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 reportedTechnology adoption by agency
Row share: each agency's records by classification · 16 largest portfolios in the filter| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| VA | 22% | 11% | 22% | 22% | 22% | 9 | ||
| DOJ | 25% | 63% | 13% | 8 | ||||
| DOI | 20% | 60% | 20% | 5 | ||||
| TREAS | 40% | 40% | 20% | 5 | ||||
| NASA | 25% | 25% | 50% | 4 | ||||
| GSA | 33% | 33% | 33% | 3 | ||||
| HHS | 50% | 50% | 2 | |||||
| SEC | 100% | 1 | ||||||
| NSF | 100% | 1 | ||||||
| FCC | 100% | 1 | ||||||
| FRB | 100% | 1 | ||||||
| FDIC | 100% | 1 | ||||||
| DOT | 100% | 1 | ||||||
| DOL | 100% | 1 | ||||||
| USDA | 100% | 1 | ||||||
| DHS | 100% | 1 |
Technology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| Science | 13% | 25% | 50% | 13% | 8 | |||
| Information Technology | 14% | 29% | 14% | 29% | 14% | 7 | ||
| Administrative Functions | 50% | 17% | 33% | 6 | ||||
| Law Enforcement | 17% | 67% | 17% | 6 | ||||
| Health and Medical | 40% | 40% | 20% | 5 | ||||
| Other | 40% | 20% | 40% | 5 | ||||
| Service Delivery | 33% | 33% | 33% | 3 | ||||
| Government Benefits Processing | 50% | 50% | 2 | |||||
| Procurement and Financial Management | 50% | 50% | 2 | |||||
| Transportation | 100% | 1 |
High-impact designation by technology
High-impact: 1 (8.3%)Generative AI
Not high-impact: 10 (83.3%)Generative AI
Not reported: 1 (8.3%)
High-impact: 1 (11.1%)Classical / predictive ML
Presumed high-impact, determined not: 2 (22.2%)Classical / predictive ML
Not high-impact: 6 (66.7%)
High-impact: 1 (7.7%)Natural language processing
Presumed high-impact, determined not: 1 (7.7%)Natural language processing
Not high-impact: 10 (76.9%)Natural language processing
Not reported: 1 (7.7%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Classical / predictive ML | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Natural language processing | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Computer vision | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Agentic AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Other | 0 | – | – | – | – | – |
| 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
0 of 46 records name a vendor (0%)No records match the current filters.
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 0% 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.