A tailored course, built for your situation
Strategic AI Vendor Risk Assessment for Established Enterprises
Master enterprise-grade AI vendor governance with implementation-ready frameworks
$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.
Fragmented AI vendor assessments lead to misaligned expectations, compliance gaps, and operational friction in large organizations.
The situation this course is for
Teams struggle to evaluate AI vendors with consistency, often relying on outdated procurement checklists or overly technical reviews that miss strategic alignment. This results in delayed deployments, regulatory exposure, and mismatched capabilities.
Who this is for
Business and technology professionals in established enterprises responsible for AI governance, vendor due diligence, risk management, or technology procurement.
Who this is not for
Startups evaluating first-time AI tools, individual developers, or teams focused solely on open-source AI without vendor engagement.
What you walk away with
- Apply a structured framework to assess AI vendors across technical, legal, and operational dimensions
- Identify critical risk vectors in vendor contracts, model explainability, and data handling practices
- Align AI procurement with enterprise risk appetite and compliance requirements
- Lead cross-functional assessments with confidence using standardized templates
- Deploy and monitor AI vendors with long-term governance playbooks
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Establish core principles of AI risk in vendor contexts.
12 chapters in this module
- Defining AI vendor risk in enterprise settings
- Distinguishing AI from traditional software procurement
- Regulatory drivers shaping vendor assessment
- The role of internal audit and compliance
- Vendor lifecycle stages and risk touchpoints
- Enterprise risk appetite and AI adoption
- Mapping stakeholder responsibilities
- Common misconceptions about AI safety claims
- Building cross-functional assessment teams
- Benchmarking current assessment maturity
- Case study: Global bank’s AI due diligence
- Module 1 implementation checklist
Module 2. Due Diligence Frameworks
Implement structured due diligence processes.
12 chapters in this module
- Designing scalable vendor questionnaires
- Evaluating model development practices
- Assessing training data provenance
- Verifying claims of fairness and bias mitigation
- Reviewing third-party audits and certifications
- Onsite assessment protocols
- Remote evaluation techniques
- Third-party validation mechanisms
- Documenting due diligence findings
- Risk rating vendor proposals
- Integrating findings into procurement
- Module 2 implementation checklist
Module 3. Contract Architecture and SLAs
Structure contracts for AI-specific risks.
12 chapters in this module
- Key differences in AI vendor contracts
- Defining model performance metrics
- Establishing retraining obligations
- Specifying model drift detection thresholds
- Data ownership and usage rights
- Audit rights and access provisions
- Liability for erroneous outputs
- Exit strategies and model handover
- Subcontractor oversight clauses
- Insurance and indemnification needs
- Negotiation leverage points
- Module 3 implementation checklist
Module 4. Model Transparency and Explainability
Evaluate model interpretability claims.
12 chapters in this module
- Understanding model explainability techniques
- Assessing vendor-provided explanations
- Validating feature importance claims
- Testing for spurious correlations
- Handling black-box models responsibly
- Documentation standards for model cards
- System cards and transparency reports
- Right to explanation regulations
- Internal stakeholder communication
- Tools for ongoing model monitoring
- Vendor accountability for model changes
- Module 4 implementation checklist
Module 5. Data Governance and Privacy
Ensure compliance in data handling practices.
12 chapters in this module
- Mapping data flows in AI systems
- Assessing data minimization compliance
- Cross-border data transfer considerations
- Anonymization and pseudonymization efficacy
- Purpose limitation in model training
- Consent management integration
- Data subject rights fulfillment
- Vendor data breach response plans
- Logging and access controls
- Data lineage and traceability
- Third-party data sourcing risks
- Module 5 implementation checklist
Module 6. Operational Resilience
Evaluate system reliability and uptime.
