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: 10 (25.0%)Generative AI
Pilot: 7 (17.5%)Generative AI
Pre-deployment: 22 (55.0%)Generative AI
Retired: 1 (2.5%)
Deployed: 32 (59.3%)Classical / predictive ML
Pilot: 7 (13.0%)Classical / predictive ML
Pre-deployment: 14 (25.9%)Classical / predictive ML
Retired: 1 (1.9%)
Sourcing by technology
Where reportedDeveloped in-house: 17 (42.5%)Generative AI
Contract and in-house: 4 (10.0%)Generative AI
Purchased from vendor: 19 (47.5%)
Developed in-house: 33 (61.1%)Classical / predictive ML
Contract and in-house: 6 (11.1%)Classical / predictive ML
Purchased from vendor: 7 (13.0%)Classical / predictive ML
Not reported: 8 (14.8%)
Developed in-house: 3 (30.0%)Natural language processing
Contract and in-house: 3 (30.0%)Natural language processing
Purchased from vendor: 3 (30.0%)Natural language processing
Not reported: 1 (10.0%)
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 | |
|---|---|---|---|---|---|---|---|
| Science | 7% | 82% | 7% | 4% | 28 | ||
| Law Enforcement | 36% | 4% | 60% | 25 | |||
| Administrative Functions | 71% | 12% | 12% | 6% | 17 | ||
| Information Technology | 59% | 24% | 12% | 6% | 17 | ||
| Government Benefits Processing | 25% | 75% | 8 | ||||
| Service Delivery | 71% | 29% | 7 | ||||
| Energy and the Environment | 17% | 17% | 17% | 50% | 6 | ||
| Other | 50% | 17% | 33% | 6 | |||
| Procurement and Financial Management | 33% | 33% | 17% | 17% | 6 | ||
| Transportation | 67% | 33% | 3 | ||||
| Cybersecurity | 50% | 50% | 2 | ||||
| Emergency Management | 100% | 1 | |||||
| Health and Medical | 100% | 1 | |||||
| Human Resources | 100% | 1 |
High-impact designation by technology
High-impact: 5 (12.5%)Generative AI
Presumed high-impact, determined not: 1 (2.5%)Generative AI
Not high-impact: 34 (85.0%)
High-impact: 18 (33.3%)Classical / predictive ML
Not high-impact: 36 (66.7%)
High-impact: 2 (20.0%)Natural language processing
Not high-impact: 8 (80.0%)
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 | 18 | 18 / 18 | 16 / 18 | 16 / 18 | 18 / 18 | 18 / 18 |
| Natural language processing | 2 | 2 / 2 | 1 / 2 | 1 / 2 | 1 / 2 | 2 / 2 |
| Computer vision | 17 | 17 / 17 | 15 / 17 | 15 / 17 | 17 / 17 | 17 / 17 |
| 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
64 of 129 records name a vendor (50%)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 71% 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.