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: 198 (26.4%)Generative AI
Pilot: 136 (18.1%)Generative AI
Pre-deployment: 413 (55.1%)Generative AI
Retired: 3 (0.4%)
Deployed: 304 (29.5%)Classical / predictive ML
Pilot: 150 (14.6%)Classical / predictive ML
Pre-deployment: 575 (55.8%)Classical / predictive ML
Retired: 1 (0.1%)
Deployed: 174 (41.0%)Natural language processing
Pilot: 60 (14.2%)Natural language processing
Pre-deployment: 188 (44.3%)Natural language processing
Retired: 2 (0.5%)
Deployed: 122 (48.6%)Computer vision
Pilot: 29 (11.6%)Computer vision
Pre-deployment: 99 (39.4%)Computer vision
Retired: 1 (0.4%)
Sourcing by technology
Where reportedDeveloped in-house: 106 (14.1%)Generative AI
Contract and in-house: 128 (17.1%)Generative AI
Purchased from vendor: 129 (17.2%)Generative AI
Not reported: 387 (51.6%)
Developed in-house: 264 (25.6%)Classical / predictive ML
Contract and in-house: 104 (10.1%)Classical / predictive ML
Purchased from vendor: 107 (10.4%)Classical / predictive ML
Not reported: 555 (53.9%)
Developed in-house: 95 (22.4%)Natural language processing
Contract and in-house: 79 (18.6%)Natural language processing
Purchased from vendor: 61 (14.4%)Natural language processing
Not reported: 189 (44.6%)
Developed in-house: 28 (11.2%)Computer vision
Contract and in-house: 25 (10.0%)Computer vision
Purchased from vendor: 100 (39.8%)Computer vision
Not reported: 98 (39.0%)
Developed in-house: 16 (14.0%)Agentic AI
Contract and in-house: 14 (12.3%)Agentic AI
Purchased from vendor: 11 (9.6%)Agentic AI
Not reported: 73 (64.0%)
Developed in-house: 4 (33.3%)Reinforcement learning
Contract and in-house: 2 (16.7%)Reinforcement learning
Purchased from vendor: 1 (8.3%)Reinforcement learning
Not reported: 5 (41.7%)
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 | 31% | 19% | 26% | 4% | 6% | 2% | 11% | 447 | |
| NASA | 9% | 66% | 6% | 11% | 5% | 1% | 1% | 0% | 425 |
| VA | 15% | 21% | 16% | 22% | 7% | 0% | 20% | 367 | |
| DOE | 35% | 37% | 5% | 3% | 4% | 2% | 8% | 6% | 251 |
| DOJ | 33% | 32% | 20% | 6% | 1% | 8% | 239 | ||
| DOI | 6% | 68% | 6% | 8% | 2% | 0% | 3% | 5% | 238 |
| DOC | 100% | 223 | |||||||
| DHS | 31% | 23% | 14% | 15% | 1% | 17% | 199 | ||
| USDA | 20% | 48% | 9% | 10% | 2% | 12% | 161 | ||
| TREAS | 61% | 13% | 12% | 2% | 2% | 2% | 9% | 127 | |
| DOT | 21% | 7% | 4% | 6% | 61% | 70 | |||
| SEC | 40% | 23% | 3% | 7% | 5% | 2% | 20% | 60 | |
| TVA | 100% | 59 | |||||||
| STATE | 29% | 9% | 17% | 2% | 3% | 40% | 58 | ||
| ED | 100% | 56 | |||||||
| FDIC | 14% | 30% | 18% | 4% | 6% | 28% | 50 |
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 | |
|---|---|---|---|---|---|---|---|---|---|
| Science | 8% | 70% | 6% | 10% | 4% | 1% | 1% | 748 | |
| Administrative Functions | 47% | 19% | 21% | 5% | 3% | 0% | 3% | 1% | 401 |
| Information Technology | 52% | 20% | 14% | 2% | 8% | 2% | 3% | 333 | |
| Health and Medical | 17% | 23% | 27% | 29% | 4% | 1% | 264 | ||
| Other | 32% | 29% | 24% | 3% | 1% | 0% | 9% | 0% | 214 |
| Law Enforcement | 24% | 36% | 20% | 17% | 2% | 1% | 190 | ||
| Service Delivery | 38% | 22% | 30% | 5% | 2% | 1% | 1% | 129 | |
| Energy and the Environment | 13% | 36% | 2% | 7% | 2% | 2% | 4% | 33% | 123 |
| Procurement and Financial Management | 41% | 33% | 17% | 1% | 8% | 83 | |||
| Transportation | 13% | 20% | 9% | 8% | 4% | 1% | 45% | 76 | |
| Government Benefits Processing | 35% | 19% | 19% | 8% | 19% | 48 | |||
| Cybersecurity | 27% | 53% | 9% | 9% | 2% | 45 | |||
| Human Resources | 55% | 18% | 16% | 7% | 2% | 2% | 44 | ||
| Emergency Management | 12% | 48% | 24% | 9% | 3% | 3% | 33 |
High-impact designation by technology
High-impact: 45 (6.0%)Generative AI
Presumed high-impact, determined not: 19 (2.5%)Generative AI
Not high-impact: 648 (86.4%)Generative AI
Not reported: 38 (5.1%)
High-impact: 80 (7.8%)Classical / predictive ML
Presumed high-impact, determined not: 49 (4.8%)Classical / predictive ML
Not high-impact: 895 (86.9%)Classical / predictive ML
Not reported: 6 (0.6%)
High-impact: 37 (8.7%)Natural language processing
Presumed high-impact, determined not: 12 (2.8%)Natural language processing
Not high-impact: 357 (84.2%)Natural language processing
Not reported: 18 (4.2%)
High-impact: 77 (30.7%)Computer vision
Presumed high-impact, determined not: 23 (9.2%)Computer vision
Not high-impact: 147 (58.6%)Computer vision
Not reported: 4 (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 | 45 | 0 / 45 | 0 / 45 | 0 / 45 | 0 / 45 | 0 / 45 |
| Classical / predictive ML | 80 | 0 / 80 | 0 / 80 | 0 / 80 | 0 / 80 | 0 / 80 |
| Natural language processing | 37 | 0 / 37 | 0 / 37 | 0 / 37 | 0 / 37 | 0 / 37 |
| Computer vision | 77 | 0 / 77 | 0 / 77 | 0 / 77 | 0 / 77 | 0 / 77 |
| Agentic AI | 21 | 0 / 21 | 0 / 21 | 0 / 21 | 0 / 21 | 0 / 21 |
| Reinforcement learning | 0 | – | – | – | – | – |
| Other | 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 |
| Not reported | 55 | 0 / 55 | 0 / 55 | 0 / 55 | 0 / 55 | 0 / 55 |
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
588 of 3,341 records name a vendor (18%)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 20% 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.