Technology landscape
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 recordsDeployedPilotPre-deploymentRetiredNot reported
Generative AI
Generative AI
Deployed: 15 (25.0%)Generative AI
Pilot: 11 (18.3%)Generative AI
Pre-deployment: 34 (56.7%)
60Deployed: 15 (25.0%)Generative AI
Pilot: 11 (18.3%)Generative AI
Pre-deployment: 34 (56.7%)
Classical / predictive ML
Classical / predictive ML
Deployed: 86 (65.2%)Classical / predictive ML
Pilot: 6 (4.5%)Classical / predictive ML
Pre-deployment: 40 (30.3%)
132Deployed: 86 (65.2%)Classical / predictive ML
Pilot: 6 (4.5%)Classical / predictive ML
Pre-deployment: 40 (30.3%)
Natural language processing
Natural language processing
Deployed: 26 (46.4%)Natural language processing
Pilot: 3 (5.4%)Natural language processing
Pre-deployment: 27 (48.2%)
56Deployed: 26 (46.4%)Natural language processing
Pilot: 3 (5.4%)Natural language processing
Pre-deployment: 27 (48.2%)
Sourcing by technology
Where reportedDeveloped in-houseContract and in-housePurchased from vendorNot reported
Generative AI
Generative AI
Developed in-house: 3 (5.0%)Generative AI
Contract and in-house: 8 (13.3%)Generative AI
Purchased from vendor: 17 (28.3%)Generative AI
Not reported: 32 (53.3%)
60Developed in-house: 3 (5.0%)Generative AI
Contract and in-house: 8 (13.3%)Generative AI
Purchased from vendor: 17 (28.3%)Generative AI
Not reported: 32 (53.3%)
Classical / predictive ML
Classical / predictive ML
Developed in-house: 24 (18.2%)Classical / predictive ML
Contract and in-house: 15 (11.4%)Classical / predictive ML
Purchased from vendor: 50 (37.9%)Classical / predictive ML
Not reported: 43 (32.6%)
132Developed in-house: 24 (18.2%)Classical / predictive ML
Contract and in-house: 15 (11.4%)Classical / predictive ML
Purchased from vendor: 50 (37.9%)Classical / predictive ML
Not reported: 43 (32.6%)
Natural language processing
Natural language processing
Developed in-house: 2 (3.6%)Natural language processing
Contract and in-house: 9 (16.1%)Natural language processing
Purchased from vendor: 18 (32.1%)Natural language processing
Not reported: 27 (48.2%)
56Developed in-house: 2 (3.6%)Natural language processing
Contract and in-house: 9 (16.1%)Natural language processing
Purchased from vendor: 18 (32.1%)Natural language processing
Not reported: 27 (48.2%)
Computer vision
Computer vision
Developed in-house: 2 (1.7%)Computer vision
Contract and in-house: 12 (10.2%)Computer vision
Purchased from vendor: 85 (72.0%)Computer vision
Not reported: 19 (16.1%)
118Developed in-house: 2 (1.7%)Computer vision
Contract and in-house: 12 (10.2%)Computer vision
Purchased from vendor: 85 (72.0%)Computer vision
Not reported: 19 (16.1%)
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 | |
|---|---|---|---|---|---|---|---|---|
| VA | 6% | 24% | 11% | 31% | 8% | 19% | 215 | |
| DOJ | 18% | 36% | 19% | 18% | 1% | 8% | 114 | |
| DHS | 7% | 24% | 15% | 47% | 7% | 55 | ||
| DOE | 59% | 24% | 3% | 7% | 7% | 29 | ||
| SSA | 22% | 67% | 11% | 9 | ||||
| NCUA | 100% | 5 | ||||||
| USDA | 75% | 25% | 4 | |||||
| TREAS | 50% | 25% | 25% | 4 | ||||
| STATE | 33% | 33% | 33% | 3 | ||||
| HHS | 50% | 50% | 2 | |||||
| EPA | 100% | 2 | ||||||
| NASA | 100% | 1 | ||||||
| FDIC | 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 | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| Health and Medical | 7% | 30% | 12% | 46% | 4% | 140 | ||
| Law Enforcement | 14% | 32% | 19% | 32% | 2% | 130 | ||
| Government Benefits Processing | 14% | 34% | 21% | 3% | 28% | 29 | ||
| Administrative Functions | 28% | 39% | 17% | 17% | 18 | |||
| Information Technology | 58% | 25% | 8% | 8% | 12 | |||
| Science | 36% | 45% | 18% | 11 | ||||
| Other | 33% | 22% | 11% | 11% | 22% | 9 | ||
| Human Resources | 50% | 38% | 13% | 8 | ||||
| Cybersecurity | 14% | 57% | 14% | 14% | 7 | |||
| Procurement and Financial Management | 86% | 14% | 7 | |||||
| Service Delivery | 14% | 57% | 14% | 14% | 7 | |||
| Transportation | 14% | 29% | 57% | 7 | ||||
| Energy and the Environment | 40% | 40% | 20% | 5 |
High-impact designation by technology
High-impactPresumed high-impact, determined notNot high-impactNot reported
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 60 | 3 / 60 | 4 / 60 | 4 / 60 | 2 / 60 | 5 / 60 |
| Classical / predictive ML | 132 | 19 / 132 | 16 / 132 | 16 / 132 | 19 / 132 | 18 / 132 |
| Natural language processing | 56 | 4 / 56 | 2 / 56 | 2 / 56 | 2 / 56 | 2 / 56 |
| Computer vision | 118 | 19 / 118 | 15 / 118 | 15 / 118 | 17 / 118 | 17 / 118 |
| Agentic AI | 22 | 0 / 22 | 0 / 22 | 0 / 22 | 0 / 22 | 0 / 22 |
| Other | 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 |
| Not reported | 55 | 0 / 55 | 0 / 55 | 0 / 55 | 0 / 55 | 0 / 55 |
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
115 of 445 records name a vendor (26%)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 31% 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.