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: 3 (4.8%)Generative AI
Contract and in-house: 2 (3.2%)Generative AI
Purchased from vendor: 3 (4.8%)Generative AI
Not reported: 55 (87.3%)
Developed in-house: 1 (25.0%)Classical / predictive ML
Not reported: 3 (75.0%)
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 | 37% | 11% | 32% | 21% | 19 | |||
| VA | 45% | 9% | 18% | 9% | 9% | 9% | 11 | |
| DOJ | 70% | 20% | 10% | 10 | ||||
| NASA | 88% | 13% | 8 | |||||
| DHS | 50% | 50% | 8 | |||||
| GSA | 33% | 67% | 6 | |||||
| USDA | 100% | 5 | ||||||
| TREAS | 100% | 5 | ||||||
| DOE | 100% | 5 | ||||||
| SEC | 100% | 4 | ||||||
| DOT | 50% | 25% | 25% | 4 | ||||
| SBA | 75% | 25% | 4 | |||||
| DOI | 67% | 33% | 3 | |||||
| HUD | 100% | 1 | ||||||
| NEA | 100% | 1 | ||||||
| DOL | 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 | |
|---|---|---|---|---|---|---|---|---|
| Information Technology | 58% | 8% | 23% | 12% | 26 | |||
| Administrative Functions | 76% | 4% | 16% | 4% | 25 | |||
| Service Delivery | 47% | 40% | 7% | 7% | 15 | |||
| Science | 75% | 25% | 8 | |||||
| Other | 86% | 14% | 7 | |||||
| Law Enforcement | 25% | 50% | 25% | 4 | ||||
| Health and Medical | 33% | 33% | 33% | 3 | ||||
| Procurement and Financial Management | 100% | 3 | ||||||
| Transportation | 67% | 33% | 3 | |||||
| Cybersecurity | 50% | 50% | 2 | |||||
| Emergency Management | 100% | 1 | ||||||
| Human Resources | 100% | 1 |
High-impact designation by technology
High-impact: 1 (1.6%)Generative AI
Not high-impact: 60 (95.2%)Generative AI
Not reported: 2 (3.2%)
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 | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| 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
5 of 98 records name a vendor (5%)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 5% 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.