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: 8 (28.6%)Generative AI
Pilot: 7 (25.0%)Generative AI
Pre-deployment: 12 (42.9%)Generative AI
Retired: 1 (3.6%)
Deployed: 28 (57.1%)Classical / predictive ML
Pilot: 7 (14.3%)Classical / predictive ML
Pre-deployment: 13 (26.5%)Classical / predictive ML
Retired: 1 (2.0%)
Sourcing by technology
Where reportedDeveloped in-house: 6 (21.4%)Generative AI
Contract and in-house: 3 (10.7%)Generative AI
Purchased from vendor: 19 (67.9%)
Developed in-house: 30 (61.2%)Classical / predictive ML
Contract and in-house: 4 (8.2%)Classical / predictive ML
Purchased from vendor: 7 (14.3%)Classical / predictive ML
Not reported: 8 (16.3%)
Developed in-house: 1 (10.0%)Natural language processing
Contract and in-house: 3 (30.0%)Natural language processing
Purchased from vendor: 5 (50.0%)Natural language processing
Not reported: 1 (10.0%)
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 | n | |
|---|---|---|---|---|---|---|---|
| Science | 4% | 85% | 7% | 4% | 27 | ||
| Law Enforcement | 36% | 64% | 22 | ||||
| Information Technology | 53% | 24% | 18% | 6% | 17 | ||
| Administrative Functions | 82% | 18% | 11 | ||||
| Energy and the Environment | 11% | 11% | 11% | 33% | 33% | 9 | |
| Government Benefits Processing | 25% | 75% | 8 | ||||
| Service Delivery | 71% | 29% | 7 | ||||
| Transportation | 67% | 33% | 3 | ||||
| Cybersecurity | 50% | 50% | 2 | ||||
| Other | 50% | 50% | 2 | ||||
| Emergency Management | 100% | 1 | |||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 16 (32.7%)Classical / predictive ML
Not high-impact: 33 (67.3%)
High-impact: 2 (20.0%)Natural language processing
Not high-impact: 8 (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 | 4 | 2 / 4 | 4 / 4 | 4 / 4 | 1 / 4 | 4 / 4 |
| Classical / predictive ML | 16 | 16 / 16 | 16 / 16 | 16 / 16 | 16 / 16 | 16 / 16 |
| Natural language processing | 2 | 2 / 2 | 2 / 2 | 2 / 2 | 2 / 2 | 1 / 2 |
| Computer vision | 15 | 15 / 15 | 15 / 15 | 15 / 15 | 15 / 15 | 15 / 15 |
| Agentic AI | 0 | – | – | – | – | – |
| Other | 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
64 of 110 records name a vendor (58%)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 65% 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.