Technology landscape
How reported AI classifications spread across agencies, missions, lifecycle stages and sourcing. Each record carries one reported classification, so categories do not overlap. A classification describes the technique, not a specific commercial model.
Lifecycle by technology
Share of each technology's recordsSourcing by technology
Where reportedDeveloped in-house: 25 (49.0%)Generative AI
Contract and in-house: 2 (3.9%)Generative AI
Purchased from vendor: 3 (5.9%)Generative AI
Not reported: 21 (41.2%)
Developed in-house: 5 (7.0%)Classical / predictive ML
Contract and in-house: 2 (2.8%)Classical / predictive ML
Not reported: 64 (90.1%)
Developed in-house: 1 (11.1%)Natural language processing
Contract and in-house: 1 (11.1%)Natural language processing
Purchased from vendor: 1 (11.1%)Natural language processing
Not reported: 6 (66.7%)
Technology adoption by agency
Row share: each agency's records by classification · 16 largest portfolios in the filterTechnology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Reinforcement learning | Other | n | |
|---|---|---|---|---|---|---|---|---|
| Science | 11% | 78% | 3% | 3% | 3% | 2% | 63 | |
| Information Technology | 71% | 4% | 13% | 8% | 4% | 24 | ||
| Energy and the Environment | 14% | 73% | 5% | 5% | 5% | 22 | ||
| Administrative Functions | 67% | 10% | 14% | 10% | 21 | |||
| Procurement and Financial Management | 50% | 17% | 17% | 17% | 6 | |||
| Other | 60% | 40% | 5 | |||||
| Cybersecurity | 33% | 33% | 33% | 3 | ||||
| Human Resources | 100% | 3 | ||||||
| International Affairs | 100% | 1 | ||||||
| Transportation | 100% | 1 |
High-impact designation by technology
High-impact: 6 (8.5%)Classical / predictive ML
Presumed high-impact, determined not: 3 (4.2%)Classical / predictive ML
Not high-impact: 62 (87.3%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 12 | 0 / 12 | 0 / 12 | 0 / 12 | 0 / 12 | 0 / 12 |
| Classical / predictive ML | 6 | 0 / 6 | 0 / 6 | 0 / 6 | 0 / 6 | 0 / 6 |
| Natural language processing | 0 | – | – | – | – | – |
| Computer vision | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Agentic AI | 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 |
| Reinforcement learning | 0 | – | – | – | – | – |
| Other | 0 | – | – | – | – | – |
Shows counts reported in place against all high-impact records of that technology. The remainder includes in-progress, not applicable and unanswered. Full governance view
Systems, vendors and technology are different things
18 of 149 records name a vendor (12%)Vendor: the supplier the agency names in a free-text field. Matched to a standard list of companies; one record may name several.
System name: the agency's name for the system (reported for 28% of records).
AI classification: the technique category the agency selected. A vendor name does not show which model or technique is used, and this platform does not infer it.