A tailored course, built for your situation
Modern AI Vendor Risk Assessment for Senior Leaders
Master governance, compliance, and strategic oversight in AI procurement and deployment
$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.
Navigating AI vendor promises without a clear assessment framework leads to misaligned investments and hidden compliance gaps.
The situation this course is for
Senior leaders are increasingly expected to approve or guide AI vendor decisions, yet lack structured, practical frameworks to assess risk, compliance, and long-term value. Without a consistent methodology, organizations default to technical teams or sales narratives, leading to costly mismatches between promise and performance.
Who this is for
Senior business and technology leaders responsible for AI strategy, procurement, compliance, or governance, particularly in regulated or scaling environments.
Who this is not for
Individual contributors focused solely on coding, data science, or IT support without decision-making authority over vendor selection or governance policy.
What you walk away with
- Apply a repeatable framework to assess AI vendor risk across legal, ethical, and operational dimensions
- Map vendor capabilities to organizational compliance requirements including data privacy and algorithmic accountability
- Evaluate AI contracts and SLAs with confidence using proven checklists and red-flag indicators
- Establish board-ready reporting structures for AI vendor performance and risk exposure
- Lead cross-functional discussions with legal, security, and operations using a common governance language
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Governance
Establish core principles for overseeing AI vendor relationships with strategic alignment.
12 chapters in this module
- Defining AI vendor risk in context
- Evolution of vendor governance models
- Key differences between traditional and AI procurement
- Governance vs. management: clarifying roles
- The role of leadership in setting tone
- Regulatory drivers shaping vendor expectations
- Common misconceptions about AI accountability
- Stakeholder mapping for vendor oversight
- Integrating ESG considerations
- Building cross-functional alignment
- Vendor lifecycle overview
- From pilot to enterprise scaling
Module 2. Strategic Alignment and Use Case Validation
Ensure AI solutions support real business outcomes, not just technological novelty.
12 chapters in this module
- Identifying high-impact AI opportunities
- Validating problem-solution fit
- Assessing vendor claims vs. reality
- Use case prioritization frameworks
- Measuring alignment with strategic goals
- Avoiding solutioneering traps
- Benchmarking against industry peers
- Defining success criteria early
- Risk of misaligned use cases
- Stakeholder expectation management
- Translating business needs to technical specs
- Creating vendor-agnostic evaluation criteria
Module 3. Compliance and Regulatory Mapping
Navigate evolving requirements across jurisdictions and sectors.
12 chapters in this module
- Global regulatory landscape overview
- Data privacy obligations in AI systems
- Algorithmic transparency requirements
- Sector-specific rules (education, finance, health)
- Cross-border data flow implications
- Recordkeeping and audit readiness
- Children's data and educational AI
- Accessibility and equity standards
- Vendor documentation expectations
- Third-party certification value
- Preparing for regulatory scrutiny
- Future-proofing compliance approaches
Module 4. Data Integrity and Security Due Diligence
Evaluate how vendors handle data sourcing, storage, and protection.
12 chapters in this module
- Data provenance and lineage tracking
- Vendor data handling policies
- Encryption standards in transit and at rest
- Access control and identity management
- Incident response planning
- Penetration testing expectations
- Data minimization practices
- Anonymization and de-identification rigor
- Cloud infrastructure security
- Shared responsibility models
- Data sovereignty considerations
- Audit rights and verification access
Module 5. Model Transparency and Explainability
Understand how AI decisions are made and when to demand clarity.
12 chapters in this module
- Levels of model interpretability
- Black box vs. explainable AI trade-offs
- Right to explanation frameworks
- Performance monitoring for drift
- Bias detection and mitigation
- Documentation completeness checks
- Vendor explainability claims validation
- Human-in-the-loop requirements
- Model card and datasheet review
- Third-party validation options
- Error analysis and edge cases
- Confidence scoring reliability
Module 6. Contractual Safeguards and SLA Evaluation
Transform vague promises into enforceable commitments.
