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: 30 (18.5%)Classical / predictive ML
Pilot: 62 (38.3%)Classical / predictive ML
Pre-deployment: 70 (43.2%)
Deployed: 5 (33.3%)Natural language processing
Pilot: 1 (6.7%)Natural language processing
Pre-deployment: 9 (60.0%)
Deployed: 2 (10.5%)Computer vision
Pilot: 13 (68.4%)Computer vision
Pre-deployment: 4 (21.1%)
Sourcing by technology
Where reportedDeveloped in-house: 5 (33.3%)Generative AI
Contract and in-house: 1 (6.7%)Generative AI
Purchased from vendor: 1 (6.7%)Generative AI
Not reported: 8 (53.3%)
Developed in-house: 91 (56.2%)Classical / predictive ML
Contract and in-house: 5 (3.1%)Classical / predictive ML
Purchased from vendor: 8 (4.9%)Classical / predictive ML
Not reported: 58 (35.8%)
Developed in-house: 5 (33.3%)Natural language processing
Purchased from vendor: 1 (6.7%)Natural language processing
Not reported: 9 (60.0%)
Developed in-house: 11 (57.9%)Computer vision
Contract and in-house: 2 (10.5%)Computer vision
Purchased from vendor: 4 (21.1%)Computer vision
Not reported: 2 (10.5%)
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% | 83% | 2% | 7% | 1% | 3% | 163 | ||
| Energy and the Environment | 67% | 21% | 4% | 8% | 24 | ||||
| Other | 17% | 33% | 50% | 12 | |||||
| Administrative Functions | 22% | 22% | 22% | 11% | 22% | 9 | |||
| Service Delivery | 29% | 43% | 29% | 7 | |||||
| Emergency Management | 75% | 25% | 4 | ||||||
| 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
21 of 238 records name a vendor (9%)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.