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: 2 (13.3%)Classical / predictive ML
Contract and in-house: 5 (33.3%)Classical / predictive ML
Purchased from vendor: 3 (20.0%)Classical / predictive ML
Not reported: 5 (33.3%)
Developed in-house: 2 (25.0%)Natural language processing
Contract and in-house: 1 (12.5%)Natural language processing
Purchased from vendor: 1 (12.5%)Natural language processing
Not reported: 4 (50.0%)
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 | Not reported | n | |
|---|---|---|---|---|---|---|---|
| Government Benefits Processing | 10% | 50% | 20% | 20% | 10 | ||
| Law Enforcement | 17% | 50% | 17% | 17% | 6 | ||
| Information Technology | 20% | 20% | 20% | 40% | 5 | ||
| Administrative Functions | 25% | 25% | 50% | 4 | |||
| Other | 67% | 33% | 3 | ||||
| Service Delivery | 67% | 33% | 3 | ||||
| Health and Medical | 50% | 50% | 2 | ||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 9 (60.0%)Classical / predictive ML
Presumed high-impact, determined not: 1 (6.7%)Classical / predictive ML
Not high-impact: 5 (33.3%)
High-impact: 3 (37.5%)Natural language processing
Not high-impact: 5 (62.5%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 |
| Classical / predictive ML | 9 | 4 / 9 | 3 / 9 | 3 / 9 | 4 / 9 | 3 / 9 |
| Natural language processing | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Computer vision | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Agentic AI | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Not reported | 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
7 of 35 records name a vendor (20%)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 26% 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.