A tailored course, built for your situation
Compliance-Ready AI Procurement Strategy for Risk-Adverse Boards
A 12-module implementation path for governance, risk, and technology leaders navigating board-level AI adoption
The situation this course is for
As AI tools move from pilot to production, procurement decisions are escalating to board-level discussions. Without a structured, compliance-aware strategy, leaders face pressure to act quickly while lacking frameworks to justify vendor choices, assess risk exposure, or demonstrate due diligence.
Who this is for
Governance, risk, compliance, and technology leaders responsible for guiding AI adoption in regulated or risk-sensitive environments.
Who this is not for
Individuals seeking introductory AI literacy or technical model development training.
What you walk away with
- Build a board-ready AI procurement framework aligned with compliance requirements
- Evaluate AI vendors using a risk-weighted, due-diligence checklist
- Design contracts with enforceable compliance and audit clauses
- Anticipate regulatory shifts using adaptive policy mapping techniques
- Lead cross-functional alignment between legal, security, and procurement teams
The 12 modules (with all 144 chapters)
- Defining AI in the context of procurement
- Mapping AI use cases to risk categories
- Compliance domains relevant to AI acquisition
- Lifecycle overview: from ideation to decommissioning
- Regulatory drivers shaping AI procurement
- Board expectations on AI oversight
- Internal policy alignment
- Stakeholder roles in procurement decisions
- Vendor ecosystem landscape
- Ethical principles in sourcing decisions
- Risk tolerance frameworks
- Procurement maturity assessment
- Global AI regulation trends
- Sector-specific compliance obligations
- Data protection and AI interaction
- Intellectual property considerations
- Liability frameworks for AI decisions
- Export control implications
- Accessibility standards for AI tools
- Industry-specific mandates
- Guidance from NIST, ISO, and OECD
- Internal policy inventory
- Gap analysis methodology
- Regulatory horizon scanning
- Risk categorization models
- High-risk AI use case identification
- Due diligence scoping principles
- Transparency requirements from vendors
- Model development lifecycle review
- Data provenance and bias mitigation
- Third-party audit readiness
- Security posture evaluation
- Incident response planning
- Change management processes
- Sub-processor oversight
- Exit strategy requirements
- Core clauses for AI contracts
- Performance benchmarking
- Accuracy and drift monitoring
- Data ownership and usage rights
- Model explainability commitments
- Audit rights and access protocols
- Liability caps and indemnities
- Termination triggers
- Service level agreement design
- Penalty structures for non-compliance
- Renewal and renegotiation terms
- Confidentiality in AI systems
- Stakeholder mapping
- Governance committee design
- Procurement workflow integration
- Legal review integration
- Security assessment coordination
- Compliance monitoring handoffs
- Training and awareness planning
- Escalation protocols
- Feedback loops across teams
- Decision rights framework
- Change control integration
- Board reporting templates
- Policy scoping and audience definition
- Approval workflows
- Exemption request processes
- Use case pre-clearance
- Vendor pre-qualification
- Internal audit alignment
- Training requirements
- Documentation standards
- Policy version control
- Integration with enterprise risk management
- Policy enforcement mechanisms
- Review and update cycles
- AI inventory tracking
- Automated policy checks
- Risk scoring dashboards
- Contract clause monitoring
- Audit trail generation
- Integration with GRC platforms
- Alerting for non-compliant usage
- Vendor performance tracking
- Model lifecycle monitoring
- License compliance tools
- Data flow mapping
- AI asset registry design
- Ethical AI principles
- Bias and fairness evaluation
- Transparency expectations
- Human oversight requirements
- Stakeholder impact assessments
- Community engagement strategies
- Fair labor practices in AI supply chain
- Environmental considerations
- Dual-use risk assessment
- Whistleblower protections
- Ethics review board integration
- Public trust metrics
- Board reporting structure
- Risk dashboard design
- Procurement decision narratives
- AI investment justification
- Incident disclosure protocols
- Regulatory readiness updates
- Vendor performance summaries
- Audit findings communication
- Strategic roadmap alignment
- Crisis communication planning
- Board education strategy
- Escalation thresholds
- Jurisdictional compliance conflicts
- Data transfer mechanisms
- Language and localization needs
- Cultural context in AI outputs
- Local legal counsel integration
- Vendor location risk
- Time zone and support expectations
- Currency and payment terms
- Trade regulation compliance
- Sanctions screening
- Local representation requirements
- Global policy harmonization
- Centralized vs decentralized models
- Center of excellence design
- Procurement enablement teams
- Standardized templates rollout
- Training at scale
- Feedback integration
- Continuous improvement cycles
- Lessons learned capture
- Maturity model progression
- Benchmarking against peers
- Resource planning
- Budgeting for compliance
- Regulatory change monitoring
- Technology shift anticipation
- Scenario planning for AI evolution
- Policy adaptability design
- Vendor innovation tracking
- Internal innovation feedback
- Exit and migration planning
- Contract flexibility clauses
- AI sunset strategies
- Lessons from enforcement actions
- Horizon scanning methods
- Strategic renewal planning
How this maps to your situation
- Preparing for first AI procurement review
- Responding to board questions on AI risk
- Building internal AI governance policy
- Scaling AI adoption across departments
Before vs. after
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.
How this compares to the alternatives
Unlike generic AI awareness courses or technical bootcamps, this program focuses exclusively on procurement strategy, compliance alignment, and board-level communication for risk-adverse environments.
Frequently asked
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.