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: 7 (15.6%)Generative AI
Purchased from vendor: 2 (4.4%)Generative AI
Not reported: 36 (80.0%)
Purchased from vendor: 1 (20.0%)Classical / predictive ML
Not reported: 4 (80.0%)
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 | n | |
|---|---|---|---|---|---|---|---|---|
| HHS | 60% | 20% | 20% | 10 | ||||
| VA | 40% | 20% | 10% | 20% | 10% | 10 | ||
| NASA | 67% | 11% | 11% | 11% | 9 | |||
| TREAS | 100% | 8 | ||||||
| DOE | 75% | 13% | 13% | 8 | ||||
| DHS | 60% | 40% | 5 | |||||
| SEC | 75% | 25% | 4 | |||||
| DOI | 25% | 25% | 50% | 4 | ||||
| NARA | 100% | 3 | ||||||
| GSA | 100% | 2 | ||||||
| USDA | 100% | 2 | ||||||
| DOJ | 50% | 50% | 2 | |||||
| FRB | 100% | 1 | ||||||
| FDIC | 100% | 1 | ||||||
| DOL | 100% | 1 | ||||||
| EPA | 100% | 1 |
Technology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Reinforcement learning | Other | n | |
|---|---|---|---|---|---|---|---|---|
| Administrative Functions | 71% | 14% | 7% | 7% | 14 | |||
| Information Technology | 75% | 8% | 8% | 8% | 12 | |||
| Other | 45% | 45% | 9% | 11 | ||||
| Science | 60% | 20% | 10% | 10% | 10 | |||
| Service Delivery | 56% | 33% | 11% | 9 | ||||
| Law Enforcement | 60% | 20% | 20% | 5 | ||||
| Procurement and Financial Management | 100% | 4 | ||||||
| Health and Medical | 50% | 50% | 2 | |||||
| Human Resources | 100% | 2 | ||||||
| Cybersecurity | 100% | 1 | ||||||
| Emergency Management | 100% | 1 | ||||||
| Energy and the Environment | 100% | 1 |
High-impact designation by technology
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 | 0 | – | – | – | – | – |
| Natural language processing | 0 | – | – | – | – | – |
| Computer vision | 0 | – | – | – | – | – |
| Agentic AI | 0 | – | – | – | – | – |
| Reinforcement learning | 0 | – | – | – | – | – |
| Other | 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
2 of 72 records name a vendor (3%)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 13% 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.