12 chapters in this module
- Key clauses in AI vendor contracts
- Service level agreement interpretation
- Uptime and performance guarantees
- Penalty enforcement mechanisms
- IP ownership and licensing terms
- Data rights and portability
- Termination and exit planning
- Liability caps and indemnification
- Audit rights and reporting access
- Subprocessor disclosures
- Renewal and pricing lock-in risks
- Force majeure and continuity planning
Module 7. Ethical AI and Social Impact Assessment
Proactively address fairness, equity, and societal implications.
12 chapters in this module
- Defining ethical boundaries for AI use
- Stakeholder impact analysis
- Bias testing across demographics
- Community engagement strategies
- Whistleblower and feedback channels
- AI for social good frameworks
- Reputation risk monitoring
- Handling controversial applications
- Vendor ethics board review
- Transparency in marketing claims
- Long-term societal implications
- Balancing innovation and responsibility
Module 8. Performance Monitoring and Continuous Oversight
Move beyond initial approval to sustained governance.
12 chapters in this module
- Establishing KPIs for AI vendors
- Ongoing performance dashboards
- Model drift detection protocols
- User feedback integration
- Regular review cadence design
- Escalation pathways for issues
- Third-party benchmarking
- Cost-benefit reassessment
- Vendor maturity progression
- Scaling and capacity planning
- Renewal readiness assessment
- Lessons learned documentation
Module 9. Incident Response and Contingency Planning
Prepare for what happens when things go wrong.
12 chapters in this module
- AI failure mode identification
- Breach notification timelines
- Vendor responsibility during incidents
- Crisis communication protocols
- Legal and regulatory reporting duties
- Third-party investigations
- Reputation damage control
- System rollback procedures
- Alternate solution readiness
- Insurance and liability coverage
- Post-mortem analysis framework
- Regulatory cooperation strategies
Module 10. Board and Executive Communication
Translate technical risk into strategic insight.
12 chapters in this module
- Building board-level dashboards
- Risk appetite framing
- Incident reporting templates
- Budget justification narratives
- Strategic opportunity articulation
- Balancing innovation and caution
- Vendor portfolio overview
- Long-term AI roadmap alignment
- Regulatory outlook briefings
- Stakeholder trust metrics
- Benchmarking against peers
- Success story development
Module 11. Cross-Functional Collaboration Models
Align legal, security, procurement, and business units.
12 chapters in this module
- Stakeholder role definition
- Decision rights clarification
- Governance committee design
- Procurement integration
- Legal review workflows
- Security team coordination
- HR and workforce impact
- Finance and budget alignment
- Operations and support planning
- Training and change management
- Feedback loop integration
- Conflict resolution protocols
Module 12. Future-Proofing and Adaptive Governance
Build systems that evolve with technology and regulation.
12 chapters in this module
- Monitoring emerging AI trends
- Regulatory horizon scanning
- Technology substitution planning
- Vendor diversification strategies
- Internal capability development
- Adaptive policy frameworks
- Scenario planning exercises
- Investment in internal audits
- Knowledge transfer mechanisms
- Succession planning for oversight
- Building organizational resilience
- Leading governance innovation
How this maps to your situation
- Assessing a new AI vendor proposal
- Reviewing an existing AI contract renewal
- Responding to regulatory inquiry about AI use
- Designing internal AI governance policy
Before vs. after
Before
Uncertain about how to evaluate AI vendor claims or assess long-term risk exposure.
After
Confidently lead AI vendor assessments with a structured, repeatable framework aligned to compliance, performance, and strategic goals.
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 8, 10 hours total, designed for completion in focused sessions across two weeks.
If nothing changes
Organizations that lack formal AI vendor assessment practices risk compliance gaps, reputational harm, and suboptimal investments that fail to deliver promised value.
How this compares to the alternatives
Unlike generic AI ethics guides or technical deep dives, this course is tailored for senior leaders who must make binding decisions about AI vendors, offering actionable governance frameworks, not just theory or code.
Frequently asked
Who is this course designed for?
Senior leaders in business and technology roles who oversee AI strategy, procurement, compliance, or governance, especially in regulated or scaling environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a digital certificate is awarded upon finishing all modules and assessments.
$199 one-time. Approximately 8, 10 hours total, designed for completion in focused sessions across two 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