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DAT5215 Mastering ISO 42001 for Corporate FP&A Leaders

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Corporate FP&A Leaders

Build audit-ready AI governance frameworks with confidence and precision

$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.
Even strong FP&A teams stall when auditors question AI model provenance

The situation this course is for

AI governance feels abstract until the audit hits, then suddenly everyone wants documentation, control ownership, and traceability. Without a clear framework, FP&A gets pulled into reactive cycles, losing time from strategic work. The issue isn’t effort, it’s not having a recognized, repeatable method baked into planning workflows.

Who this is for

Senior finance leaders at tech-first enterprises who oversee AI-augmented planning and reporting, and must answer to internal audit, compliance, or external regulators

Who this is not for

Individual contributors without cross-functional influence, practitioners outside FP&A or governance roles, or teams focused solely on engineering AI models

What you walk away with

  • Lead ISO 42001 implementation projects with full control over scope and timeline
  • Produce documented governance packages that pass internal review on first submission
  • Become the named reference for AI governance questions across finance and compliance
  • Anticipate auditor questions with pre-built evidence chains specific to FP&A workflows
  • Differentiate FP&A as a governance leader, not just a data consumer

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 42001 and Its Relevance to Financial Planning
Lay the foundation by aligning AI governance principles with FP&A deliverables such as forecasting models, variance analysis, and executive reporting. Learn how ISO 42001 closes visibility gaps in AI-augmented workflows.
12 chapters in this module
  1. Defining AI governance in the context of corporate finance
  2. Mapping ISO 42001 clauses to FP&A processes
  3. How AI use cases differ from traditional financial models
  4. The role of transparency in audit-ready AI systems
  5. Why FP&A owns part of the AI accountability chain
  6. Linking governance to forecast accuracy claims
  7. Common misconceptions about AI in financial systems
  8. Distinguishing between AI oversight and model development
  9. Integrating ISO 42001 into quarterly planning cycles
  10. Aligning with compliance without slowing innovation
  11. Documenting intent for AI-driven scenarios
  12. Preparing for cross-functional alignment meetings
Module 2. Scoping AI Systems Under ISO 42001 for Finance Teams
Identify which tools and models under FP&A fall under governance requirements, including predictive analytics, automated reporting, and self-service dashboards.
12 chapters in this module
  1. Inventorying AI-augmented tools in current FP&A stack
  2. Classifying models by risk and business impact
  3. Determining which systems require formal documentation
  4. Working with data science teams on boundary setting
  5. Handling third-party AI components in forecasts
  6. Documenting human oversight mechanisms
  7. Establishing thresholds for model review frequency
  8. Tracking model updates in planning timelines
  9. Managing exceptions for experimental use cases
  10. Integrating scope decisions into audit logs
  11. Using scope to define escalation paths
  12. Avoiding over-governance of low-risk automation
Module 3. Building the AI Governance Statement of Applicability
Customize a SoA that reflects FP&A’s responsibilities without overreaching into engineering domains.
12 chapters in this module
  1. Purpose and structure of a valid SoA
  2. Extracting relevant controls from ISO 42001 Annex A
  3. Aligning controls with FP&A-specific risks
  4. Documenting justification for exclusions
  5. Linking control ownership to role responsibilities
  6. Creating version-controlled SoA drafts
  7. Reviewing SoA with internal audit in advance
  8. Using SoA to clarify cross-team boundaries
  9. Updating SoA during planning cycle changes
  10. Handling new models introduced mid-quarter
  11. Storing SoA in accessible, non-proprietary formats
  12. Presenting SoA to leadership without jargon
Module 4. Designing AI Risk Assessments for Planning Workflows
Develop repeatable methods to assess risks in AI-augmented forecasting, reporting, and scenario modeling.
12 chapters in this module
  1. Defining risk criteria relevant to FP&A
  2. Scoring model influence on executive decisions
  3. Assessing bias in historical data inputs
  4. Evaluating explainability gaps in vendor tools
  5. Measuring confidence in model outputs
  6. Involving stakeholders in risk validation
  7. Documenting risk acceptance thresholds
  8. Linking risk outcomes to control design
  9. Tracking residual risk in planning memos
  10. Updating assessments after model changes
  11. Avoiding duplication with security team reviews
  12. Using assessments to justify governance effort
Module 5. Implementing Human Oversight Mechanisms
Establish clear review checkpoints for AI-generated outputs used in reporting and forecasting.
12 chapters in this module
  1. Defining when human review is mandatory
  2. Setting thresholds for automated override
  3. Designing sign-off workflows for model outputs
  4. Training FP&A staff on red flag identification
  5. Documenting rationale for accepting model results
  6. Capturing exceptions in audit trails
  7. Balancing speed and scrutiny in monthly closes
  8. Integrating oversight into existing approval chains
  9. Using dashboards to monitor oversight compliance
  10. Auditing oversight effectiveness over time
  11. Handling edge cases with incomplete explanations
  12. Scaling oversight across global reporting teams
Module 6. Documenting AI Training Data and Model Lineage
Create defensible records showing how models were built and maintained, tailored to auditor expectations.
12 chapters in this module
  1. Defining minimum data provenance for FP&A models
  2. Tracking data sources in forecasting pipelines
  3. Documenting assumptions behind training sets
  4. Handling synthetic data in scenario planning
  5. Mapping model versions to planning periods
  6. Recording feature engineering decisions
