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: 42 (75.0%)Generative AI
Pilot: 2 (3.6%)Generative AI
Pre-deployment: 11 (19.6%)Generative AI
Retired: 1 (1.8%)
Deployed: 39 (78.0%)Classical / predictive ML
Pilot: 2 (4.0%)Classical / predictive ML
Pre-deployment: 9 (18.0%)
Sourcing by technology
Where reportedDeveloped in-house: 2 (3.6%)Generative AI
Contract and in-house: 5 (8.9%)Generative AI
Purchased from vendor: 49 (87.5%)
Developed in-house: 5 (10.0%)Classical / predictive ML
Contract and in-house: 5 (10.0%)Classical / predictive ML
Purchased from vendor: 39 (78.0%)Classical / predictive ML
Not reported: 1 (2.0%)
Developed in-house: 2 (9.5%)Natural language processing
Contract and in-house: 4 (19.0%)Natural language processing
Purchased from vendor: 15 (71.4%)
Technology adoption by agency
Row share: each agency's records by classification · 16 largest portfolios in the filterTechnology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Agentic AI | n | |
|---|---|---|---|---|---|---|
| Law Enforcement | 12% | 34% | 22% | 30% | 1% | 73 |
| Information Technology | 91% | 3% | 6% | 34 | ||
| Administrative Functions | 27% | 45% | 9% | 18% | 11 | |
| Service Delivery | 73% | 27% | 11 | |||
| Health and Medical | 13% | 75% | 13% | 8 | ||
| Energy and the Environment | 80% | 20% | 5 | |||
| Cybersecurity | 25% | 75% | 4 | |||
| Other | 67% | 33% | 3 | |||
| Science | 100% | 2 | ||||
| Human Resources | 100% | 1 | ||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 11 (19.6%)Generative AI
Presumed high-impact, determined not: 1 (1.8%)Generative AI
Not high-impact: 44 (78.6%)
High-impact: 36 (72.0%)Classical / predictive ML
Not high-impact: 14 (28.0%)
High-impact: 17 (81.0%)Natural language processing
Not high-impact: 4 (19.0%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 11 | 1 / 11 | 0 / 11 | 0 / 11 | 1 / 11 | 1 / 11 |
| Classical / predictive ML | 36 | 3 / 36 | 0 / 36 | 0 / 36 | 3 / 36 | 2 / 36 |
| Natural language processing | 17 | 2 / 17 | 0 / 17 | 0 / 17 | 0 / 17 | 1 / 17 |
| Computer vision | 25 | 4 / 25 | 0 / 25 | 0 / 25 | 2 / 25 | 2 / 25 |
| Agentic AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
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
125 of 153 records name a vendor (82%)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 92% 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.