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 (76.9%)Generative AI
Pre-deployment: 11 (21.2%)Generative AI
Retired: 1 (1.9%)
Deployed: 34 (79.1%)Classical / predictive ML
Pilot: 1 (2.3%)Classical / predictive ML
Pre-deployment: 8 (18.6%)
Sourcing by technology
Where reportedDeveloped in-house: 1 (1.9%)Generative AI
Contract and in-house: 4 (7.7%)Generative AI
Purchased from vendor: 47 (90.4%)
Developed in-house: 4 (9.3%)Classical / predictive ML
Contract and in-house: 2 (4.7%)Classical / predictive ML
Purchased from vendor: 37 (86.0%)
Developed in-house: 2 (10.5%)Natural language processing
Contract and in-house: 2 (10.5%)Natural language processing
Purchased from vendor: 15 (78.9%)
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 | 13% | 36% | 21% | 28% | 1% | 67 |
| Information Technology | 91% | 3% | 6% | 34 | ||
| Service Delivery | 73% | 27% | 11 | |||
| Health and Medical | 86% | 14% | 7 | |||
| Administrative Functions | 17% | 50% | 17% | 17% | 6 | |
| Cybersecurity | 25% | 75% | 4 | |||
| Energy and the Environment | 67% | 33% | 3 | |||
| Other | 50% | 50% | 2 | |||
| Human Resources | 100% | 1 | ||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 33 (76.7%)Classical / predictive ML
Not high-impact: 10 (23.3%)
High-impact: 15 (78.9%)Natural language processing
Not high-impact: 4 (21.1%)
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 | 15 | 0 / 15 | 0 / 15 | 0 / 15 | 0 / 15 | 0 / 15 |
| Computer vision | 21 | 0 / 21 | 0 / 21 | 0 / 21 | 0 / 21 | 0 / 21 |
| 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
112 of 136 records name a vendor (82%)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 94% 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.