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: 41 (74.5%)Generative AI
Pilot: 2 (3.6%)Generative AI
Pre-deployment: 11 (20.0%)Generative AI
Retired: 1 (1.8%)
Deployed: 38 (77.6%)Classical / predictive ML
Pilot: 2 (4.1%)Classical / predictive ML
Pre-deployment: 9 (18.4%)
Sourcing by technology
Where reportedDeveloped in-house: 2 (3.6%)Generative AI
Contract and in-house: 4 (7.3%)Generative AI
Purchased from vendor: 49 (89.1%)
Developed in-house: 5 (10.2%)Classical / predictive ML
Contract and in-house: 4 (8.2%)Classical / predictive ML
Purchased from vendor: 39 (79.6%)Classical / predictive ML
Not reported: 1 (2.0%)
Developed in-house: 2 (9.1%)Natural language processing
Contract and in-house: 3 (13.6%)Natural language processing
Purchased from vendor: 17 (77.3%)
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 | Other | n | |
|---|---|---|---|---|---|---|---|
| Law Enforcement | 13% | 35% | 22% | 28% | 1% | 68 | |
| Information Technology | 89% | 3% | 8% | 36 | |||
| Service Delivery | 67% | 33% | 12 | ||||
| Administrative Functions | 30% | 40% | 10% | 10% | 10% | 10 | |
| Health and Medical | 86% | 14% | 7 | ||||
| Energy and the Environment | 80% | 20% | 5 | ||||
| Cybersecurity | 25% | 75% | 4 | ||||
| Other | 33% | 33% | 33% | 3 | |||
| Science | 100% | 2 | |||||
| Human Resources | 100% | 1 | |||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 34 (69.4%)Classical / predictive ML
Not high-impact: 15 (30.6%)
High-impact: 16 (72.7%)Natural language processing
Not high-impact: 6 (27.3%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 10 | 0 / 10 | 0 / 10 | 0 / 10 | 0 / 10 | 0 / 10 |
| Classical / predictive ML | 34 | 1 / 34 | 0 / 34 | 0 / 34 | 1 / 34 | 0 / 34 |
| Natural language processing | 16 | 1 / 16 | 0 / 16 | 0 / 16 | 0 / 16 | 0 / 16 |
| Computer vision | 21 | 2 / 21 | 0 / 21 | 0 / 21 | 0 / 21 | 0 / 21 |
| Agentic AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| 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
122 of 149 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 93% 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.