  7. Storing metadata in non-proprietary formats
  8. Connecting lineage to audit requests
  9. Managing documentation across teams
  10. Updating lineage after model refreshes
  11. Using lineage to defend forecast choices
  12. Avoiding over-documentation of minor changes
Module 7. Ensuring Transparency in AI System Behavior
Communicate how AI supports , not replaces , FP&A judgment in ways auditors and peers trust.
12 chapters in this module
  1. Defining transparency in financial modeling context
  2. Disclosing AI use in executive reports
  3. Creating user guides for AI-augmented tools
  4. Explaining model logic without technical depth
  5. Using plain language for leadership audiences
  6. Publishing model performance metrics
  7. Handling questions about unexpected outputs
  8. Maintaining consistency across reporting units
  9. Updating transparency docs with each release
  10. Archiving historical versions of disclosures
  11. Linking transparency to stakeholder trust
  12. Balancing clarity with confidentiality
Module 8. Managing AI System Lifecycle in FP&A Processes
Incorporate governance checks at each phase from pilot to retirement, aligned with planning cycles.
12 chapters in this module
  1. Defining lifecycle stages for finance AI
  2. Setting entry criteria for pilot models
  3. Conducting performance reviews before rollout
  4. Documenting lessons from failed experiments
  5. Establishing renewal triggers for active models
  6. Handling model deactivation and data purge
  7. Updating governance artifacts at each stage
  8. Involving legal and compliance as needed
  9. Archiving retired model documentation
  10. Scaling lifecycle management across teams
  11. Using lifecycle data to improve planning
  12. Aligning with enterprise AI policy updates
Module 9. Preparing for Internal and External Audits
Anticipate auditor needs and deliver complete, well-organized evidence packages on demand.
12 chapters in this module
  1. Predicting common auditor questions on AI
  2. Organizing documentation by control objective
  3. Creating auditor-friendly summaries of SoA
  4. Compiling evidence trails for model reviews
  5. Preparing FP&A staff for audit interviews
  6. Rehearsing responses to escalation scenarios
  7. Using checklists to ensure coverage
  8. Responding to findings without delay
  9. Tracking open items to resolution
  10. Leveraging audit feedback for improvement
  11. Maintaining audit readiness year-round
  12. Reducing audit fatigue across teams
Module 10. Integrating ISO 42001 with Existing Compliance Frameworks
Align AI governance with SOX, internal audit standards, and other FP&A compliance obligations.
12 chapters in this module
  1. Mapping ISO 42001 to SOX controls
  2. Avoiding duplication with SOX documentation
  3. Using common evidence for multiple audits
  4. Coordinating review timelines across teams
  5. Harmonizing terminology with compliance peers
  6. Translating FP&A governance for broader use
  7. Supporting enterprise AI policy rollouts
  8. Sharing best practices across functions
  9. Leveraging cross-functional efficiencies
  10. Maintaining independence where required
  11. Updating integration as frameworks evolve
  12. Documenting alignment decisions
Module 11. Communicating AI Governance Leadership Across Functions
Position FP&A as a steward of responsible AI use to enhance cross-functional credibility.
12 chapters in this module
  1. Framing governance as strategic enablement
  2. Sharing wins without sounding boastful
  3. Presenting governance work to non-technical peers
  4. Using metrics to show value of oversight
  5. Building trust through consistency
  6. Contributing to enterprise AI councils
  7. Mentoring junior staff on governance basics
  8. Inviting feedback from other departments
  9. Balancing transparency with discretion
  10. Highlighting FP&A’s role in ethical AI
  11. Sustaining visibility beyond audits
  12. Positioning FP&A as a governance innovator
Module 12. Sustaining and Scaling AI Governance Maturity
Embed practices into culture so they survive leadership changes and scale with new use cases.
12 chapters in this module
  1. Measuring governance process effectiveness
  2. Identifying gaps in current implementation
  3. Prioritizing improvements based on risk
  4. Updating training materials for new hires
  5. Scaling documentation practices globally
  6. Automating evidence collection where possible
  7. Maintaining momentum after audit season
  8. Celebrating governance milestones
  9. Learning from peer organizations
  10. Contributing to industry standards
  11. Future-proofing for emerging AI trends
  12. Leaving a documented legacy for successors

How this maps to your situation

  • FP&A leadership in tech enterprises
  • AI governance integration into planning cycles
  • Audit readiness for executive reporting systems
  • Cross-functional credibility in compliance matters

Before vs. after

Before
Spending cycles reworking AI documentation under audit pressure, while peers don't recognize FP&A's governance role.
After
Leading with a documented, repeatable ISO 42001 approach , so your name comes up first when AI governance is discussed.

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, or 36 total hours to complete the course at your pace.

If nothing changes
Without a clear governance method, FP&A risks being seen as a passive consumer of AI tools, rather than a leader shaping their responsible use , limiting influence and visibility.

How this compares to the alternatives

Unlike generic AI ethics courses or engineering-focused certifications, this program is designed specifically for finance leaders who must govern AI systems without becoming technical experts.

Frequently asked

Is this course technical?
No , it’s designed for FP&A leaders who need to govern AI systems, not build them. The focus is on accountability, documentation, and audit readiness.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I share this with my team?
Each enrollment is individual. Team licenses are available via contact.
$199 one-time. Approximately 3 hours per module, or 36 total hours to complete the course at your pace..

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
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