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: 6 (24.0%)Generative AI
Pilot: 6 (24.0%)Generative AI
Pre-deployment: 12 (48.0%)Generative AI
Retired: 1 (4.0%)
Deployed: 33 (61.1%)Classical / predictive ML
Pilot: 7 (13.0%)Classical / predictive ML
Pre-deployment: 13 (24.1%)Classical / predictive ML
Retired: 1 (1.9%)
Sourcing by technology
Where reportedDeveloped in-house: 6 (24.0%)Generative AI
Contract and in-house: 3 (12.0%)Generative AI
Purchased from vendor: 16 (64.0%)
Developed in-house: 32 (59.3%)Classical / predictive ML
Contract and in-house: 7 (13.0%)Classical / predictive ML
Purchased from vendor: 7 (13.0%)Classical / predictive ML
Not reported: 8 (14.8%)
Developed in-house: 3 (27.3%)Natural language processing
Contract and in-house: 3 (27.3%)Natural language processing
Purchased from vendor: 4 (36.4%)Natural language processing
Not reported: 1 (9.1%)
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 | n | |
|---|---|---|---|---|---|---|
| Science | 4% | 85% | 7% | 4% | 27 | |
| Law Enforcement | 38% | 63% | 24 | |||
| Administrative Functions | 57% | 21% | 14% | 7% | 14 | |
| Information Technology | 43% | 29% | 21% | 7% | 14 | |
| Government Benefits Processing | 14% | 86% | 7 | |||
| Service Delivery | 71% | 29% | 7 | |||
| Energy and the Environment | 17% | 17% | 17% | 50% | 6 | |
| Other | 25% | 25% | 50% | 4 | ||
| Transportation | 67% | 33% | 3 | |||
| Cybersecurity | 50% | 50% | 2 | |||
| Procurement and Financial Management | 100% | 2 | ||||
| Emergency Management | 100% | 1 | ||||
| Health and Medical | 100% | 1 |
High-impact designation by technology
High-impact: 2 (8.0%)Generative AI
Presumed high-impact, determined not: 1 (4.0%)Generative AI
Not high-impact: 22 (88.0%)
High-impact: 19 (35.2%)Classical / predictive ML
Not high-impact: 35 (64.8%)
High-impact: 2 (18.2%)Natural language processing
Not high-impact: 9 (81.8%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 2 | 2 / 2 | 1 / 2 | 1 / 2 | 2 / 2 | 2 / 2 |
| Classical / predictive ML | 19 | 19 / 19 | 16 / 19 | 16 / 19 | 19 / 19 | 18 / 19 |
| Natural language processing | 2 | 2 / 2 | 2 / 2 | 2 / 2 | 2 / 2 | 1 / 2 |
| Computer vision | 17 | 17 / 17 | 15 / 17 | 15 / 17 | 17 / 17 | 17 / 17 |
| Agentic AI | 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
63 of 112 records name a vendor (56%)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 67% 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.