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: 41 (67.2%)Classical / predictive ML
Pilot: 6 (9.8%)Classical / predictive ML
Pre-deployment: 14 (23.0%)
Sourcing by technology
Where reportedDeveloped in-house: 1 (7.7%)Generative AI
Contract and in-house: 2 (15.4%)Generative AI
Purchased from vendor: 10 (76.9%)
Developed in-house: 20 (32.8%)Classical / predictive ML
Contract and in-house: 1 (1.6%)Classical / predictive ML
Purchased from vendor: 32 (52.5%)Classical / predictive ML
Not reported: 8 (13.1%)
Developed in-house: 2 (10.5%)Natural language processing
Contract and in-house: 2 (10.5%)Natural language processing
Purchased from vendor: 14 (73.7%)Natural language processing
Not reported: 1 (5.3%)
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 | |
|---|---|---|---|---|---|---|
| Law Enforcement | 14% | 36% | 20% | 29% | 2% | 66 |
| Science | 4% | 85% | 7% | 4% | 27 | |
| Health and Medical | 86% | 14% | 7 | |||
| Administrative Functions | 17% | 33% | 33% | 17% | 6 | |
| Energy and the Environment | 25% | 75% | 4 | |||
| Information Technology | 50% | 50% | 2 | |||
| Service Delivery | 50% | 50% | 2 | |||
| Emergency Management | 100% | 1 | ||||
| Human Resources | 100% | 1 | ||||
| Other | 100% | 1 | ||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 33 (54.1%)Classical / predictive ML
Not high-impact: 28 (45.9%)
High-impact: 14 (73.7%)Natural language processing
Not high-impact: 5 (26.3%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 10 | 0 / 10 | 0 / 10 | 0 / 10 | 0 / 10 | 0 / 10 |
| Classical / predictive ML | 33 | 0 / 33 | 0 / 33 | 0 / 33 | 0 / 33 | 0 / 33 |
| Natural language processing | 14 | 0 / 14 | 0 / 14 | 0 / 14 | 0 / 14 | 0 / 14 |
| Computer vision | 20 | 0 / 20 | 0 / 20 | 0 / 20 | 0 / 20 | 0 / 20 |
| Agentic AI | 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
61 of 118 records name a vendor (52%)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 66% 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.