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: 20 (32.3%)Generative AI
Pilot: 4 (6.5%)Generative AI
Pre-deployment: 38 (61.3%)
Deployed: 29 (64.4%)Classical / predictive ML
Pilot: 1 (2.2%)Classical / predictive ML
Pre-deployment: 15 (33.3%)
Sourcing by technology
Where reportedDeveloped in-house: 5 (8.1%)Generative AI
Contract and in-house: 8 (12.9%)Generative AI
Purchased from vendor: 11 (17.7%)Generative AI
Not reported: 38 (61.3%)
Developed in-house: 11 (24.4%)Classical / predictive ML
Contract and in-house: 11 (24.4%)Classical / predictive ML
Purchased from vendor: 8 (17.8%)Classical / predictive ML
Not reported: 15 (33.3%)
Developed in-house: 1 (3.6%)Natural language processing
Contract and in-house: 4 (14.3%)Natural language processing
Purchased from vendor: 4 (14.3%)Natural language processing
Not reported: 19 (67.9%)
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 | Not reported | n | |
|---|---|---|---|---|---|---|---|
| Law Enforcement | 20% | 36% | 11% | 33% | 55 | ||
| Administrative Functions | 56% | 18% | 18% | 8% | 50 | ||
| Transportation | 15% | 15% | 23% | 38% | 8% | 13 | |
| Service Delivery | 25% | 25% | 42% | 8% | 12 | ||
| Information Technology | 82% | 9% | 9% | 11 | |||
| Cybersecurity | 14% | 86% | 7 | ||||
| Emergency Management | 60% | 40% | 5 | ||||
| Government Benefits Processing | 67% | 33% | 3 | ||||
| Health and Medical | 33% | 67% | 3 | ||||
| Human Resources | 33% | 33% | 33% | 3 | |||
| Procurement and Financial Management | 67% | 33% | 3 | ||||
| International Affairs | 100% | 1 |
High-impact designation by technology
High-impact: 3 (4.8%)Generative AI
Presumed high-impact, determined not: 5 (8.1%)Generative AI
Not high-impact: 54 (87.1%)
High-impact: 1 (2.2%)Classical / predictive ML
Presumed high-impact, determined not: 20 (44.4%)Classical / predictive ML
Not high-impact: 24 (53.3%)
High-impact: 4 (14.3%)Natural language processing
Presumed high-impact, determined not: 5 (17.9%)Natural language processing
Not high-impact: 19 (67.9%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Classical / predictive ML | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Natural language processing | 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 |
| Computer vision | 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 |
| Agentic AI | 0 | – | – | – | – | – |
| Not reported | 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 |
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
63 of 199 records name a vendor (32%)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 25% 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.