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: 36 (34.6%)Generative AI
Pilot: 25 (24.0%)Generative AI
Pre-deployment: 43 (41.3%)
Deployed: 2 (15.4%)Classical / predictive ML
Pilot: 2 (15.4%)Classical / predictive ML
Pre-deployment: 9 (69.2%)
Deployed: 11 (32.4%)Natural language processing
Pilot: 2 (5.9%)Natural language processing
Pre-deployment: 20 (58.8%)Natural language processing
Retired: 1 (2.9%)
Deployed: 1 (33.3%)Computer vision
Pilot: 1 (33.3%)Computer vision
Pre-deployment: 1 (33.3%)
Sourcing by technology
Where reportedDeveloped in-house: 21 (20.2%)Generative AI
Contract and in-house: 19 (18.3%)Generative AI
Purchased from vendor: 22 (21.2%)Generative AI
Not reported: 42 (40.4%)
Developed in-house: 2 (15.4%)Classical / predictive ML
Contract and in-house: 1 (7.7%)Classical / predictive ML
Purchased from vendor: 1 (7.7%)Classical / predictive ML
Not reported: 9 (69.2%)
Developed in-house: 6 (17.6%)Natural language processing
Contract and in-house: 3 (8.8%)Natural language processing
Purchased from vendor: 3 (8.8%)Natural language processing
Not reported: 22 (64.7%)
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 | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| HHS | 60% | 3% | 27% | 7% | 3% | 30 | ||
| VA | 53% | 11% | 32% | 5% | 19 | |||
| DOJ | 41% | 6% | 47% | 6% | 17 | |||
| DHS | 88% | 6% | 6% | 16 | ||||
| TREAS | 92% | 8% | 12 | |||||
| SEC | 78% | 22% | 9 | |||||
| ED | 100% | 9 | ||||||
| DOE | 100% | 8 | ||||||
| DOL | 86% | 14% | 7 | |||||
| DOI | 50% | 33% | 17% | 6 | ||||
| STATE | 50% | 33% | 17% | 6 | ||||
| FHFA | 75% | 25% | 4 | |||||
| NASA | 75% | 25% | 4 | |||||
| USDA | 50% | 50% | 4 | |||||
| NSF | 100% | 3 | ||||||
| FCC | 100% | 3 |
Technology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| Administrative Functions | 80% | 7% | 14% | 44 | ||||
| Information Technology | 79% | 4% | 13% | 4% | 24 | |||
| Law Enforcement | 47% | 41% | 12% | 17 | ||||
| Health and Medical | 43% | 50% | 7% | 14 | ||||
| Service Delivery | 64% | 14% | 21% | 14 | ||||
| Other | 58% | 8% | 17% | 17% | 12 | |||
| Science | 42% | 33% | 17% | 8% | 12 | |||
| Emergency Management | 33% | 17% | 50% | 6 | ||||
| Procurement and Financial Management | 83% | 17% | 6 | |||||
| Energy and the Environment | 100% | 4 | ||||||
| Government Benefits Processing | 75% | 25% | 4 | |||||
| Cybersecurity | 100% | 1 | ||||||
| Transportation | 100% | 1 |
High-impact designation by technology
High-impact: 8 (7.7%)Generative AI
Presumed high-impact, determined not: 2 (1.9%)Generative AI
Not high-impact: 93 (89.4%)Generative AI
Not reported: 1 (1.0%)
High-impact: 1 (7.7%)Classical / predictive ML
Presumed high-impact, determined not: 1 (7.7%)Classical / predictive ML
Not high-impact: 11 (84.6%)
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 | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Natural language processing | 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 |
| Computer vision | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Agentic AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| 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
46 of 178 records name a vendor (26%)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.