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: 40 (59.7%)Generative AI
Pilot: 9 (13.4%)Generative AI
Pre-deployment: 18 (26.9%)
Sourcing by technology
Where reportedDeveloped in-house: 15 (22.4%)Generative AI
Contract and in-house: 4 (6.0%)Generative AI
Purchased from vendor: 48 (71.6%)
Developed in-house: 6 (33.3%)Classical / predictive ML
Contract and in-house: 5 (27.8%)Classical / predictive ML
Purchased from vendor: 7 (38.9%)
Developed in-house: 2 (28.6%)Natural language processing
Contract and in-house: 3 (42.9%)Natural language processing
Purchased from vendor: 2 (28.6%)
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 | |
|---|---|---|---|---|---|---|---|
| Information Technology | 97% | 3% | 37 | ||||
| Administrative Functions | 84% | 11% | 5% | 19 | |||
| Law Enforcement | 21% | 7% | 71% | 14 | |||
| Energy and the Environment | 14% | 57% | 29% | 7 | |||
| Procurement and Financial Management | 29% | 43% | 14% | 14% | 7 | ||
| Service Delivery | 57% | 43% | 7 | ||||
| Other | 50% | 50% | 4 | ||||
| Cybersecurity | 33% | 33% | 33% | 3 | |||
| Transportation | 67% | 33% | 3 | ||||
| Government Benefits Processing | 100% | 2 | |||||
| Health and Medical | 100% | 1 | |||||
| Human Resources | 100% | 1 | |||||
| International Affairs | 100% | 1 | |||||
| Science | 100% | 1 |
High-impact designation by technology
High-impact: 2 (28.6%)Natural language processing
Not high-impact: 5 (71.4%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 3 | 3 / 3 | 2 / 3 | 2 / 3 | 2 / 3 | 3 / 3 |
| Classical / predictive ML | 6 | 6 / 6 | 5 / 6 | 5 / 6 | 6 / 6 | 6 / 6 |
| Natural language processing | 2 | 2 / 2 | 1 / 2 | 1 / 2 | 1 / 2 | 2 / 2 |
| Computer vision | 12 | 11 / 12 | 10 / 12 | 10 / 12 | 11 / 12 | 11 / 12 |
| 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
83 of 107 records name a vendor (78%)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 93% 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.