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AIG6967 Mastering ISO 42001 for Partners Leading AI Governance in Deals

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Partners Leading AI Governance in Deals

A structured path to authoritative, durable AI governance frameworks tailored for senior practitioners at global firms.

$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 risk assessments in deals that collapse under review, requiring rework and eroding stakeholder trust.

The situation this course is for

In complex M&A environments, AI governance assessments often lack standardized control mappings, leading to reactive evidence gathering, extended review cycles, and weakened position at the negotiation table. Without a recognized framework, even accurate findings can be dismissed as ad hoc.

Who this is for

Senior partner in Deals at a Big 4 firm, leading SPA teams through technically complex acquisitions where AI systems, ESG claims, and operational risk converge. Values authority, precision, and repeatable artefacts that scale across engagements.

Who this is not for

Junior auditors, implementation engineers, or practitioners focused solely on internal AI policy. This is not for those outside transactional or governance decision-making roles.

What you walk away with

  • Ability to produce AI governance assessments that are consistently validated and recognized by review bodies
  • Reusable templates for control mapping under ISO 42001 specific to M&A contexts
  • Clear differentiation as a recognized authority on AI governance in deals
  • Reduced rework in due diligence cycles by leveraging pre-validated control assertions
  • Stronger narrative position when advising clients on AI-related transaction risks

The 12 modules (with all 144 chapters)

