AI
Procuring and Managing AI Solutions
Artificial Intelligence (AI) is revolutionizing industries and government operations. However, procuring AI solutions for the federal government presents unique challenges, including navigating federal regulations, assessing vendor capabilities, and ensuring responsible deployment. This intensive, hands-on course provides federal procurement professionals, IT project managers, and contract managers with a comprehensive framework for acquiring, evaluating, and managing AI systems within the boundaries of U.S. government policies and compliance requirements.
The course draws from best practices in public procurement. Participants will leave with actionable skills to draft AI-specific solicitations, evaluate vendor proposals, and oversee post-award contract performance.
Hands-on Activities
- Federal Solicitation Analysis: Review real AI-related solicitations and contract awards to identify best practices and potential pitfalls.
- Drafting RFP Sections: Write sample RFP sections, including SOW and evaluation criteria, for an AI-based project.
- Vendor Evaluation Role-Play: Simulate a vendor evaluation and negotiation scenario, practicing scoring, negotiation techniques, and consensus building.
- Compliance Checklist Exercise: Develop a compliance checklist for a hypothetical AI procurement project.
Competencies
- Federal procurement processes.
- RFI and RFP preparation for emerging technologies.
- Vendor and proposal evaluation.
- Contract types and vehicles for federal acquisitions.
- Federal compliance standards for technology procurements.
- Risk management in AI systems acquisition.
Duration
2 days
Prerequisites
Recommended Pre-Course Readings:
1. “Federal Acquisition Regulation (FAR): Key Clauses for AI” (selected excerpts).
2. NIST AI Risk Management Framework.
3. “Guidelines for Responsible AI” by the U.S. Department of Defense.
Upon completing this course, participants will:
1. Develop AI project requirements and write precise RFIs and RFPs tailored to AI solutions.
2. Identify appropriate federal contract vehicles (e.g., GSA schedules, BPAs, GWACs) and understand their strategic use for AI procurements.
3. Understand budgeting and cost evaluation models for AI solutions, including Total Cost of Ownership (TCO) and performance-based pricing structures.
4. Navigate federal compliance standards, including FISMA, FedRAMP, FOIA, and Section 508, as they apply to AI.
5. Apply frameworks such as the NIST AI Risk Management Framework and Responsible AI Best Practices in procurement and project management.
6. Evaluate vendor proposals using metrics like algorithm transparency, data security, and bias mitigation.
7. Design monitoring and management plans to ensure vendor accountability and system performance after acquisition.
8. Integrate strategies to achieve small business contracting quotas while ensuring vendor capability.
Post-Course Certification
Participants will receive a certificate of completion and earn continuing education credits applicable to certifications like the Federal Acquisition Certification in Contracting (FAC-C) or Certified Professional Contracts Manager (CPCM).
Day 1: Foundations of AI Procurement
Morning Session
Module 1: Introduction to AI Procurement
• Overview of AI applications in government (e.g., healthcare, cybersecurity, transportation).
• Challenges unique to procuring AI: algorithm transparency, data dependency, and ethical concerns.
• Case studies of federal AI procurements: successes and lessons learned.
Module 2: Developing Requirements for AI Projects
• Techniques for defining AI-specific requirements.
• Key questions to address in RFIs and RFPs:
• What data will the solution require, and who owns it?
• How will the solution integrate with existing systems?
• What metrics will measure success?
• Writing clear and concise statements of work (SOW) and performance work statements (PWS).
• Real-world examples of AI-focused RFIs and RFPs.
Afternoon Session
Module 3: Federal Contract Vehicles and Types
• Deep dive into federal contract vehicles:
• General Services Administration (GSA) schedules.
• Blanket Purchase Agreements (BPAs).
• Government-Wide Acquisition Contracts (GWACs).
• Multi-award and sole-source contracts.
• Selecting the best contract vehicle for AI procurement.
• Overview of cooperative purchasing agreements for multi-agency use.
Module 4: Budgeting and Pricing AI Projects
• Budget formulation for AI solutions.
• Exploring pricing models:
• Fixed-price vs. cost-plus.
• Subscription-based pricing.
• Outcome-based contracting.
• Cost considerations:
• Licensing fees.
• Cloud storage and compute costs.
• Data labeling and training costs.
• Tools for estimating Total Cost of Ownership (TCO).
Day 2: Compliance, Evaluation, and Post-Award Management
Morning Session
Module 5: Federal Compliance Standards for AI
• Overview of key compliance frameworks:
• Federal Information Security Management Act (FISMA).
• Federal Risk and Authorization Management Program (FedRAMP).
• Freedom of Information Act (FOIA).
• Section 508 compliance for accessibility.
• Practical steps to ensure AI systems meet these requirements.
• Integration of compliance standards into procurement documents.
Module 6: Responsible AI and Risk Management
• Introduction to the NIST AI Risk Management Framework.
• Addressing ethical AI concerns:
• Mitigating algorithmic bias.
• Ensuring data security and privacy.
• Responsible AI procurement practices:
• Requiring explainability and auditability in AI solutions.
• Including de-biasing requirements in RFPs.
Afternoon Session
Module 7: Vendor Evaluation and Proposal Assessment
• Designing evaluation criteria for AI solutions:
• Technical capability: algorithm quality, scalability, and adaptability.
• Security: data protection and compliance.
• Vendor qualifications: expertise, past performance, and staffing.
• Weighting and scoring proposals: balancing cost, technical merit, and compliance.
• Conducting oral presentations and technical demonstrations.
Module 8: Managing AI Contracts Post-Acquisition
• Designing post-award monitoring plans:
• Service-level agreements (SLAs) for AI systems.
• Performance monitoring: KPIs and regular reporting.
• Risk management strategies:
• Handling system failures.
• Ensuring vendor accountability for upgrades and bug fixes.
• End-of-life planning for AI systems: decommissioning and data migration.
• Risk management in AI systems acquisition.
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.