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: 33 (16.6%)Generative AI
Contract and in-house: 72 (36.2%)Generative AI
Purchased from vendor: 85 (42.7%)Generative AI
Not reported: 9 (4.5%)
Developed in-house: 134 (44.1%)Classical / predictive ML
Contract and in-house: 74 (24.3%)Classical / predictive ML
Purchased from vendor: 91 (29.9%)Classical / predictive ML
Not reported: 5 (1.6%)
Developed in-house: 57 (32.8%)Natural language processing
Contract and in-house: 62 (35.6%)Natural language processing
Purchased from vendor: 49 (28.2%)Natural language processing
Not reported: 6 (3.4%)
Developed in-house: 11 (9.0%)Computer vision
Contract and in-house: 17 (13.9%)Computer vision
Purchased from vendor: 92 (75.4%)Computer vision
Not reported: 2 (1.6%)
Developed in-house: 7 (36.8%)Agentic AI
Contract and in-house: 7 (36.8%)Agentic AI
Purchased from vendor: 5 (26.3%)
Developed in-house: 2 (40.0%)Reinforcement learning
Contract and in-house: 2 (40.0%)Reinforcement learning
Purchased from vendor: 1 (20.0%)
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 | Reinforcement learning | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|---|
| HHS | 26% | 28% | 33% | 8% | 4% | 2% | 167 | ||
| VA | 6% | 26% | 17% | 49% | 2% | 138 | |||
| DOJ | 24% | 45% | 22% | 8% | 1% | 88 | |||
| DHS | 28% | 40% | 13% | 19% | 72 | ||||
| DOE | 43% | 19% | 7% | 3% | 3% | 6% | 18% | 67 | |
| DOI | 77% | 13% | 5% | 3% | 3% | 39 | |||
| USDA | 21% | 55% | 16% | 5% | 3% | 38 | |||
| NASA | 50% | 15% | 19% | 15% | 26 | ||||
| TREAS | 28% | 36% | 36% | 25 | |||||
| SEC | 35% | 35% | 10% | 15% | 5% | 20 | |||
| DOL | 35% | 60% | 5% | 20 | |||||
| SSA | 42% | 32% | 11% | 11% | 5% | 19 | |||
| GSA | 35% | 12% | 35% | 12% | 6% | 17 | |||
| STATE | 47% | 12% | 29% | 12% | 17 | ||||
| FRB | 6% | 31% | 63% | 16 | |||||
| FDIC | 6% | 25% | 44% | 13% | 13% | 16 |
Technology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Reinforcement learning | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|---|
| Administrative Functions | 38% | 29% | 22% | 6% | 3% | 1% | 2% | 157 | |
| Health and Medical | 5% | 28% | 22% | 44% | 1% | 147 | |||
| Other | 23% | 34% | 25% | 4% | 1% | 1% | 12% | 97 | |
| Science | 6% | 64% | 8% | 15% | 6% | 1% | 86 | ||
| Law Enforcement | 14% | 40% | 20% | 20% | 2% | 2% | 84 | ||
| Information Technology | 45% | 30% | 13% | 1% | 2% | 7% | 82 | ||
| Service Delivery | 23% | 28% | 45% | 5% | 40 | ||||
| Procurement and Financial Management | 31% | 38% | 23% | 3% | 5% | 39 | |||
| Human Resources | 54% | 21% | 13% | 8% | 4% | 24 | |||
| Energy and the Environment | 45% | 36% | 9% | 9% | 22 | ||||
| Cybersecurity | 30% | 70% | 20 | ||||||
| Transportation | 15% | 15% | 30% | 5% | 5% | 30% | 20 | ||
| Government Benefits Processing | 11% | 32% | 37% | 16% | 5% | 19 | |||
| Emergency Management | 64% | 27% | 9% | 11 |
High-impact designation by technology
High-impact: 1 (0.5%)Generative AI
Presumed high-impact, determined not: 8 (4.0%)Generative AI
Not high-impact: 182 (91.5%)Generative AI
Not reported: 8 (4.0%)
High-impact: 34 (11.2%)Classical / predictive ML
Presumed high-impact, determined not: 33 (10.9%)Classical / predictive ML
Not high-impact: 235 (77.3%)Classical / predictive ML
Not reported: 2 (0.7%)
High-impact: 7 (4.0%)Natural language processing
Presumed high-impact, determined not: 5 (2.9%)Natural language processing
Not high-impact: 156 (89.7%)Natural language processing
Not reported: 6 (3.4%)
High-impact: 58 (47.5%)Computer vision
Presumed high-impact, determined not: 14 (11.5%)Computer vision
Not high-impact: 48 (39.3%)Computer vision
Not reported: 2 (1.6%)
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 | 34 | 0 / 34 | 0 / 34 | 0 / 34 | 0 / 34 | 0 / 34 |
| Natural language processing | 7 | 0 / 7 | 0 / 7 | 0 / 7 | 0 / 7 | 0 / 7 |
| Computer vision | 58 | 0 / 58 | 0 / 58 | 0 / 58 | 0 / 58 | 0 / 58 |
| Agentic AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Reinforcement learning | 0 | – | – | – | – | – |
| 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
409 of 857 records name a vendor (48%)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 56% 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.