Module 1. Introduction to ISO 42001 in M&A Contexts
Establish foundational understanding of how ISO 42001 applies uniquely to transactional AI risk, including scope definition and stakeholder expectations in deals.
12 chapters in this module
  1. Understanding ISO 42001's role in transactional due diligence
  2. Differences between internal AI governance and deal-time assessments
  3. Key clauses in ISO 42001 relevant to acquisition risk
  4. How AI governance maturity affects valuation adjustments
  5. Integrating ISO 42001 with existing SPA frameworks
  6. Common misconceptions about AI audits in M&A
  7. The shift from reactive findings to proactive control mapping
  8. Benchmarking current AI due diligence practices
  9. Case study: AI clause in recent TMT sector acquisition
  10. Role clarity for the SPA lead in AI governance validation
  11. Navigating ESG-linked AI claims in seller disclosures
  12. Common pitfalls in early-stage AI control assessments
Module 2. Control Mapping for AI Systems in Acquired Entities
Learn to translate technical AI implementations into documented, auditable controls aligned with ISO 42001 clauses.
12 chapters in this module
  1. Extracting control evidence from black-box AI models
  2. Mapping model risk to ISO 42001 Annex A controls
  3. Documenting training data provenance under A.7
  4. Validating transparency claims for third-party AI vendors
  5. Assessing fairness and bias controls in acquired systems
  6. Linking AI oversight mechanisms to governance clauses
  7. Handling undocumented model updates in due diligence
  8. Control sufficiency thresholds for deal-stage reviews
  9. Time-bound validation approaches under pressure
  10. Cross-referencing vendor SOC 2 reports with ISO 42001
  11. Dealing with incomplete AI development lifecycle records
  12. Using ISO 42001 to justify scope limitations
Module 3. Scoping AI Governance in High-Pressure Deals
Define precise, defensible boundaries for AI assessments without overextending team bandwidth.
12 chapters in this module
  1. Identifying material AI systems in short-window due diligence
  2. Applying risk-based scoping to AI governance assessments
  3. Exclusion justification under ISO 42001 A.12
  4. When to escalate AI concerns to legal or compliance
  5. Defining 'material impact' for AI-driven business processes
  6. Scoping generative AI exposure in content-heavy businesses
  7. Aligning with client risk appetite for AI legacy systems
  8. Handling AI embedded in ERP or CRM platforms
  9. Prioritizing cloud-hosted AI systems in assessments
  10. Leveraging prior audit findings to narrow scope
  11. Communicating scope decisions to deal partners
  12. Avoiding scope creep in fast-moving transactions
Module 4. Assessing Third-Party AI Vendor Risk
Evaluate AI dependencies introduced through vendors, SaaS platforms, and outsourced decisioning systems.
12 chapters in this module
  1. Reviewing vendor AI governance documentation for completeness
  2. Assessing compliance with ISO 42001 in third-party offerings
  3. Evaluating transparency of model updates and drift monitoring
  4. Contractual levers for AI control enforcement post-acquisition
  5. Analyzing vendor incident response plans for AI failures
  6. Validating claims of fairness and bias mitigation
  7. Assessing supply chain risks in AI model training data
  8. Reviewing vendor SOC 2 reports for AI-specific gaps
  9. Common red flags in AI vendor SIG responses
  10. Leveraging ISO 42001 clauses in vendor negotiation
  11. Handling black-box AI platforms with limited disclosure
  12. Post-close integration risks for AI-dependent platforms
Module 5. Documenting AI Risk Findings for Deal Committees
Transform technical findings into clear, actionable narratives for senior leadership and legal teams.
12 chapters in this module
  1. Structuring AI risk findings for executive reviewers
  2. Translating control failures into financial implications
  3. Avoiding technical jargon in deal committee memos
  4. Using ISO 42001 as a structured response framework
  5. Linking AI gaps to warranty or indemnity clauses
  6. Presenting risk severity with consistent scoring
  7. Distinguishing between policy and implementation gaps
  8. Documenting residual risk with clear ownership
  9. Aligning findings with client negotiation strategy
  10. Using standardized templates for faster reviews
  11. Integrating AI findings into overall due diligence report
  12. Common reviewer pushback and how to preempt it
Module 6. Integrating AI Governance with ESG and Sustainability Claims
Address the convergence of AI ethics, ESG reporting, and regulatory scrutiny in acquisitions.
12 chapters in this module
  1. Validating ESG-linked AI claims in marketing materials
  2. Assessing greenwashing risks in AI-driven sustainability claims
  3. Reviewing carbon footprint estimates for AI workloads
  4. Evaluating fairness in AI-based hiring or lending tools
  5. Linking AI governance to CSRD and SEC climate rules
  6. Handling social impact claims in AI systems
  7. Assessing bias in ESG scoring algorithms
  8. Reviewing third-party ESG certifications for AI reliance
  9. Documenting AI's role in net-zero commitments
  10. Dealing with unverified 'AI for good' narratives
  11. Connecting ISO 42001 to broader ESG control frameworks
  12. Positioning AI ethics as a valuation differentiator
Module 7. Regulator-Ready AI Audit Trails
Build defensible, inspection-ready documentation that survives regulatory scrutiny.
12 chapters in this module
  1. Establishing version control for AI model documentation
  2. Creating immutable logs for model decisioning
  3. Documenting training data lineage for audit
  4. Handling model drift detection in acquired systems
  5. Logging AI overrides and human-in-the-loop processes
  6. Preparing for potential CFPB or FTC inquiries
