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 recordsSourcing by technology
Where reportedDeveloped in-house: 1 (3.3%)Generative AI
Contract and in-house: 4 (13.3%)Generative AI
Purchased from vendor: 25 (83.3%)
Developed in-house: 5 (6.9%)Classical / predictive ML
Contract and in-house: 6 (8.3%)Classical / predictive ML
Purchased from vendor: 61 (84.7%)
Developed in-house: 4 (11.4%)Natural language processing
Contract and in-house: 4 (11.4%)Natural language processing
Purchased from vendor: 27 (77.1%)
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 | 16% | 37% | 25% | 20% | 2% | 104 |
| Administrative Functions | 17% | 54% | 21% | 8% | 24 | |
| Information Technology | 27% | 60% | 13% | 15 | ||
| Health and Medical | 86% | 14% | 7 | |||
| Procurement and Financial Management | 25% | 75% | 4 | |||
| Cybersecurity | 67% | 33% | 3 | |||
| Service Delivery | 67% | 33% | 3 | |||
| Human Resources | 100% | 2 | ||||
| Transportation | 100% | 1 |
High-impact designation by technology
High-impact: 9 (30.0%)Generative AI
Presumed high-impact, determined not: 3 (10.0%)Generative AI
Not high-impact: 18 (60.0%)
High-impact: 32 (44.4%)Classical / predictive ML
Presumed high-impact, determined not: 11 (15.3%)Classical / predictive ML
Not high-impact: 29 (40.3%)
High-impact: 14 (40.0%)Natural language processing
Presumed high-impact, determined not: 3 (8.6%)Natural language processing
Not high-impact: 18 (51.4%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 9 | 0 / 9 | 0 / 9 | 0 / 9 | 0 / 9 | 0 / 9 |
| Classical / predictive ML | 32 | 0 / 32 | 0 / 32 | 0 / 32 | 0 / 32 | 0 / 32 |
| Natural language processing | 14 | 0 / 14 | 0 / 14 | 0 / 14 | 0 / 14 | 0 / 14 |
| Computer vision | 17 | 0 / 17 | 0 / 17 | 0 / 17 | 0 / 17 | 0 / 17 |
| Agentic AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
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
117 of 163 records name a vendor (72%)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 100% 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.