fraud detection in metering systems
- Supply chain optimization with AI
- Cybersecurity threat prediction models
- Climate risk modeling for infrastructure
Module 12. Sustaining and Scaling the AI Governance Framework
Ensure long-term success of AI governance by building scalable processes and knowledge sharing mechanisms. Focus on adaptability to new technologies and regulations.
12 chapters in this module
- Establishing continuous improvement cycles
- Incorporating lessons from audits and incidents
- Updating the framework for new AI technologies
- Sharing best practices across regions
- Mentoring emerging practitioners
- Integrating with broader digital transformation
- Measuring ROI of governance investments
- Adapting to regulatory changes
- Scaling processes for new business units
- Maintaining stakeholder engagement
- Preserving institutional knowledge
- Celebrating governance successes
How this maps to your situation
- Preparation for upcoming regulatory reviews
- Implementation of AI governance across multi-country operations
- Integration with existing compliance and risk frameworks
- Leadership accountability for AI system outcomes
Before vs. after
Before
Spending 80+ hours assembling fragmented evidence for AI governance reviews, chasing inputs across teams, and responding to auditor follow-ups without a centralized reference.
After
Completing validation cycles in under 6 hours using standardized templates, repeatable processes, and a fully documented control framework aligned with ISO 42001.
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 90 minutes per module, designed to be completed over Sunday mornings or weekday evenings without disrupting core responsibilities.
If nothing changes
Without a structured approach, teams risk repeated audit findings, inconsistent application of controls, increased rework, and potential regulatory scrutiny. Knowledge remains siloed, making governance fragile to personnel changes.
How this compares to the alternatives
Unlike generic AI ethics courses or broad compliance overviews, this course delivers actionable, clause-by-clause implementation guidance for ISO 42001, specifically tailored to energy and utility sector challenges and multi-region operations.
Frequently asked
Is this course aligned with the latest version of ISO 42001?
Yes, all content reflects the most current ISO 42001 standard as of this cycle, including recent clarifications relevant to infrastructure sectors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-IBM client projects?
Absolutely , the framework is designed for transferability across energy and utility organizations, regardless of vendor ecosystem.
$199 one-time. Approximately 90 minutes per module, designed to be completed over Sunday mornings or weekday evenings without disrupting core responsibilities..
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