  7. Aligning with DORA and NIS2 AI-related expectations
  8. Using ISO 42001 as a foundation for regulator responses
  9. Common evidence requests from financial regulators
  10. Handling cross-border AI regulation conflicts
  11. Time-bound remediation planning for audit findings
  12. Building credibility through consistent evidence formatting
Module 8. Negotiating AI-Related Warranties and Indemnities
Turn governance findings into actionable contractual protections.
12 chapters in this module
  1. Drafting AI-specific representations and warranties
  2. Defining material breach for AI control failures
  3. Structuring indemnities for AI-related liabilities
  4. Setting clear thresholds for AI risk escalation
  5. Linking escrow terms to AI model documentation
  6. Negotiating AI audit rights post-acquisition
  7. Addressing model drift in warranty periods
  8. Handling third-party AI liability cascades
  9. Ensuring access to model source code and training data
  10. Defining 'best efforts' for AI maintenance commitments
  11. Using ISO 42001 as a benchmark for compliance
  12. Avoiding overly broad AI warranty language
Module 9. Post-Acquisition AI Integration Governance
Ensure acquired AI systems comply with buyer standards and don't introduce new risk.
12 chapters in this module
  1. Developing AI integration playbooks for Day 1
  2. Assessing compatibility with buyer's AI governance framework
  3. Establishing oversight for inherited AI models
  4. Planning for model retraining and validation cycles
  5. Aligning AI risk appetite across organizations
  6. Handling cultural resistance to AI controls
  7. Documenting AI system sunsetting decisions
  8. Integrating AI metrics into buyer's risk dashboards
  9. Updating incident response plans for new AI exposure
  10. Conducting post-merger AI control audits
  11. Establishing cross-functional AI stewardship
  12. Using ISO 42001 as a harmonization tool
Module 10. Scaling AI Governance Across Deal Pipelines
Create repeatable, firm-wide approaches to AI due diligence without sacrificing rigor.
12 chapters in this module
  1. Building standardized AI risk questionnaires
  2. Developing sector-specific AI control benchmarks
  3. Creating reusable control mapping templates
  4. Training junior staff on ISO 42001 fundamentals
  5. Establishing AI governance review checkpoints
  6. Integrating AI checks into deal lifecycle tools
  7. Sharing knowledge across deal teams securely
  8. Automating evidence collection where possible
  9. Developing playbooks for common AI risk patterns
  10. Using ISO 42001 as a common language across engagements
  11. Tracking AI findings across the portfolio
  12. Demonstrating ROI on AI governance efforts
Module 11. Communicating AI Risk to Non-Technical Stakeholders
Enable clear, credible dialogue between technical assessors and executive decision-makers.
12 chapters in this module
  1. Translating model risk into business impact terms
  2. Using analogies to explain AI complexity
  3. Creating visual summaries for AI risk exposure
  4. Anticipating pushback on AI findings
  5. Framing AI risk in valuation context
  6. Balancing certainty vs. uncertainty in reporting
  7. Handling 'AI washing' in seller presentations
  8. Building credibility through consistent delivery
  9. Using ISO 42001 to depersonalize risk discussions
  10. Aligning with legal and finance teams on messaging
  11. Managing expectations around AI assurance
  12. Turning technical findings into strategic opportunities
Module 12. Establishing Authority as the Go-To AI Governance Advisor
Position yourself as the recognized firm expert on AI governance in deals.
12 chapters in this module
  1. Developing a distinct point of view on AI risk
  2. Publishing internal guidance on AI due diligence
  3. Mentoring junior staff on AI control mapping
  4. Contributing to firm-wide AI governance standards
  5. Speaking at internal technical forums
  6. Building relationships with AI engineering leads
  7. Positioning ISO 42001 as a differentiator
  8. Creating signature client presentations
  9. Documenting successful case studies
  10. Generating demand through thought leadership
  11. Earning recognition from partners and clients
  12. Sustaining authority through continuous learning

How this maps to your situation

  • Deals due diligence
  • AI governance in M&A
  • Regulatory scrutiny
  • Reputation and authority

Before vs. after

Before
AI risk assessments that require extensive rework, lack standardized control mappings, and fail to gain recognition at senior levels.
After
Confident, repeatable AI governance assessments grounded in ISO 42001 that position the practitioner as the recognized authority within the firm.

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 week over 12 weeks, designed for completion alongside active deal cycles.

If nothing changes
Continuing without a recognized framework increases exposure to regulatory scrutiny, client disputes, and missed opportunities to lead in high-value AI governance work.

How this compares to the alternatives

Public AI governance courses lack deal-specific context. Internal training is often siloed. This course delivers partner-level, ISO 42001-aligned frameworks tailored to M&A realities.

Frequently asked

Is this course technical or strategic?
It's practitioner-focused: concrete control mapping, evidence gathering, and negotiation levers , not abstract theory.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-AI systems?
The ISO 42001 foundation is transferable, but content is optimized for AI in deals.
$199 one-time. Approximately 90 minutes per week over 12 weeks, designed for completion alongside active deal cycles..

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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