Technology landscape
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 recordsDeployedPilotPre-deploymentRetiredNot reported
Generative AI
Generative AI
Deployed: 44 (31.4%)Generative AI
Pilot: 38 (27.1%)Generative AI
Pre-deployment: 58 (41.4%)
140Deployed: 44 (31.4%)Generative AI
Pilot: 38 (27.1%)Generative AI
Pre-deployment: 58 (41.4%)
Classical / predictive ML
Classical / predictive ML
Deployed: 46 (52.9%)Classical / predictive ML
Pilot: 12 (13.8%)Classical / predictive ML
Pre-deployment: 29 (33.3%)
87Deployed: 46 (52.9%)Classical / predictive ML
Pilot: 12 (13.8%)Classical / predictive ML
Pre-deployment: 29 (33.3%)
Natural language processing
Natural language processing
Deployed: 55 (47.0%)Natural language processing
Pilot: 28 (23.9%)Natural language processing
Pre-deployment: 34 (29.1%)
117Deployed: 55 (47.0%)Natural language processing
Pilot: 28 (23.9%)Natural language processing
Pre-deployment: 34 (29.1%)
Computer vision
Computer vision
Deployed: 13 (76.5%)Computer vision
Pilot: 1 (5.9%)Computer vision
Pre-deployment: 3 (17.6%)
17Deployed: 13 (76.5%)Computer vision
Pilot: 1 (5.9%)Computer vision
Pre-deployment: 3 (17.6%)
Sourcing by technology
Where reportedDeveloped in-houseContract and in-housePurchased from vendorNot reported
Generative AI
Generative AI
Developed in-house: 22 (15.7%)Generative AI
Contract and in-house: 42 (30.0%)Generative AI
Purchased from vendor: 18 (12.9%)Generative AI
Not reported: 58 (41.4%)
140Developed in-house: 22 (15.7%)Generative AI
Contract and in-house: 42 (30.0%)Generative AI
Purchased from vendor: 18 (12.9%)Generative AI
Not reported: 58 (41.4%)
Classical / predictive ML
Classical / predictive ML
Developed in-house: 22 (25.3%)Classical / predictive ML
Contract and in-house: 27 (31.0%)Classical / predictive ML
Purchased from vendor: 9 (10.3%)Classical / predictive ML
Not reported: 29 (33.3%)
87Developed in-house: 22 (25.3%)Classical / predictive ML
Contract and in-house: 27 (31.0%)Classical / predictive ML
Purchased from vendor: 9 (10.3%)Classical / predictive ML
Not reported: 29 (33.3%)
Natural language processing
Natural language processing
Developed in-house: 34 (29.1%)Natural language processing
Contract and in-house: 37 (31.6%)Natural language processing
Purchased from vendor: 12 (10.3%)Natural language processing
Not reported: 34 (29.1%)
117Developed in-house: 34 (29.1%)Natural language processing
Contract and in-house: 37 (31.6%)Natural language processing
Purchased from vendor: 12 (10.3%)Natural language processing
Not reported: 34 (29.1%)
Computer vision
Computer vision
Developed in-house: 1 (5.9%)Computer vision
Contract and in-house: 5 (29.4%)Computer vision
Purchased from vendor: 8 (47.1%)Computer vision
Not reported: 3 (17.6%)
17Developed in-house: 1 (5.9%)Computer vision
Contract and in-house: 5 (29.4%)Computer vision
Purchased from vendor: 8 (47.1%)Computer vision
Not reported: 3 (17.6%)
Agentic AI
Agentic AI
Developed in-house: 1 (3.4%)Agentic AI
Contract and in-house: 9 (31.0%)Agentic AI
Purchased from vendor: 3 (10.3%)Agentic AI
Not reported: 16 (55.2%)
29Developed in-house: 1 (3.4%)Agentic AI
Contract and in-house: 9 (31.0%)Agentic AI
Purchased from vendor: 3 (10.3%)Agentic AI
Not reported: 16 (55.2%)
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 | |
|---|---|---|---|---|---|---|---|---|
| Administrative Functions | 41% | 21% | 30% | 1% | 5% | 3% | 117 | |
| Health and Medical | 27% | 20% | 42% | 4% | 3% | 3% | 89 | |
| Information Technology | 39% | 25% | 23% | 13% | 87 | |||
| Science | 23% | 29% | 19% | 19% | 6% | 4% | 48 | |
| Service Delivery | 33% | 17% | 44% | 6% | 18 | |||
| Other | 45% | 27% | 9% | 18% | 11 | |||
| Emergency Management | 13% | 25% | 63% | 8 | ||||
| Procurement and Financial Management | 57% | 14% | 29% | 7 | ||||
| Government Benefits Processing | 40% | 40% | 20% | 5 | ||||
| Law Enforcement | 50% | 25% | 25% | 4 | ||||
| Human Resources | 100% | 3 | ||||||
| Cybersecurity | 100% | 1 |
High-impact designation by technology
High-impactPresumed high-impact, determined notNot high-impactNot reported
Generative AI
Generative AI
Presumed high-impact, determined not: 1 (0.7%)Generative AI
Not high-impact: 139 (99.3%)
140Presumed high-impact, determined not: 1 (0.7%)Generative AI
Not high-impact: 139 (99.3%)
Classical / predictive ML
Classical / predictive ML
High-impact: 1 (1.1%)Classical / predictive ML
Presumed high-impact, determined not: 8 (9.2%)Classical / predictive ML
Not high-impact: 78 (89.7%)
87High-impact: 1 (1.1%)Classical / predictive ML
Presumed high-impact, determined not: 8 (9.2%)Classical / predictive ML
Not high-impact: 78 (89.7%)
Computer vision
Computer vision
High-impact: 1 (5.9%)Computer vision
Presumed high-impact, determined not: 3 (17.6%)Computer vision
Not high-impact: 13 (76.5%)
17High-impact: 1 (5.9%)Computer vision
Presumed high-impact, determined not: 3 (17.6%)Computer vision
Not high-impact: 13 (76.5%)
Agentic AI29
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 | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Natural language processing | 0 | – | – | – | – | – |
| Computer vision | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Agentic AI | 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
139 of 447 records name a vendor (31%)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 39% 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.