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: 3 (13.0%)Generative AI
Pilot: 6 (26.1%)Generative AI
Pre-deployment: 14 (60.9%)
Deployed: 30 (50.0%)Classical / predictive ML
Pilot: 6 (10.0%)Classical / predictive ML
Pre-deployment: 24 (40.0%)
Sourcing by technology
Where reportedDeveloped in-house: 4 (17.4%)Generative AI
Contract and in-house: 2 (8.7%)Generative AI
Purchased from vendor: 3 (13.0%)Generative AI
Not reported: 14 (60.9%)
Developed in-house: 18 (30.0%)Classical / predictive ML
Contract and in-house: 5 (8.3%)Classical / predictive ML
Purchased from vendor: 14 (23.3%)Classical / predictive ML
Not reported: 23 (38.3%)
Developed in-house: 8 (20.0%)Natural language processing
Contract and in-house: 7 (17.5%)Natural language processing
Purchased from vendor: 5 (12.5%)Natural language processing
Not reported: 20 (50.0%)
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 | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| Health and Medical | 13% | 25% | 22% | 35% | 4% | 1% | 141 | |
| Science | 79% | 7% | 7% | 7% | 14 | |||
| Administrative Functions | 33% | 44% | 22% | 9 | ||||
| Service Delivery | 29% | 43% | 14% | 14% | 7 | |||
| Other | 20% | 60% | 20% | 5 | ||||
| Information Technology | 67% | 33% | 3 | |||||
| Government Benefits Processing | 50% | 50% | 2 | |||||
| Transportation | 100% | 2 | ||||||
| Cybersecurity | 100% | 1 | ||||||
| Emergency Management | 100% | 1 | ||||||
| Energy and the Environment | 100% | 1 |
High-impact designation by technology
High-impact: 31 (51.7%)Classical / predictive ML
Presumed high-impact, determined not: 1 (1.7%)Classical / predictive ML
Not high-impact: 28 (46.7%)
High-impact: 15 (37.5%)Natural language processing
Not high-impact: 25 (62.5%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 10 | 1 / 10 | 0 / 10 | 0 / 10 | 1 / 10 | 1 / 10 |
| Classical / predictive ML | 31 | 1 / 31 | 1 / 31 | 1 / 31 | 1 / 31 | 1 / 31 |
| Natural language processing | 15 | 0 / 15 | 0 / 15 | 0 / 15 | 0 / 15 | 0 / 15 |
| Computer vision | 46 | 0 / 46 | 0 / 46 | 0 / 46 | 0 / 46 | 0 / 46 |
| Agentic AI | 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 |
| Other | 0 | – | – | – | – | – |
| Not reported | 6 | 0 / 6 | 0 / 6 | 0 / 6 | 0 / 6 | 0 / 6 |
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
12 of 194 records name a vendor (6%)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 9% 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.