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: 38 (62.3%)Generative AI
Pilot: 19 (31.1%)Generative AI
Pre-deployment: 3 (4.9%)Generative AI
Retired: 1 (1.6%)
Deployed: 8 (80.0%)Classical / predictive ML
Pilot: 1 (10.0%)Classical / predictive ML
Pre-deployment: 1 (10.0%)
Sourcing by technology
Where reportedDeveloped in-house: 9 (14.8%)Generative AI
Contract and in-house: 26 (42.6%)Generative AI
Purchased from vendor: 26 (42.6%)
Developed in-house: 1 (10.0%)Classical / predictive ML
Contract and in-house: 3 (30.0%)Classical / predictive ML
Purchased from vendor: 6 (60.0%)
Contract and in-house: 6 (54.5%)Natural language processing
Purchased from vendor: 5 (45.5%)
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 | |
|---|---|---|---|---|---|---|---|---|
| DOE | 74% | 4% | 9% | 4% | 4% | 4% | 23 | |
| DOJ | 50% | 33% | 8% | 8% | 12 | |||
| HHS | 75% | 25% | 8 | |||||
| DHS | 83% | 17% | 6 | |||||
| STATE | 50% | 33% | 17% | 6 | ||||
| SSA | 80% | 20% | 5 | |||||
| FTC | 100% | 5 | ||||||
| DOL | 100% | 4 | ||||||
| DOI | 25% | 50% | 25% | 4 | ||||
| USDA | 67% | 33% | 3 | |||||
| EPA | 67% | 33% | 3 | |||||
| HUD | 50% | 50% | 2 | |||||
| EAC | 100% | 2 | ||||||
| NRC | 100% | 1 | ||||||
| FHFA | 100% | 1 | ||||||
| NTSB | 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 | 68% | 8% | 4% | 16% | 4% | 25 | ||
| Information Technology | 70% | 13% | 9% | 4% | 4% | 23 | ||
| Other | 80% | 10% | 10% | 10 | ||||
| Energy and the Environment | 100% | 7 | ||||||
| Science | 43% | 29% | 29% | 7 | ||||
| Law Enforcement | 60% | 40% | 5 | |||||
| Government Benefits Processing | 67% | 33% | 3 | |||||
| Human Resources | 33% | 67% | 3 | |||||
| Procurement and Financial Management | 100% | 2 | ||||||
| Cybersecurity | 100% | 1 | ||||||
| Emergency Management | 100% | 1 | ||||||
| Health and Medical | 100% | 1 | ||||||
| Service Delivery | 100% | 1 | ||||||
| Transportation | 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 | 0 | – | – | – | – | – |
| Classical / predictive ML | 0 | – | – | – | – | – |
| Natural language processing | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| 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
90 of 90 records name a vendor (100%)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 81% 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.