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: 53 (34.4%)Generative AI
Contract and in-house: 51 (33.1%)Generative AI
Purchased from vendor: 49 (31.8%)Generative AI
Not reported: 1 (0.6%)
Developed in-house: 115 (74.2%)Classical / predictive ML
Contract and in-house: 22 (14.2%)Classical / predictive ML
Purchased from vendor: 18 (11.6%)
Developed in-house: 35 (57.4%)Natural language processing
Contract and in-house: 13 (21.3%)Natural language processing
Purchased from vendor: 11 (18.0%)Natural language processing
Not reported: 2 (3.3%)
Developed in-house: 13 (43.3%)Computer vision
Contract and in-house: 8 (26.7%)Computer vision
Purchased from vendor: 8 (26.7%)Computer vision
Not reported: 1 (3.3%)
Developed in-house: 7 (38.9%)Agentic AI
Contract and in-house: 6 (33.3%)Agentic AI
Purchased from vendor: 5 (27.8%)
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 | |
|---|---|---|---|---|---|---|---|---|---|
| DOI | 7% | 71% | 1% | 15% | 3% | 3% | 89 | ||
| HHS | 43% | 14% | 32% | 1% | 8% | 2% | 88 | ||
| DOE | 54% | 23% | 2% | 2% | 7% | 12% | 57 | ||
| USDA | 6% | 70% | 9% | 13% | 2% | 47 | |||
| DOJ | 60% | 20% | 12% | 8% | 25 | ||||
| TREAS | 59% | 14% | 18% | 5% | 5% | 22 | |||
| NASA | 24% | 52% | 10% | 5% | 10% | 21 | |||
| VA | 38% | 29% | 29% | 5% | 21 | ||||
| STATE | 56% | 11% | 33% | 9 | |||||
| DHS | 50% | 13% | 38% | 8 | |||||
| FRB | 14% | 57% | 29% | 7 | |||||
| EPA | 57% | 14% | 29% | 7 | |||||
| SEC | 80% | 20% | 5 | ||||||
| NSF | 100% | 4 | |||||||
| SSA | 100% | 4 | |||||||
| SBA | 75% | 25% | 4 |
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 | 10% | 72% | 4% | 9% | 1% | 1% | 3% | 147 | |
| Administrative Functions | 53% | 14% | 23% | 6% | 3% | 1% | 70 | ||
| Information Technology | 65% | 10% | 14% | 2% | 10% | 63 | |||
| Other | 36% | 17% | 24% | 5% | 2% | 17% | 42 | ||
| Health and Medical | 48% | 14% | 28% | 7% | 3% | 29 | |||
| Law Enforcement | 53% | 16% | 5% | 26% | 19 | ||||
| Energy and the Environment | 13% | 53% | 7% | 20% | 7% | 15 | |||
| Service Delivery | 53% | 13% | 7% | 13% | 7% | 7% | 15 | ||
| Emergency Management | 22% | 44% | 22% | 11% | 9 | ||||
| Procurement and Financial Management | 13% | 38% | 38% | 13% | 8 | ||||
| Cybersecurity | 33% | 33% | 17% | 17% | 6 | ||||
| Human Resources | 50% | 50% | 6 | ||||||
| Government Benefits Processing | 100% | 4 | |||||||
| International Affairs | 100% | 1 |
High-impact designation by technology
High-impact: 11 (7.1%)Generative AI
Presumed high-impact, determined not: 3 (1.9%)Generative AI
Not high-impact: 139 (90.3%)Generative AI
Not reported: 1 (0.6%)
High-impact: 6 (3.9%)Classical / predictive ML
Presumed high-impact, determined not: 3 (1.9%)Classical / predictive ML
Not high-impact: 146 (94.2%)
High-impact: 3 (4.9%)Natural language processing
Presumed high-impact, determined not: 1 (1.6%)Natural language processing
Not high-impact: 55 (90.2%)Natural language processing
Not reported: 2 (3.3%)
High-impact: 1 (3.3%)Computer vision
Presumed high-impact, determined not: 2 (6.7%)Computer vision
Not high-impact: 27 (90.0%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 11 | 1 / 11 | 1 / 11 | 1 / 11 | 0 / 11 | 1 / 11 |
| Classical / predictive ML | 6 | 0 / 6 | 0 / 6 | 0 / 6 | 0 / 6 | 0 / 6 |
| 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 | 0 | – | – | – | – | – |
| Reinforcement learning | 0 | – | – | – | – | – |
| Other | 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 |
| 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
169 of 440 records name a vendor (38%)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 38% 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.