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: 45 (75.0%)Generative AI
Pilot: 3 (5.0%)Generative AI
Pre-deployment: 11 (18.3%)Generative AI
Retired: 1 (1.7%)
Deployed: 36 (76.6%)Classical / predictive ML
Pilot: 2 (4.3%)Classical / predictive ML
Pre-deployment: 9 (19.1%)
Sourcing by technology
Where reportedDeveloped in-house: 3 (5.0%)Generative AI
Contract and in-house: 5 (8.3%)Generative AI
Purchased from vendor: 52 (86.7%)
Developed in-house: 5 (10.6%)Classical / predictive ML
Contract and in-house: 2 (4.3%)Classical / predictive ML
Purchased from vendor: 39 (83.0%)Classical / predictive ML
Not reported: 1 (2.1%)
Developed in-house: 2 (9.1%)Natural language processing
Contract and in-house: 4 (18.2%)Natural language processing
Purchased from vendor: 16 (72.7%)
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 | |
|---|---|---|---|---|---|---|---|
| Law Enforcement | 13% | 34% | 23% | 30% | 1% | 71 | |
| Information Technology | 92% | 3% | 5% | 37 | |||
| Service Delivery | 73% | 27% | 11 | ||||
| Administrative Functions | 40% | 30% | 10% | 10% | 10% | 10 | |
| Health and Medical | 86% | 14% | 7 | ||||
| Energy and the Environment | 80% | 20% | 5 | ||||
| Cybersecurity | 25% | 75% | 4 | ||||
| Other | 50% | 25% | 25% | 4 | |||
| Science | 100% | 2 | |||||
| Government Benefits Processing | 100% | 1 | |||||
| Human Resources | 100% | 1 | |||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 33 (70.2%)Classical / predictive ML
Not high-impact: 14 (29.8%)
High-impact: 17 (77.3%)Natural language processing
Not high-impact: 5 (22.7%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 13 | 1 / 13 | 3 / 13 | 3 / 13 | 0 / 13 | 3 / 13 |
| Classical / predictive ML | 33 | 0 / 33 | 0 / 33 | 0 / 33 | 0 / 33 | 0 / 33 |
| Natural language processing | 17 | 2 / 17 | 0 / 17 | 0 / 17 | 0 / 17 | 1 / 17 |
| Computer vision | 23 | 2 / 23 | 0 / 23 | 0 / 23 | 0 / 23 | 0 / 23 |
| Agentic AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| 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
125 of 154 records name a vendor (81%)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 92% 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.