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: 45 (68.2%)Generative AI
Pilot: 8 (12.1%)Generative AI
Pre-deployment: 12 (18.2%)Generative AI
Retired: 1 (1.5%)
Deployed: 38 (77.6%)Classical / predictive ML
Pilot: 3 (6.1%)Classical / predictive ML
Pre-deployment: 8 (16.3%)
Sourcing by technology
Where reportedDeveloped in-house: 4 (6.1%)Generative AI
Contract and in-house: 7 (10.6%)Generative AI
Purchased from vendor: 55 (83.3%)
Developed in-house: 4 (8.2%)Classical / predictive ML
Contract and in-house: 5 (10.2%)Classical / predictive ML
Purchased from vendor: 40 (81.6%)
Developed in-house: 2 (9.1%)Natural language processing
Contract and in-house: 4 (18.2%)Natural language processing
Purchased from vendor: 16 (72.7%)
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 | 12% | 34% | 22% | 30% | 1% | 73 | |
| Information Technology | 93% | 2% | 5% | 42 | |||
| Administrative Functions | 25% | 42% | 8% | 17% | 8% | 12 | |
| Service Delivery | 75% | 25% | 12 | ||||
| Health and Medical | 13% | 75% | 13% | 8 | |||
| Cybersecurity | 20% | 60% | 20% | 5 | |||
| Energy and the Environment | 80% | 20% | 5 | ||||
| Other | 40% | 20% | 20% | 20% | 5 | ||
| Science | 50% | 50% | 2 | ||||
| Human Resources | 100% | 1 | |||||
| Procurement and Financial Management | 100% | 1 |
High-impact designation by technology
High-impact: 11 (16.7%)Generative AI
Presumed high-impact, determined not: 1 (1.5%)Generative AI
Not high-impact: 54 (81.8%)
High-impact: 36 (73.5%)Classical / predictive ML
Not high-impact: 13 (26.5%)
High-impact: 17 (77.3%)Natural language processing
Not high-impact: 5 (22.7%)
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 | 26 | 4 / 26 | 0 / 26 | 0 / 26 | 2 / 26 | 2 / 26 |
| 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
140 of 166 records name a vendor (84%)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 94% 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.