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Board-Level AI Vendor Risk Assessment for Senior Leaders

$199.00
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A tailored course, built for your situation

Board-Level AI Vendor Risk Assessment for Senior Leaders

Master the governance, due diligence, and strategic oversight skills needed to lead AI vendor decisions at the executive level.

$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 vendor decisions are accelerating, but without structured risk assessment, even high-potential partnerships can expose organizations to compliance, operational, and reputational risk.

The situation this course is for

Senior leaders are increasingly asked to approve AI vendor investments without clear frameworks to assess long-term risk. Traditional procurement checklists don’t address model drift, data provenance, or algorithmic accountability. This creates decision paralysis or overreliance on technical teams, slowing innovation and weakening governance.

Who this is for

Business and technology professionals in leadership, strategy, compliance, risk, or operations roles who influence or approve AI vendor engagements and want to lead with confidence.

Who this is not for

Individual contributors without decision-making authority, technical implementers focused on integration, or those seeking certification in data science or cybersecurity.

What you walk away with

  • Apply a board-ready framework for evaluating AI vendor risk across legal, ethical, and operational domains
  • Lead vendor due diligence with structured assessment templates and scoring models
  • Translate technical risk into strategic insights for executive and board discussions
  • Negotiate contracts with clarity on AI-specific clauses: model ownership, audit rights, and performance guarantees
  • Build an internal playbook for consistent, scalable AI vendor governance

The 12 modules (with all 144 chapters)

