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: 36 (47.4%)Generative AI
Pilot: 11 (14.5%)Generative AI
Pre-deployment: 27 (35.5%)Generative AI
Retired: 2 (2.6%)
Deployed: 13 (33.3%)Classical / predictive ML
Pilot: 8 (20.5%)Classical / predictive ML
Pre-deployment: 17 (43.6%)Classical / predictive ML
Retired: 1 (2.6%)
Sourcing by technology
Where reportedDeveloped in-house: 18 (23.7%)Generative AI
Contract and in-house: 4 (5.3%)Generative AI
Purchased from vendor: 54 (71.1%)
Developed in-house: 27 (69.2%)Classical / predictive ML
Contract and in-house: 1 (2.6%)Classical / predictive ML
Purchased from vendor: 2 (5.1%)Classical / predictive ML
Not reported: 9 (23.1%)
Developed in-house: 3 (37.5%)Natural language processing
Contract and in-house: 2 (25.0%)Natural language processing
Purchased from vendor: 2 (25.0%)Natural language processing
Not reported: 1 (12.5%)
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 | |
|---|---|---|---|---|---|---|---|
| Information Technology | 98% | 2% | 45 | ||||
| Science | 6% | 84% | 6% | 3% | 31 | ||
| Administrative Functions | 82% | 6% | 6% | 6% | 17 | ||
| Service Delivery | 71% | 29% | 14 | ||||
| Law Enforcement | 25% | 25% | 50% | 8 | |||
| Procurement and Financial Management | 40% | 20% | 20% | 20% | 5 | ||
| Energy and the Environment | 25% | 75% | 4 | ||||
| Other | 50% | 25% | 25% | 4 | |||
| Cybersecurity | 33% | 33% | 33% | 3 | |||
| Emergency Management | 100% | 1 | |||||
| Government Benefits Processing | 100% | 1 | |||||
| Human Resources | 100% | 1 | |||||
| International Affairs | 100% | 1 |
High-impact designation by technology
High-impact: 3 (37.5%)Natural language processing
Not high-impact: 5 (62.5%)
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 | 3 | 3 / 3 | 2 / 3 | 2 / 3 | 3 / 3 | 3 / 3 |
| Natural language processing | 3 | 2 / 3 | 1 / 3 | 1 / 3 | 1 / 3 | 1 / 3 |
| Computer vision | 4 | 4 / 4 | 4 / 4 | 4 / 4 | 4 / 4 | 4 / 4 |
| Agentic AI | 0 | – | – | – | – | – |
| 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
77 of 135 records name a vendor (57%)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 70% 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.