GenAI for Public Procurement Professionals
A practical, no-hype introduction to generative AI for the people who buy for government — how to use it across the acquisition lifecycle, and how to keep it responsible.
Overview
Generative AI is moving into acquisition faster than the guidance can keep up. This course gives procurement professionals a grounded, practitioner's view: what generative AI actually does, where it genuinely helps in the acquisition lifecycle, and where the risk, policy, and governance guardrails belong. No hype, no doom — just what you need to use these tools well and defend how you used them.
What you'll learn
Foundations of generative AI
What large language models are, what they're good and bad at, and the vocabulary you need to evaluate them honestly.
Buying AI vs. buying with AI
The two distinct challenges — acquiring AI capabilities responsibly, and applying AI to run acquisition better.
AI across the acquisition lifecycle
Practical use in market research, requirements development, and drafting — with the human judgment that stays in the loop.
Evaluation & source selection
Where AI can and cannot support evaluation, and how to keep decisions defensible and documented.
Risk, ethics & governance
Data handling, bias, transparency, and the emerging policy landscape federal buyers must work within.
Putting it to work
Building AI-ready habits into your team's workflow and keeping practice current as guidance evolves.
Draft outline — final module list confirmed at launch.
Who it's for
- Contracting officers, specialists, and CORs
- Program and acquisition managers
- Acquisition leaders shaping AI policy and adoption
- Contractors and partners working the federal marketplace
Your instructor
Led by Jaime Gracia, Director of Corporate Affairs at The Wolverine Group — a federal acquisition practitioner, instructor, and NCMA Fellow with decades of experience across the acquisition enterprise.
Want this for your whole team?
Ask about cohort delivery and tailored versions for your agency or organization.
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