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: 1 (5.9%)Generative AI
Contract and in-house: 2 (11.8%)Generative AI
Purchased from vendor: 3 (17.6%)Generative AI
Not reported: 11 (64.7%)
Developed in-house: 30 (38.0%)Classical / predictive ML
Contract and in-house: 11 (13.9%)Classical / predictive ML
Purchased from vendor: 5 (6.3%)Classical / predictive ML
Not reported: 33 (41.8%)
Technology adoption by agency
Row share: each agency's records by classification · 16 largest portfolios in the filter| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Reinforcement learning | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|---|
| DOI | 9% | 83% | 4% | 4% | 23 | ||||
| NASA | 7% | 87% | 7% | 15 | |||||
| HHS | 8% | 75% | 8% | 8% | 12 | ||||
| DOC | 100% | 10 | |||||||
| VA | 33% | 56% | 11% | 9 | |||||
| TREAS | 33% | 56% | 11% | 9 | |||||
| DOJ | 63% | 25% | 13% | 8 | |||||
| DOE | 13% | 75% | 13% | 8 | |||||
| DHS | 14% | 86% | 7 | ||||||
| USDA | 83% | 17% | 6 | ||||||
| FRB | 40% | 60% | 5 | ||||||
| FHFA | 100% | 3 | |||||||
| DOT | 67% | 33% | 3 | ||||||
| TVA | 100% | 2 | |||||||
| GSA | 100% | 1 | |||||||
| EPA | 100% | 1 |
Technology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Reinforcement learning | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|---|
| Science | 8% | 87% | 3% | 3% | 38 | ||||
| Other | 10% | 60% | 30% | 10 | |||||
| Health and Medical | 78% | 11% | 11% | 9 | |||||
| Law Enforcement | 56% | 33% | 11% | 9 | |||||
| Information Technology | 13% | 75% | 13% | 8 | |||||
| Administrative Functions | 43% | 57% | 7 | ||||||
| Energy and the Environment | 71% | 29% | 7 | ||||||
| Procurement and Financial Management | 29% | 71% | 7 | ||||||
| Emergency Management | 83% | 17% | 6 | ||||||
| Transportation | 50% | 25% | 25% | 4 | |||||
| Cybersecurity | 100% | 2 | |||||||
| Human Resources | 100% | 2 | |||||||
| Service Delivery | 100% | 1 |
High-impact designation by technology
High-impact: 3 (17.6%)Generative AI
Presumed high-impact, determined not: 4 (23.5%)Generative AI
Not high-impact: 10 (58.8%)
High-impact: 8 (10.1%)Classical / predictive ML
Presumed high-impact, determined not: 2 (2.5%)Classical / predictive ML
Not high-impact: 68 (86.1%)Classical / predictive ML
Not reported: 1 (1.3%)
High-impact: 1 (20.0%)Natural language processing
Not high-impact: 4 (80.0%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Classical / predictive ML | 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 |
| Natural language processing | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Computer vision | 0 | – | – | – | – | – |
| Agentic AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Reinforcement learning | 0 | – | – | – | – | – |
| Other | 0 | – | – | – | – | – |
| Not reported | 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
17 of 124 records name a vendor (14%)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 19% 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.