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: 9 (21.4%)Classical / predictive ML
Pilot: 10 (23.8%)Classical / predictive ML
Pre-deployment: 23 (54.8%)
Sourcing by technology
Where reportedDeveloped in-house: 12 (28.6%)Classical / predictive ML
Contract and in-house: 5 (11.9%)Classical / predictive ML
Purchased from vendor: 3 (7.1%)Classical / predictive ML
Not reported: 22 (52.4%)
Contract and in-house: 1 (25.0%)Natural language processing
Purchased from vendor: 2 (50.0%)Natural language processing
Not reported: 1 (25.0%)
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 | Not reported | n | |
|---|---|---|---|---|---|---|---|---|
| Science | 2% | 54% | 41% | 2% | 2% | 56 | ||
| Law Enforcement | 6% | 13% | 25% | 56% | 16 | |||
| Health and Medical | 100% | 12 | ||||||
| Energy and the Environment | 70% | 30% | 10 | |||||
| Service Delivery | 67% | 33% | 3 | |||||
| Other | 50% | 50% | 2 | |||||
| Administrative Functions | 100% | 1 | ||||||
| Emergency Management | 100% | 1 | ||||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 2 (4.8%)Classical / predictive ML
Presumed high-impact, determined not: 1 (2.4%)Classical / predictive ML
Not high-impact: 39 (92.9%)
High-impact: 3 (75.0%)Natural language processing
Presumed high-impact, determined not: 1 (25.0%)
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 | 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 | 0 / 2 |
| Natural language processing | 3 | 1 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Computer vision | 13 | 0 / 13 | 0 / 13 | 0 / 13 | 0 / 13 | 0 / 13 |
| Agentic AI | 0 | – | – | – | – | – |
| Other | 0 | – | – | – | – | – |
| Not reported | 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
20 of 116 records name a vendor (17%)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 14% 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.