A tailored course, built for your situation
Practical AI Procurement Strategy for Senior Leaders
A structured approach to responsible, effective AI acquisition in enterprise settings
$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.
Leaders face pressure to adopt AI quickly but lack structured methods to assess risk, value, and vendor integrity.
The situation this course is for
AI procurement is no longer just an IT decision, it involves legal, financial, operational, and reputational stakes. Without a clear strategy, organizations risk costly misalignment, compliance exposure, and failed deployments. Leaders need a repeatable, cross-functional framework to evaluate AI solutions with confidence.
Who this is for
Senior business and technology leaders responsible for strategic technology adoption, vendor governance, and enterprise risk management.
Who this is not for
Individual contributors without budget or procurement authority, or those seeking technical AI development skills rather than strategic oversight.
What you walk away with
- Apply a structured framework to evaluate AI vendor claims and capabilities
- Integrate compliance and risk requirements into procurement workflows
- Lead cross-functional AI acquisition decisions with confidence
- Avoid common pitfalls in AI pilot scaling and contract negotiation
- Build organizational capacity for repeatable, responsible AI adoption
The 12 modules (with all 144 chapters)
Module 1. The Strategic Shift in AI Procurement
Understanding the evolving role of leadership in AI acquisition decisions.
12 chapters in this module
- From IT purchase to executive priority
- Market drivers shaping AI procurement
- Defining 'responsible' in AI sourcing
- Stakeholder mapping for AI decisions
- Governance maturity models
- Risk classes in AI vendor selection
- Benchmarking organizational readiness
- Aligning AI with enterprise strategy
- Ethical procurement principles
- Regulatory anticipation frameworks
- Vendor ecosystem landscape
- Procurement lifecycle transformation
Module 2. AI Procurement Governance Foundations
Establishing governance structures for AI acquisition.
12 chapters in this module
- Board-level engagement strategies
- Cross-functional procurement teams
- Risk ownership models
- AI-specific due diligence
- Vendor evaluation scorecards
- Compliance integration frameworks
- Audit trail design
- Ethics review processes
- Third-party oversight models
- Procurement policy modernization
- Stakeholder communication plans
- Decision rights frameworks
Module 3. Vendor Landscape and Market Intelligence
Navigating the fragmented AI vendor ecosystem.
12 chapters in this module
- Classifying AI vendors by capability
- Market consolidation trends
- Evaluating technical claims
- Reference validation techniques
- Financial health screening
- Partnership model analysis
- Geopolitical risk in sourcing
- Open vs. proprietary trade-offs
- Reseller and integrator roles
- Vendor roadmap assessment
- Market intelligence tools
- Competitive positioning analysis
Module 4. AI Use Case Prioritization
Identifying high-impact, low-risk AI procurement opportunities.
12 chapters in this module
- Operational pain point alignment
- Value potential scoring
- Implementation complexity mapping
- Data readiness assessment
- Stakeholder buy-in pathways
- Pilot scope definition
- ROI modeling frameworks
- Change management requirements
- Integration effort estimation
- Regulatory alignment checks
- Scalability filters
- Exit strategy considerations
Module 5. Requirements Engineering for AI
Translating business needs into technical procurement criteria.
12 chapters in this module
- Functional requirement patterns
- Non-functional specification design
- Performance benchmarking standards
- Data interface requirements
- Model transparency expectations
- Explainability thresholds
- Bias detection protocols
- Security control mapping
- Privacy by design integration
- Accessibility standards
- Localization needs
- Support and maintenance SLAs
Module 6. Request for Proposal Design
Crafting RFPs that elicit meaningful AI vendor responses.
12 chapters in this module
- RFP structure for AI solutions
- Vendor qualification filters
- Technical demonstration design
- Pilot project scoping
- Evaluation criteria weighting
- Compliance checklist integration
- Ethics disclosure requirements
- Pricing model analysis
- Contract flexibility clauses
- Intellectual property terms
- Data ownership language
- Termination condition design
Module 7. AI Vendor Evaluation Frameworks
Systematic assessment of AI vendor proposals.
12 chapters in this module
- Proposal scoring methodologies
- Technical feasibility validation
- Reference call frameworks
- Proof of concept design
- Model performance verification
- Data governance assessment
- Security audit preparation
- Compliance gap analysis
- Team capability review
- Cultural fit evaluation
- Implementation timeline realism
- Support model adequacy
Module 8. Contract Negotiation and Risk Allocation
Structuring agreements that protect organizational interests.
12 chapters in this module
- Liability allocation frameworks
- Indemnification clauses for AI
- Warranty and guarantee design
- Performance penalty structures
- Data breach response terms
- Model drift monitoring obligations
- Audit rights specification
- Subcontractor oversight
- IP ownership frameworks
- Derivative work rights
- Renewal and exit terms
- Dispute resolution mechanisms
Module 9. Pilot and Proof of Concept Management
Designing and executing AI validation projects.
12 chapters in this module
- Pilot success criteria definition
- Data environment setup
- Stakeholder feedback loops
- Performance metric selection
- Bias testing protocols
- Integration testing frameworks
- User acceptance criteria
- Security validation steps
- Compliance verification
- Cost tracking methods
- Vendor support evaluation
- Go/no-go decision frameworks
Module 10. Scaling AI Procurement Decisions
Transitioning from pilot to enterprise deployment.
12 chapters in this module
- Architecture alignment checks
- Data pipeline readiness
- Team scaling requirements
- Change management scaling
- Vendor support capacity
- Cost model evolution
- Performance monitoring design
- Model version governance
- User training scalability
- Compliance audit readiness
- Incident response planning
- Vendor lock-in mitigation
Module 11. AI Procurement in Regulated Environments
Adapting frameworks for high-compliance sectors.
12 chapters in this module
- Regulatory mapping techniques
- Audit trail requirements
- Data sovereignty rules
- Industry-specific constraints
- Third-party risk frameworks
- Oversight committee design
- Compliance documentation
- Reporting structure integration
- Regulator engagement strategies
- Policy exception management
- Certification alignment
- Cross-border data flow rules
Module 12. Building Organizational AI Procurement Capacity
Creating lasting capability beyond individual projects.
12 chapters in this module
- Center of excellence models
- Procurement playbook development
- Knowledge transfer frameworks
- Vendor relationship management
- Lessons learned integration
- Capability maturity tracking
- Training program design
- Cross-functional collaboration
- Procurement tooling selection
- Market intelligence updating
- Benchmarking participation
- Leadership development pathways
How this maps to your situation
- Evaluating first AI vendor proposal
- Designing AI procurement policy
- Scaling pilot to production
- Responding to board inquiry on AI risk
Before vs. after
Before
Uncertain how to assess AI vendor claims, manage risk, or align procurement with strategic goals.
After
Confidently lead AI acquisition decisions with structured frameworks, stakeholder alignment, and risk-aware governance.
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 3-4 hours per module, designed for busy leaders to complete at their own pace over 8-12 weeks.
If nothing changes
Organizations without structured AI procurement risk costly missteps, compliance exposure, and failed deployments that erode trust and delay transformation.
How this compares to the alternatives
Unlike generic AI awareness courses or technical developer programs, this offering focuses specifically on procurement decision-making for senior leaders, combining governance, vendor assessment, and implementation planning in one structured curriculum.
Frequently asked
Who is this course designed for?
Senior business and technology leaders responsible for strategic technology adoption, vendor governance, and enterprise risk management.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is technical AI expertise required?
No. The course is designed for decision-makers and assumes no prior AI development experience.
$199 one-time. Approximately 3-4 hours per module, designed for busy leaders 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