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: 22 (15.5%)Classical / predictive ML
Pilot: 57 (40.1%)Classical / predictive ML
Pre-deployment: 63 (44.4%)
Deployed: 1 (6.7%)Computer vision
Pilot: 10 (66.7%)Computer vision
Pre-deployment: 4 (26.7%)
Sourcing by technology
Where reportedDeveloped in-house: 4 (28.6%)Generative AI
Contract and in-house: 1 (7.1%)Generative AI
Purchased from vendor: 1 (7.1%)Generative AI
Not reported: 8 (57.1%)
Developed in-house: 77 (54.2%)Classical / predictive ML
Contract and in-house: 5 (3.5%)Classical / predictive ML
Purchased from vendor: 8 (5.6%)Classical / predictive ML
Not reported: 52 (36.6%)
Developed in-house: 4 (33.3%)Natural language processing
Not reported: 8 (66.7%)
Developed in-house: 7 (46.7%)Computer vision
Contract and in-house: 2 (13.3%)Computer vision
Purchased from vendor: 4 (26.7%)Computer vision
Not reported: 2 (13.3%)
Developed in-house: 2 (40.0%)Agentic AI
Contract and in-house: 1 (20.0%)Agentic AI
Purchased from vendor: 1 (20.0%)Agentic AI
Not reported: 1 (20.0%)
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 | Reinforcement learning | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|---|
| Science | 4% | 84% | 1% | 7% | 1% | 4% | 141 | ||
| Energy and the Environment | 76% | 10% | 5% | 10% | 21 | ||||
| Other | 18% | 27% | 55% | 11 | |||||
| Administrative Functions | 25% | 25% | 13% | 13% | 25% | 8 | |||
| Service Delivery | 29% | 43% | 29% | 7 | |||||
| Emergency Management | 67% | 33% | 3 | ||||||
| Information Technology | 100% | 2 | |||||||
| Procurement and Financial Management | 50% | 50% | 2 | ||||||
| Law Enforcement | 100% | 1 | |||||||
| Transportation | 100% | 1 |
High-impact designation by technology
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 0 | – | – | – | – | – |
| Classical / predictive ML | 0 | – | – | – | – | – |
| Natural language processing | 0 | – | – | – | – | – |
| Computer vision | 0 | – | – | – | – | – |
| Agentic AI | 0 | – | – | – | – | – |
| Reinforcement learning | 0 | – | – | – | – | – |
| 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
20 of 210 records name a vendor (10%)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 0% 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.