12 chapters in this module
- Defining uptime for AI systems
- Monitoring inference pipeline health
- Failover and fallback mechanisms
- Incident response coordination
- Disaster recovery planning
- Capacity planning for scaling
- Dependency management
- Human-in-the-loop requirements
- Performance degradation thresholds
- Vendor communication protocols
- Redundancy and fallback models
- Module 6 implementation checklist
Module 7. Ethical Alignment and Bias
Assess ethical frameworks and bias controls.
12 chapters in this module
- Defining ethical AI principles
- Evaluating vendor ethics boards
- Bias testing methodologies
- Demographic parity assessment
- Fairness across use cases
- Bias mitigation techniques
- Ongoing monitoring for drift
- Stakeholder feedback mechanisms
- Ethical escalation paths
- Public trust considerations
- Reputational risk management
- Module 7 implementation checklist
Module 8. Regulatory Compliance Landscape
Navigate evolving AI regulations.
12 chapters in this module
- Global AI regulatory trends
- Sector-specific requirements
- Documentation for audit readiness
- Regulatory sandbox participation
- Proactive compliance strategies
- Engaging with regulators
- Anticipating future rule changes
- Cross-jurisdictional alignment
- Compliance automation tools
- Vendor responsibility mapping
- Reporting obligations
- Module 8 implementation checklist
Module 9. Third-Party Audit and Certification
Leverage external validation.
12 chapters in this module
- Types of AI audits available
- Selecting audit firms
- Preparing for certification
- SOC 2 for AI systems
- ISO standards applicability
- Algorithmic impact assessments
- Transparency report evaluation
- Vendor audit trail access
- Corrective action tracking
- Public disclosure strategies
- Audit frequency planning
- Module 9 implementation checklist
Module 10. Change Management and Integration
Plan for organizational adoption.
12 chapters in this module
- Stakeholder readiness assessment
- Training program design
- Process integration strategies
- Pilot program design
- Feedback loop implementation
- Scaling deployment
- Vendor support engagement
- Knowledge transfer planning
- User acceptance testing
- Post-launch review cycles
- Continuous improvement
- Module 10 implementation checklist
Module 11. Long-Term Monitoring
Establish ongoing oversight.
12 chapters in this module
- Model performance tracking
- Drift detection systems
- Retraining triggers
- Output quality assurance
- User feedback integration
- Compliance refresh cycles
- Vendor performance reviews
- Contractual milestone tracking
- Risk reassessment frequency
- Exit readiness monitoring
- Succession planning
- Module 11 implementation checklist
Module 12. Strategic Vendor Portfolio Management
Optimize multi-vendor AI ecosystems.
12 chapters in this module
- Vendor consolidation strategies
- Performance benchmarking
- Innovation tracking
- Relationship management models
- Exit and transition planning
- Multi-vendor integration risks
- Cost optimization techniques
- Innovation pipeline engagement
- Strategic partnership development
- Portfolio risk aggregation
- Future roadmap alignment
- Module 12 implementation checklist
How this maps to your situation
- Assessing AI vendors for financial services
- Evaluating AI in regulated healthcare environments
- Procuring AI for government-contracted operations
- Managing AI vendor portfolios in multinational corporations
Before vs. after
Before
Teams rely on fragmented, ad-hoc methods to evaluate AI vendors, leading to inconsistent assessments and compliance gaps.
After
Organizations deploy standardized, enterprise-grade evaluation frameworks that align AI procurement with strategic risk and governance objectives.
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, designed for busy professionals to complete at their own pace within 90 days.
If nothing changes
Continuing with inconsistent or outdated assessment methods increases exposure to regulatory penalties, operational failures, and reputational harm as AI adoption scales.
How this compares to the alternatives
Unlike generic procurement courses or academic AI ethics programs, this course delivers implementation-grade frameworks specifically for evaluating commercial AI vendors in complex enterprise environments.
Frequently asked
Who is this course designed for?
Business and technology professionals in established enterprises responsible for AI governance, vendor due diligence, risk management, or technology procurement.
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 3 hours per module, designed for busy professionals to complete at their own pace within 90 days..
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