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 recordsDeployed: 20 (40.8%)Generative AI
Pilot: 10 (20.4%)Generative AI
Pre-deployment: 18 (36.7%)Generative AI
Retired: 1 (2.0%)
Deployed: 7 (53.8%)Classical / predictive ML
Pilot: 2 (15.4%)Classical / predictive ML
Pre-deployment: 4 (30.8%)
Deployed: 14 (38.9%)Natural language processing
Pilot: 5 (13.9%)Natural language processing
Pre-deployment: 17 (47.2%)
Sourcing by technology
Where reportedDeveloped in-house: 6 (12.2%)Generative AI
Contract and in-house: 7 (14.3%)Generative AI
Purchased from vendor: 18 (36.7%)Generative AI
Not reported: 18 (36.7%)
Developed in-house: 2 (15.4%)Classical / predictive ML
Contract and in-house: 4 (30.8%)Classical / predictive ML
Purchased from vendor: 4 (30.8%)Classical / predictive ML
Not reported: 3 (23.1%)
Developed in-house: 3 (8.3%)Natural language processing
Contract and in-house: 8 (22.2%)Natural language processing
Purchased from vendor: 9 (25.0%)Natural language processing
Not reported: 16 (44.4%)
Developed in-house: 1 (12.5%)Computer vision
Purchased from vendor: 3 (37.5%)Computer vision
Not reported: 4 (50.0%)
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 | |
|---|---|---|---|---|---|---|---|---|---|
| DOJ | 46% | 19% | 27% | 8% | 26 | ||||
| DHS | 45% | 5% | 36% | 5% | 9% | 22 | |||
| HHS | 36% | 9% | 36% | 9% | 9% | 11 | |||
| TREAS | 45% | 27% | 9% | 9% | 9% | 11 | |||
| VA | 60% | 20% | 10% | 10% | 10 | ||||
| STATE | 67% | 22% | 11% | 9 | |||||
| NASA | 17% | 50% | 17% | 17% | 6 | ||||
| DOC | 100% | 6 | |||||||
| DOL | 75% | 25% | 4 | ||||||
| DOE | 25% | 25% | 25% | 25% | 4 | ||||
| DOT | 100% | 3 | |||||||
| SSA | 33% | 67% | 3 | ||||||
| FRB | 100% | 2 | |||||||
| FDIC | 50% | 50% | 2 | ||||||
| USDA | 50% | 50% | 2 | ||||||
| FTC | 50% | 50% | 2 |
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 | |
|---|---|---|---|---|---|---|---|---|---|
| Law Enforcement | 36% | 11% | 39% | 14% | 28 | ||||
| Information Technology | 67% | 5% | 14% | 5% | 5% | 5% | 21 | ||
| Administrative Functions | 50% | 19% | 19% | 6% | 6% | 16 | |||
| Other | 58% | 33% | 8% | 12 | |||||
| Service Delivery | 30% | 20% | 50% | 10 | |||||
| Health and Medical | 33% | 22% | 33% | 11% | 9 | ||||
| Science | 13% | 25% | 38% | 13% | 13% | 8 | |||
| Transportation | 25% | 75% | 4 | ||||||
| Government Benefits Processing | 67% | 33% | 3 | ||||||
| Emergency Management | 100% | 1 | |||||||
| Energy and the Environment | 100% | 1 | |||||||
| Human Resources | 100% | 1 | |||||||
| International Affairs | 100% | 1 |
High-impact designation by technology
High-impact: 8 (16.3%)Generative AI
Presumed high-impact, determined not: 1 (2.0%)Generative AI
Not high-impact: 40 (81.6%)
High-impact: 2 (15.4%)Classical / predictive ML
Presumed high-impact, determined not: 2 (15.4%)Classical / predictive ML
Not high-impact: 9 (69.2%)
High-impact: 5 (13.9%)Natural language processing
Presumed high-impact, determined not: 4 (11.1%)Natural language processing
Not high-impact: 27 (75.0%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 |
| Classical / predictive ML | 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 |
| Natural language processing | 5 | 0 / 5 | 0 / 5 | 0 / 5 | 0 / 5 | 0 / 5 |
| Computer vision | 0 | – | – | – | – | – |
| Agentic AI | 0 | – | – | – | – | – |
| Reinforcement learning | 0 | – | – | – | – | – |
| Other | 0 | – | – | – | – | – |
| Not reported | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
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
42 of 129 records name a vendor (33%)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 31% 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.