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: 12 (37.5%)Generative AI
Pilot: 7 (21.9%)Generative AI
Pre-deployment: 12 (37.5%)Generative AI
Retired: 1 (3.1%)
Deployed: 12 (50.0%)Classical / predictive ML
Pilot: 3 (12.5%)Classical / predictive ML
Pre-deployment: 9 (37.5%)
Deployed: 16 (48.5%)Natural language processing
Pilot: 4 (12.1%)Natural language processing
Pre-deployment: 13 (39.4%)
Deployed: 17 (65.4%)Computer vision
Pilot: 3 (11.5%)Computer vision
Pre-deployment: 6 (23.1%)
Sourcing by technology
Where reportedDeveloped in-house: 3 (9.4%)Generative AI
Contract and in-house: 13 (40.6%)Generative AI
Purchased from vendor: 4 (12.5%)Generative AI
Not reported: 12 (37.5%)
Developed in-house: 3 (12.5%)Classical / predictive ML
Contract and in-house: 6 (25.0%)Classical / predictive ML
Purchased from vendor: 6 (25.0%)Classical / predictive ML
Not reported: 9 (37.5%)
Developed in-house: 6 (18.2%)Natural language processing
Contract and in-house: 9 (27.3%)Natural language processing
Purchased from vendor: 5 (15.2%)Natural language processing
Not reported: 13 (39.4%)
Developed in-house: 2 (7.7%)Computer vision
Contract and in-house: 8 (30.8%)Computer vision
Purchased from vendor: 10 (38.5%)Computer vision
Not reported: 6 (23.1%)
Technology 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 | |
|---|---|---|---|---|---|---|---|---|
| HHS | 33% | 17% | 27% | 10% | 10% | 3% | 30 | |
| DOJ | 21% | 42% | 8% | 25% | 4% | 24 | ||
| VA | 13% | 13% | 40% | 20% | 13% | 15 | ||
| DHS | 9% | 18% | 18% | 45% | 9% | 11 | ||
| DOI | 11% | 56% | 11% | 11% | 11% | 9 | ||
| TREAS | 43% | 14% | 29% | 14% | 7 | |||
| NASA | 20% | 20% | 60% | 5 | ||||
| USDA | 25% | 50% | 25% | 4 | ||||
| SEC | 33% | 67% | 3 | |||||
| GSA | 33% | 33% | 33% | 3 | ||||
| DOL | 67% | 33% | 3 | |||||
| FRB | 100% | 2 | ||||||
| FDIC | 100% | 2 | ||||||
| EPA | 50% | 50% | 2 | |||||
| SSA | 100% | 2 | ||||||
| SBA | 100% | 2 |
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 | |
|---|---|---|---|---|---|---|---|---|
| Administrative Functions | 23% | 19% | 27% | 31% | 26 | |||
| Health and Medical | 33% | 19% | 33% | 10% | 5% | 21 | ||
| Law Enforcement | 20% | 30% | 10% | 40% | 20 | |||
| Other | 23% | 8% | 38% | 8% | 15% | 8% | 13 | |
| Information Technology | 25% | 25% | 17% | 17% | 17% | 12 | ||
| Science | 18% | 18% | 45% | 9% | 9% | 11 | ||
| Government Benefits Processing | 22% | 33% | 33% | 11% | 9 | |||
| Service Delivery | 50% | 17% | 17% | 17% | 6 | |||
| Procurement and Financial Management | 33% | 33% | 33% | 3 | ||||
| Transportation | 50% | 50% | 2 | |||||
| Emergency Management | 100% | 1 | ||||||
| Human Resources | 100% | 1 |
High-impact designation by technology
High-impact: 3 (9.4%)Generative AI
Not high-impact: 28 (87.5%)Generative AI
Not reported: 1 (3.1%)
High-impact: 3 (12.5%)Classical / predictive ML
Presumed high-impact, determined not: 2 (8.3%)Classical / predictive ML
Not high-impact: 19 (79.2%)
High-impact: 5 (15.2%)Natural language processing
Presumed high-impact, determined not: 1 (3.0%)Natural language processing
Not high-impact: 26 (78.8%)Natural language processing
Not reported: 1 (3.0%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Classical / predictive ML | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Natural language processing | 5 | 0 / 5 | 0 / 5 | 0 / 5 | 0 / 5 | 0 / 5 |
| Computer vision | 7 | 0 / 7 | 0 / 7 | 0 / 7 | 0 / 7 | 0 / 7 |
| 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
55 of 131 records name a vendor (42%)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 43% 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.