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Mid-Market AI Vendor Risk Assessment for Senior Leaders

$199.00
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A tailored course, built for your situation

Mid-Market AI Vendor Risk Assessment for Senior Leaders

A 12-module implementation-grade course for business and technology leaders navigating AI procurement with confidence

$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 adoption is accelerating, but vendor risk frameworks haven't kept pace, especially in mid-market organizations where resources are constrained and stakes are high.

The situation this course is for

Senior leaders are expected to sign off on AI tools without standardized ways to assess model integrity, data provenance, or long-term compliance. Generic checklists fail under real-world pressure. The result? Delayed decisions, escalated exposure, and eroded trust at the executive level.

Who this is for

Business and technology leaders in mid-market organizations responsible for AI procurement, risk oversight, or technology governance, especially those balancing innovation velocity with compliance, security, and operational resilience.

Who this is not for

This is not for technical AI researchers, entry-level compliance staff, or vendors selling AI tools. It's designed for decision-makers who must evaluate risk, not build models.

What you walk away with

  • Apply a structured framework to evaluate AI vendor transparency and model accountability
  • Negotiate contracts with enforceable performance, audit, and exit clauses
  • Align AI procurement with evolving regulatory expectations (e.g., NIST, EU AI Act, state-level rules)
  • Build internal consensus using risk assessment templates tailored to mid-market constraints
  • Lead AI adoption initiatives with documented due diligence and board-ready reporting

