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: 11 (21.6%)Generative AI
Pilot: 15 (29.4%)Generative AI
Pre-deployment: 25 (49.0%)
Sourcing by technology
Where reportedDeveloped in-house: 4 (7.8%)Generative AI
Contract and in-house: 4 (7.8%)Generative AI
Purchased from vendor: 26 (51.0%)Generative AI
Not reported: 17 (33.3%)
Purchased from vendor: 1 (33.3%)Classical / predictive ML
Not reported: 2 (66.7%)
Developed in-house: 1 (10.0%)Natural language processing
Contract and in-house: 1 (10.0%)Natural language processing
Purchased from vendor: 2 (20.0%)Natural language processing
Not reported: 6 (60.0%)
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 | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| DOE | 81% | 10% | 5% | 5% | 21 | |||
| ED | 100% | 12 | ||||||
| HHS | 55% | 36% | 9% | 11 | ||||
| DOC | 100% | 6 | ||||||
| SBA | 100% | 6 | ||||||
| DOJ | 60% | 20% | 20% | 5 | ||||
| DHS | 60% | 20% | 20% | 5 | ||||
| NASA | 50% | 25% | 25% | 4 | ||||
| VA | 50% | 25% | 25% | 4 | ||||
| TREAS | 100% | 4 | ||||||
| DOT | 33% | 67% | 3 | |||||
| NSF | 100% | 2 | ||||||
| TVA | 100% | 2 | ||||||
| SSA | 50% | 50% | 2 | |||||
| NIGC | 100% | 1 | ||||||
| HUD | 100% | 1 |
Technology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| Information Technology | 74% | 3% | 12% | 9% | 3% | 34 | ||
| Administrative Functions | 72% | 11% | 6% | 6% | 6% | 18 | ||
| Science | 67% | 17% | 17% | 6 | ||||
| Other | 60% | 40% | 5 | |||||
| Service Delivery | 50% | 50% | 4 | |||||
| Cybersecurity | 100% | 2 | ||||||
| Transportation | 100% | 2 | ||||||
| Government Benefits Processing | 100% | 1 | ||||||
| Health and Medical | 100% | 1 | ||||||
| Law Enforcement | 100% | 1 |
High-impact designation by technology
High-impact: 3 (5.9%)Generative AI
Presumed high-impact, determined not: 1 (2.0%)Generative AI
Not high-impact: 46 (90.2%)Generative AI
Not reported: 1 (2.0%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 3 | 0 / 3 | 2 / 3 | 2 / 3 | 0 / 3 | 2 / 3 |
| Classical / predictive ML | 0 | – | – | – | – | – |
| Natural language processing | 0 | – | – | – | – | – |
| Computer vision | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Agentic AI | 0 | – | – | – | – | – |
| Other | 0 | – | – | – | – | – |
| 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
37 of 96 records name a vendor (39%)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 33% 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.