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: 13 (4.6%)Classical / predictive ML
Pilot: 11 (3.9%)Classical / predictive ML
Pre-deployment: 258 (91.5%)
Deployed: 4 (14.8%)Natural language processing
Pilot: 2 (7.4%)Natural language processing
Pre-deployment: 20 (74.1%)Natural language processing
Retired: 1 (3.7%)
Deployed: 5 (10.6%)Computer vision
Pre-deployment: 41 (87.2%)Computer vision
Retired: 1 (2.1%)
Sourcing by technology
Where reportedDeveloped in-house: 4 (10.8%)Generative AI
Contract and in-house: 1 (2.7%)Generative AI
Not reported: 32 (86.5%)
Developed in-house: 23 (8.2%)Classical / predictive ML
Contract and in-house: 1 (0.4%)Classical / predictive ML
Not reported: 258 (91.5%)
Developed in-house: 4 (14.8%)Natural language processing
Contract and in-house: 2 (7.4%)Natural language processing
Not reported: 21 (77.8%)
Developed in-house: 3 (6.4%)Computer vision
Contract and in-house: 1 (2.1%)Computer vision
Purchased from vendor: 1 (2.1%)Computer vision
Not reported: 42 (89.4%)
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 | Reinforcement learning | Other | Not reported | n | |
|---|---|---|---|---|---|---|---|---|---|
| Science | 7% | 68% | 6% | 11% | 5% | 1% | 1% | 387 | |
| Transportation | 67% | 22% | 11% | 9 | |||||
| Administrative Functions | 17% | 50% | 17% | 17% | 6 | ||||
| Information Technology | 33% | 50% | 17% | 6 | |||||
| Service Delivery | 20% | 80% | 5 | ||||||
| Health and Medical | 33% | 33% | 33% | 3 | |||||
| Other | 67% | 33% | 3 | ||||||
| Procurement and Financial Management | 50% | 50% | 2 | ||||||
| Cybersecurity | 100% | 1 | |||||||
| Emergency Management | 100% | 1 | |||||||
| Law Enforcement | 100% | 1 |
High-impact designation by technology
Presumed high-impact, determined not: 1 (0.4%)Classical / predictive ML
Not high-impact: 281 (99.6%)
High-impact: 1 (2.1%)Computer vision
Presumed high-impact, determined not: 1 (2.1%)Computer vision
Not high-impact: 45 (95.7%)
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 | 0 | – | – | – | – | – |
| Natural language processing | 0 | – | – | – | – | – |
| Computer vision | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Agentic AI | 0 | – | – | – | – | – |
| Reinforcement learning | 0 | – | – | – | – | – |
| Other | 0 | – | – | – | – | – |
| Not reported | 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
6 of 425 records name a vendor (1%)No records match the current filters.
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 8% 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.