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in the Mid-Market
Establish core definitions, threat categories, and market-specific challenges shaping AI risk today.
12 chapters in this module
  1. Defining AI vendor risk in non-enterprise contexts
  2. Key differences: mid-market vs. enterprise risk tolerance
  3. Emerging regulatory touchpoints for AI procurement
  4. The role of leadership in setting risk appetite
  5. Balancing innovation speed with due diligence
  6. Common failure points in early-stage AI adoption
  7. Stakeholder mapping: who needs to be involved
  8. Case study: AI rollout in a 500-person organization
  9. Risk taxonomy for AI vendors: model, data, operations
  10. Benchmarking current internal capabilities
  11. Setting success metrics for risk assessment
  12. Preparing for cross-functional alignment
Module 2. Regulatory and Compliance Landscape
Navigate current expectations from NIST, FTC, state laws, and sector-specific guidance.
12 chapters in this module
  1. Overview of NIST AI Risk Management Framework
  2. FTC enforcement trends in AI claims and bias
  3. State-level AI regulations: what applies to mid-market
  4. Sector-specific rules: finance, insurance, HR tech
  5. Mapping vendor offerings to compliance requirements
  6. Handling algorithmic bias disclosures
  7. Data privacy implications of third-party AI
  8. Recordkeeping and audit readiness
  9. Vendor self-assessments: what to trust and verify
  10. Preparing for regulatory inquiries
  11. Compliance as a competitive advantage
  12. Integrating compliance into procurement workflows
Module 3. Technical Due Diligence Without a PhD
Evaluate model quality, data sources, and system design using non-technical assessment tools.
12 chapters in this module
  1. Understanding model types and their risk profiles
  2. Key questions to ask about training data
  3. Assessing model drift and degradation risks
  4. Evaluating explainability and interpretability claims
  5. Third-party audits: what they do and don’t cover
  6. Red teaming basics for leadership review
  7. API security and integration risks
  8. Vendor uptime, SLAs, and incident response
  9. Model versioning and update transparency
  10. Using checklists to standardize technical reviews
  11. Working effectively with data science teams
  12. Translating technical findings for executives
Module 4. Contractual Risk Mitigation
Structure agreements that protect your organization before deployment.
12 chapters in this module
  1. Critical clauses for AI vendor contracts
  2. Performance guarantees and measurable outcomes
  3. Audit rights and access to model documentation
  4. Data ownership and usage restrictions
  5. Subprocessor transparency and control
  6. Incident notification timelines
  7. Liability caps and indemnification clauses
  8. Exit strategies and data portability
  9. Model retraining and version change protocols
  10. Dispute resolution mechanisms
  11. Renewal and termination triggers
  12. Template language for legal teams
Module 5. Operational Resilience and Monitoring
Ensure ongoing oversight once AI tools are live.
12 chapters in this module
  1. Setting up continuous monitoring protocols
  2. Defining thresholds for performance degradation
  3. Human-in-the-loop requirements
  4. Alerting and escalation paths
  5. Maintaining manual override capabilities
  6. Tracking model behavior over time
  7. Vendor communication cadence expectations
  8. Handling model updates and patches
  9. Incident response planning for AI failures
  10. Documentation standards for ongoing compliance
  11. Internal reporting rhythms for AI systems
  12. Preparing for model retirement
Module 6. Ethical and Reputational Risk
Anticipate and manage public, employee, and customer concerns about AI use.
12 chapters in this module
  1. Identifying high-reputation-risk AI use cases
  2. Stakeholder perception mapping
  3. Bias and fairness: practical assessment tools
  4. Transparency with customers and employees
  5. Public communication strategies for AI adoption
  6. Handling media inquiries about AI decisions
  7. Internal ethics review processes
  8. Whistleblower protections and reporting
  9. Social impact assessments for AI tools
  10. Vendor ethics commitments: how to verify
  11. Reputation recovery after AI incidents
  12. Building trust through responsible AI branding
Module 7. Financial and Business Model Risk
Assess vendor stability, pricing models, and long-term viability.
12 chapters in this module
  1. Evaluating vendor financial health
  2. Revenue concentration and dependency risks
  3. Pricing model traps: usage-based, per-seat, hidden fees
  4. Long-term cost forecasting for AI tools
  5. Vendor lock-in and switching costs
  6. Intellectual property ownership questions
  7. Insurance coverage for AI-related incidents
  8. M&A risk: what if your vendor gets acquired?
  9. Customer support responsiveness trends
  10. Roadmap alignment with your strategic goals
  11. Reference checks: what to ask existing clients
  12. Building financial risk into procurement scores
Module 8. Cross-Functional Alignment
Engage legal, IT, compliance, and business units in unified risk assessment.
12 chapters in this module
  1. Creating a cross-functional AI governance team
  2. Defining roles: who owns what in vendor assessment
  3. Aligning risk language across departments
  4. Facilitating joint decision-making sessions
  5. Building consensus on risk appetite
  6. Managing conflicting priorities across units
  7. Executive sponsorship and escalation paths
  8. Documenting decisions for audit purposes
  9. Onboarding new team members to AI risk standards
  10. Running effective vendor review meetings
  11. Using shared dashboards for visibility
  12. Maintaining alignment post-deployment
Module 9. Risk Communication for Leadership
Present AI vendor risks clearly to boards, executives, and auditors.
12 chapters in this module
  1. Translating technical risk into business terms
  2. Designing executive summaries for AI reviews
  3. Visualizing risk exposure for non-experts
  4. Anticipating board-level questions
  5. Reporting frequency and format standards
  6. Preparing for audit committee reviews
  7. Benchmarking against peer organizations
  8. Highlighting risk reduction achievements
  9. Balancing optimism with caution in messaging
  10. Creating board-ready risk assessment packages
  11. Using narratives to explain complex trade-offs
  12. Managing upward expectations on AI benefits
Module 10. Implementation Playbook Integration
Apply course frameworks using the hand-built implementation playbook.
12 chapters in this module
  1. How to use the implementation playbook
  2. Customizing templates for your organization
  3. Setting up a 90-day rollout plan
  4. Identifying quick wins in vendor assessment
  5. Phasing risk improvements over time
  6. Resource allocation for AI governance
  7. Tracking progress with KPIs
  8. Adapting playbooks for different vendor types
  9. Integrating with existing risk management systems
  10. Securing leadership buy-in for changes
  11. Running pilot assessments
  12. Scaling lessons across the enterprise
Module 11. Future-Proofing Your Approach
Stay ahead of evolving threats, regulations, and technologies.
12 chapters in this module
  1. Tracking emerging AI risk trends
  2. Subscribing to regulatory and industry updates
  3. Benchmarking against evolving best practices
  4. Updating internal policies regularly
  5. Reassessing vendors on a recurring schedule
  6. Preparing for generative AI-specific risks
  7. Adapting to new model types and capabilities
  8. Building a learning culture around AI risk
  9. Engaging with peer networks and forums
  10. Investing in team development and training
  11. Anticipating shifts in customer expectations
  12. Positioning your organization as a leader in responsible AI
Module 12. Capstone: Real-World Application
Complete a full AI vendor risk assessment using course tools and templates.
12 chapters in this module
  1. Selecting a real or hypothetical vendor for review
  2. Conducting a full due diligence exercise
  3. Applying technical, legal, and operational checklists
  4. Drafting a risk rating and recommendation
  5. Creating a presentation for leadership
  6. Simulating a board Q&A session
  7. Incorporating peer feedback
  8. Finalizing documentation for audit readiness
  9. Identifying next steps for improvement
  10. Reflecting on personal and organizational growth
  11. Sharing insights with your team
  12. Planning ongoing risk assessment cycles

How this maps to your situation

  • Evaluating first AI vendor for enterprise use
  • Scaling AI adoption across multiple departments
  • Responding to increased regulatory scrutiny
  • Building internal governance capacity ahead of audit

Before vs. after

Before
Uncertain about how to assess AI vendors beyond surface-level promises, relying on fragmented processes, and facing pressure to act quickly without clear frameworks.
After
Equipped with a repeatable, board-ready process to evaluate AI vendors with confidence, align stakeholders, and demonstrate rigorous due diligence.

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 6, 8 hours per module, designed for flexible, self-paced learning around executive schedules.

If nothing changes
Without a structured approach, organizations risk adopting AI tools that create compliance gaps, operational fragility, or reputational harm, especially as scrutiny intensifies and missteps become more visible.

How this compares to the alternatives

Unlike generic compliance courses or academic AI ethics programs, this course delivers implementation-grade tools specifically for mid-market leaders who must make real procurement decisions with limited resources.

Frequently asked

Is this course technical?
It’s designed for leaders who don’t need to code but must understand and assess technical risk. Concepts are explained clearly with practical tools.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I share this with my team?
Each enrollment is for individual use, but team licensing is available upon request.
$199 one-time. Approximately 6, 8 hours per module, designed for flexible, self-paced learning around executive schedules..

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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