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: 21 (27.3%)Classical / predictive ML
Pilot: 33 (42.9%)Classical / predictive ML
Pre-deployment: 23 (29.9%)
Deployed: 6 (42.9%)Natural language processing
Pilot: 4 (28.6%)Natural language processing
Pre-deployment: 4 (28.6%)
Deployed: 3 (17.6%)Computer vision
Pilot: 6 (35.3%)Computer vision
Pre-deployment: 8 (47.1%)
Sourcing by technology
Where reportedDeveloped in-house: 1 (3.1%)Generative AI
Contract and in-house: 5 (15.6%)Generative AI
Purchased from vendor: 6 (18.8%)Generative AI
Not reported: 20 (62.5%)
Developed in-house: 44 (57.1%)Classical / predictive ML
Contract and in-house: 12 (15.6%)Classical / predictive ML
Purchased from vendor: 2 (2.6%)Classical / predictive ML
Not reported: 19 (24.7%)
Developed in-house: 6 (42.9%)Natural language processing
Contract and in-house: 4 (28.6%)Natural language processing
Purchased from vendor: 1 (7.1%)Natural language processing
Not reported: 3 (21.4%)
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 | Not reported | n | |
|---|---|---|---|---|---|---|---|
| Science | 5% | 75% | 7% | 13% | 60 | ||
| Information Technology | 76% | 12% | 12% | 17 | |||
| Service Delivery | 44% | 38% | 13% | 6% | 16 | ||
| Energy and the Environment | 14% | 57% | 7% | 21% | 14 | ||
| Emergency Management | 82% | 9% | 9% | 11 | |||
| Administrative Functions | 14% | 14% | 29% | 29% | 14% | 7 | |
| Other | 29% | 71% | 7 | ||||
| Procurement and Financial Management | 33% | 33% | 33% | 3 | |||
| Government Benefits Processing | 50% | 50% | 2 | ||||
| Transportation | 50% | 50% | 2 | ||||
| Cybersecurity | 100% | 1 | |||||
| Health and Medical | 100% | 1 | |||||
| Human Resources | 100% | 1 | |||||
| International Affairs | 100% | 1 |
High-impact designation by technology
Presumed high-impact, determined not: 1 (7.1%)Natural language processing
Not high-impact: 13 (92.9%)
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 | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Natural language processing | 0 | – | – | – | – | – |
| Computer vision | 1 | 1 / 1 | 0 / 1 | 0 / 1 | 1 / 1 | 1 / 1 |
| Agentic AI | 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
38 of 162 records name a vendor (23%)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 19% 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.