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: 11 (33.3%)Generative AI
Pilot: 8 (24.2%)Generative AI
Pre-deployment: 13 (39.4%)Generative AI
Retired: 1 (3.0%)
Deployed: 27 (87.1%)Classical / predictive ML
Pilot: 2 (6.5%)Classical / predictive ML
Pre-deployment: 1 (3.2%)Classical / predictive ML
Retired: 1 (3.2%)
Sourcing by technology
Where reportedDeveloped in-house: 8 (24.2%)Generative AI
Contract and in-house: 4 (12.1%)Generative AI
Purchased from vendor: 21 (63.6%)
Developed in-house: 14 (45.2%)Classical / predictive ML
Contract and in-house: 8 (25.8%)Classical / predictive ML
Purchased from vendor: 9 (29.0%)
Developed in-house: 2 (20.0%)Natural language processing
Contract and in-house: 5 (50.0%)Natural language processing
Purchased from vendor: 3 (30.0%)
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 | n | |
|---|---|---|---|---|---|---|
| Law Enforcement | 32% | 7% | 61% | 28 | ||
| Administrative Functions | 69% | 19% | 6% | 6% | 16 | |
| Information Technology | 50% | 25% | 19% | 6% | 16 | |
| Service Delivery | 70% | 30% | 10 | |||
| Government Benefits Processing | 25% | 75% | 8 | |||
| Energy and the Environment | 25% | 50% | 25% | 4 | ||
| Other | 50% | 50% | 4 | |||
| Transportation | 67% | 33% | 3 | |||
| Cybersecurity | 50% | 50% | 2 | |||
| Procurement and Financial Management | 100% | 2 | ||||
| Health and Medical | 100% | 1 |
High-impact designation by technology
High-impact: 3 (9.1%)Generative AI
Presumed high-impact, determined not: 1 (3.0%)Generative AI
Not high-impact: 29 (87.9%)
High-impact: 19 (61.3%)Classical / predictive ML
Not high-impact: 12 (38.7%)
High-impact: 4 (40.0%)Natural language processing
Not high-impact: 6 (60.0%)
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 | 19 | 19 / 19 | 16 / 19 | 16 / 19 | 19 / 19 | 18 / 19 |
| Natural language processing | 4 | 4 / 4 | 2 / 4 | 2 / 4 | 2 / 4 | 2 / 4 |
| Computer vision | 19 | 19 / 19 | 15 / 19 | 15 / 19 | 17 / 19 | 17 / 19 |
| 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
74 of 94 records name a vendor (79%)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 95% 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.