Module 1. The Strategic Role of AI Vendor Risk in Leadership
Establish the executive imperative for proactive AI vendor governance and its impact on innovation and trust.
12 chapters in this module
  1. Why AI vendor risk is a leadership priority
  2. From procurement to strategic oversight
  3. Aligning AI adoption with corporate governance
  4. The cost of reactive vendor management
  5. Emerging expectations from boards and regulators
  6. Case study: Scaling AI with disciplined vendor selection
  7. Defining risk tolerance at the executive level
  8. Mapping stakeholder concerns across functions
  9. The lifecycle of AI vendor engagement
  10. Balancing speed and diligence in vendor decisions
  11. Creating a risk-aware leadership culture
  12. Setting the tone from the top
Module 2. Foundations of AI-Specific Vendor Risk
Understand the unique risk dimensions introduced by AI systems that differ from traditional software.
12 chapters in this module
  1. What makes AI vendor risk different
  2. Model transparency and explainability expectations
  3. Data provenance and training data integrity
  4. Algorithmic bias and fairness considerations
  5. Model drift and performance decay
  6. Third-party dependency risks
  7. Vendor lock-in and exit strategies
  8. Monitoring and audit challenges
  9. Ethical alignment and brand risk
  10. Regulatory exposure in AI procurement
  11. Reputation risk from AI failures
  12. Long-term maintenance and support
Module 3. Governance Frameworks for AI Vendor Oversight
Adopt and adapt governance models to fit organizational maturity and risk appetite.
12 chapters in this module
  1. Principles of effective AI governance
  2. Designing a vendor risk committee structure
  3. Integrating AI risk into existing governance
  4. Risk tiers and vendor classification
  5. Policy development for AI procurement
  6. Roles and responsibilities across teams
  7. Escalation pathways for high-risk vendors
  8. Board reporting cadence and content
  9. Benchmarking against industry standards
  10. Continuous improvement in governance
  11. Stakeholder alignment strategies
  12. Documenting governance decisions
Module 4. Due Diligence Process Design
Build a repeatable, scalable process for evaluating AI vendors before engagement.
12 chapters in this module
  1. Stages of AI vendor due diligence
  2. Pre-RFP risk screening checklist
  3. Request for information (RFI) optimization
  4. Evaluating vendor documentation and claims
  5. Third-party audit and certification review
  6. Assessing vendor security and compliance posture
  7. Model validation and testing protocols
  8. Reference checks with peer organizations
  9. Onsite evaluation planning
  10. Scoring models for comparative analysis
  11. Red teaming AI vendor proposals
  12. Documenting due diligence findings
Module 5. Technical Risk Assessment for Non-Technologists
Gain clarity on technical risk factors without needing to code or build models.
12 chapters in this module
  1. Understanding model inputs and outputs
  2. Interpreting model performance metrics
  3. Assessing training data quality and representativeness
  4. Evaluating model robustness and edge cases
  5. Monitoring for bias and fairness
  6. Reviewing model documentation standards
  7. Understanding MLOps and model lifecycle
  8. Auditability and logging capabilities
  9. Explainability tools and techniques
  10. Model versioning and update processes
  11. Incident response planning for AI failures
  12. Translating technical findings into business risk
Module 6. Contractual Risk Mitigation
Master the key clauses and negotiation strategies for AI-specific vendor contracts.
12 chapters in this module
  1. AI-specific terms in vendor contracts
  2. Model ownership and intellectual property
  3. Data rights and usage limitations
  4. Performance guarantees and SLAs
  5. Audit rights and transparency obligations
  6. Liability for AI-generated harm
  7. Indemnification and insurance requirements
  8. Exit clauses and data portability
  9. Change management and version control
  10. Subcontractor and supply chain disclosures
  11. Dispute resolution for AI failures
  12. Renewal and termination rights
Module 7. Compliance and Regulatory Alignment
Ensure AI vendor practices meet evolving legal and regulatory expectations.
12 chapters in this module
  1. Global AI regulatory landscape overview
  2. GDPR and data protection implications
  3. Sector-specific rules (finance, healthcare, etc.)
  4. Algorithmic accountability requirements
  5. Transparency and disclosure obligations
  6. Recordkeeping and audit trail standards
  7. Bias and discrimination regulations
  8. Export controls and national security
  9. Vendor compliance certification review
  10. Preparing for regulatory inquiries
  11. Internal compliance monitoring
  12. Updating policies as regulations evolve
Module 8. Operational Risk and Integration Challenges
Anticipate and manage risks that emerge during AI vendor onboarding and operation.
12 chapters in this module
  1. Integration complexity with legacy systems
  2. Data pipeline reliability and monitoring
  3. Model performance in production
  4. Human-in-the-loop requirements
  5. Training and change management needs
  6. Vendor support responsiveness
  7. Incident reporting and resolution
  8. Monitoring for model drift
  9. Fallback and redundancy planning
  10. Scaling challenges with vendor solutions
  11. Cost overruns and usage-based pricing
  12. Performance benchmarking over time
Module 9. Ethical Risk and Organizational Values
Align AI vendor practices with company values and societal expectations.
12 chapters in this module
  1. Defining ethical AI principles
  2. Assessing vendor alignment with values
  3. Bias testing and mitigation strategies
  4. Fairness across demographic groups
  5. Environmental impact of AI models
  6. Labor practices in AI development
  7. Community impact and stakeholder trust
  8. Transparency with customers and public
  9. Whistleblower protections and reporting
  10. Ethics review board engagement
  11. Handling ethical dilemmas in vendor use
  12. Communicating ethical commitments
Module 10. Board Communication and Executive Reporting
Translate vendor risk assessments into clear, actionable insights for board and C-suite audiences.
12 chapters in this module
  1. What boards need to know about AI risk
  2. Creating risk dashboards for leadership
  3. Narrative framing for high-impact decisions
  4. Balancing opportunity and risk in presentations
  5. Using scenarios and stress tests
  6. Highlighting key decision points
  7. Reporting on vendor performance and issues
  8. Updating risk posture over time
  9. Preparing for board questions
  10. Aligning with strategic objectives
  11. Documenting board discussions
  12. Driving accountability from oversight
Module 11. Building a Scalable AI Vendor Governance Program
Design a repeatable, organization-wide approach to managing AI vendor risk.
12 chapters in this module
  1. Assessing organizational readiness
  2. Creating a center of excellence
  3. Standardizing evaluation templates
  4. Training cross-functional teams
  5. Automating risk assessment workflows
  6. Integrating with procurement systems
  7. Vendor risk as part of ESG reporting
  8. Continuous monitoring and alerts
  9. Benchmarking against peers
  10. Iterating on governance maturity
  11. Scaling across geographies
  12. Sustaining executive sponsorship
Module 12. Leading Through Uncertainty and Change
Develop the judgment and influence to lead AI vendor decisions in dynamic environments.
12 chapters in this module
  1. Navigating ambiguity in AI risk
  2. Making decisions with incomplete information
  3. Building consensus across stakeholders
  4. Communicating risk without stifling innovation
  5. Adapting to technological change
  6. Managing vendor transitions and exits
  7. Learning from near-misses and failures
  8. Promoting a culture of responsible AI
  9. Mentoring emerging leaders
  10. Staying current with AI developments
  11. Balancing short-term wins and long-term risk
  12. Leading with integrity and foresight

How this maps to your situation

  • Evaluating first enterprise AI vendor
  • Scaling AI across multiple business units
  • Responding to board questions on AI risk
  • Building internal governance capability

Before vs. after

Before
Uncertain about how to assess AI vendor risk beyond surface-level questions, relying on technical teams to interpret complex issues, and struggling to communicate risk clearly to executives and boards.
After
Equipped with a structured, board-ready framework to lead AI vendor assessments confidently, using proven templates and strategies to evaluate risk, negotiate contracts, and report with clarity.

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 4-6 hours per module, designed for busy professionals to complete at their own pace over 8-12 weeks.

If nothing changes
Without a disciplined approach, organizations may approve high-risk AI vendors, leading to compliance issues, operational failures, or reputational damage, all while missing the opportunity to build trusted, scalable AI partnerships.

How this compares to the alternatives

Unlike generic procurement courses or technical AI trainings, this program is specifically designed for senior leaders who need to assess risk at the strategic level, not implement models. It bridges governance, compliance, and business leadership with practical tools and real-world scenarios.

Frequently asked

Who is this course designed for?
Business and technology leaders responsible for approving or overseeing AI vendor engagements, including executives, risk officers, compliance leads, and senior strategists.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is issued after finishing all modules and passing the final assessment.
$199 one-time. Approximately 4-6 hours per module, designed for busy professionals to complete at their own pace over 8-12 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
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