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: 24 (77.4%)Classical / predictive ML
Pilot: 2 (6.5%)Classical / predictive ML
Pre-deployment: 5 (16.1%)
Sourcing by technology
Where reportedDeveloped in-house: 3 (17.6%)Generative AI
Contract and in-house: 4 (23.5%)Generative AI
Purchased from vendor: 10 (58.8%)
Developed in-house: 10 (32.3%)Classical / predictive ML
Contract and in-house: 8 (25.8%)Classical / predictive ML
Purchased from vendor: 13 (41.9%)
Developed in-house: 1 (12.5%)Natural language processing
Contract and in-house: 2 (25.0%)Natural language processing
Purchased from vendor: 5 (62.5%)
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 | 39% | 61% | 18 | |||
| Information Technology | 43% | 29% | 21% | 7% | 14 | |
| Energy and the Environment | 14% | 57% | 29% | 7 | ||
| Government Benefits Processing | 29% | 71% | 7 | |||
| Administrative Functions | 17% | 50% | 17% | 17% | 6 | |
| Cybersecurity | 20% | 60% | 20% | 5 | ||
| Other | 40% | 40% | 20% | 5 | ||
| Service Delivery | 60% | 40% | 5 | |||
| Transportation | 67% | 33% | 3 | |||
| Health and Medical | 100% | 1 | ||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 5 (29.4%)Generative AI
Presumed high-impact, determined not: 1 (5.9%)Generative AI
Not high-impact: 11 (64.7%)
High-impact: 16 (51.6%)Classical / predictive ML
Not high-impact: 15 (48.4%)
High-impact: 1 (12.5%)Natural language processing
Not high-impact: 7 (87.5%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 5 | 3 / 5 | 4 / 5 | 4 / 5 | 2 / 5 | 5 / 5 |
| Classical / predictive ML | 16 | 16 / 16 | 14 / 16 | 14 / 16 | 16 / 16 | 15 / 16 |
| Natural language processing | 1 | 1 / 1 | 1 / 1 | 1 / 1 | 1 / 1 | 1 / 1 |
| Computer vision | 15 | 13 / 15 | 11 / 15 | 11 / 15 | 13 / 15 | 13 / 15 |
| Agentic AI | 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
59 of 72 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.