Industry Contracting Guide
Government Contracts for AI / Machine Learning
Federal AI investments accelerated sharply after the 2023 EO and 2024 OMB guidance. CDAO, JAIC successors, and agency-level AI accelerators are the main entry points.
Industry snapshot
- Average contract size
- $250K–$50M
- Common certifications
- FedRAMP · CMMC L2 · ISO 42001
Common government buyers
- Department of Defense (DoD)
National defense and military operations
- Department of Homeland Security (DHS)
Homeland security and counter-terrorism
- Department of Veterans Affairs (VA)
Healthcare and benefits for veterans
Typical contract types
- OTA
- SBIR
- IDIQ task order
Challenges to expect
- Model bias documentation
- Algorithmic ATO
- Test and evaluation rigor
Where the opportunities are right now
- CDAO Open DAGIR ecosystem
- DHS AI Corps task orders
- NSF AI Institutes
Most relevant NAICS codes
- NAICS 541715 — Research & Development in the Physical, Engineering & Life Sciences
- NAICS 541512 — Computer Systems Design Services
States with the most ai / machine learning contracting activity
- California — ~$73.5B annual
- Virginia — ~$105.4B annual
- Maryland — ~$33.8B annual
FAQs
- What agencies buy the most ai / machine learning services?
- Top federal buyers for ai / machine learning include Department of Defense, Department of Homeland Security, Department of Veterans Affairs.
- What is the typical contract size in ai / machine learning?
- Average federal contract size in ai / machine learning ranges $250K–$50M, with the largest awards typically flowing through IDIQ MATOC pools and BPAs.
- Which NAICS codes apply to ai / machine learning?
- The most relevant NAICS codes are 541715 (Research & Development in the Physical, Engineering & Life Sciences); 541512 (Computer Systems Design Services); 541990 ().
- What certifications matter most in ai / machine learning contracting?
- Common gating certifications include FedRAMP, CMMC L2, ISO 42001. Set-aside certifications (8(a), HUBZone, WOSB, SDVOSB) layer on top for small businesses.
- What are the biggest challenges for new entrants?
- Model bias documentation; Algorithmic ATO; Test and evaluation rigor. These are surmountable but should be priced into your B&P investment.