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: 21 (33.3%)Generative AI
Pilot: 4 (6.3%)Generative AI
Pre-deployment: 38 (60.3%)
63Deployed: 21 (33.3%)Generative AI
Pilot: 4 (6.3%)Generative AI
Pre-deployment: 38 (60.3%)
Classical / predictive ML
Classical / predictive ML
Deployed: 41 (71.9%)Classical / predictive ML
Pilot: 1 (1.8%)Classical / predictive ML
Pre-deployment: 15 (26.3%)
57Deployed: 41 (71.9%)Classical / predictive ML
Pilot: 1 (1.8%)Classical / predictive ML
Pre-deployment: 15 (26.3%)
Natural language processing
Natural language processing
Deployed: 13 (40.6%)Natural language processing
Pre-deployment: 19 (59.4%)
32Deployed: 13 (40.6%)Natural language processing
Pre-deployment: 19 (59.4%)
Sourcing by technology
Where reportedDeveloped in-houseContract and in-housePurchased from vendorNot reported
Generative AI
Generative AI
Developed in-house: 5 (7.9%)Generative AI
Contract and in-house: 9 (14.3%)Generative AI
Purchased from vendor: 11 (17.5%)Generative AI
Not reported: 38 (60.3%)
63Developed in-house: 5 (7.9%)Generative AI
Contract and in-house: 9 (14.3%)Generative AI
Purchased from vendor: 11 (17.5%)Generative AI
Not reported: 38 (60.3%)
Classical / predictive ML
Classical / predictive ML
Developed in-house: 16 (28.1%)Classical / predictive ML
Contract and in-house: 16 (28.1%)Classical / predictive ML
Purchased from vendor: 10 (17.5%)Classical / predictive ML
Not reported: 15 (26.3%)
57Developed in-house: 16 (28.1%)Classical / predictive ML
Contract and in-house: 16 (28.1%)Classical / predictive ML
Purchased from vendor: 10 (17.5%)Classical / predictive ML
Not reported: 15 (26.3%)
Natural language processing
Natural language processing
Developed in-house: 1 (3.1%)Natural language processing
Contract and in-house: 7 (21.9%)Natural language processing
Purchased from vendor: 5 (15.6%)Natural language processing
Not reported: 19 (59.4%)
32Developed in-house: 1 (3.1%)Natural language processing
Contract and in-house: 7 (21.9%)Natural language processing
Purchased from vendor: 5 (15.6%)Natural language processing
Not reported: 19 (59.4%)
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 | Not reported | n | |
|---|---|---|---|---|---|---|---|
| Law Enforcement | 13% | 34% | 10% | 43% | 86 | ||
| Administrative Functions | 53% | 19% | 19% | 9% | 53 | ||
| Transportation | 13% | 25% | 19% | 38% | 6% | 16 | |
| Service Delivery | 25% | 25% | 42% | 8% | 12 | ||
| Information Technology | 82% | 9% | 9% | 11 | |||
| Cybersecurity | 14% | 86% | 7 | ||||
| Emergency Management | 60% | 40% | 5 | ||||
| Human Resources | 50% | 25% | 25% | 4 | |||
| Government Benefits Processing | 67% | 33% | 3 | ||||
| Health and Medical | 33% | 67% | 3 | ||||
| Procurement and Financial Management | 67% | 33% | 3 | ||||
| International Affairs | 100% | 1 | |||||
| Other | 100% | 1 |
High-impact designation by technology
High-impactPresumed high-impact, determined notNot high-impactNot reported
Generative AI
Generative AI
High-impact: 4 (6.3%)Generative AI
Presumed high-impact, determined not: 5 (7.9%)Generative AI
Not high-impact: 54 (85.7%)
63High-impact: 4 (6.3%)Generative AI
Presumed high-impact, determined not: 5 (7.9%)Generative AI
Not high-impact: 54 (85.7%)
Classical / predictive ML
Classical / predictive ML
High-impact: 13 (22.8%)Classical / predictive ML
Presumed high-impact, determined not: 20 (35.1%)Classical / predictive ML
Not high-impact: 24 (42.1%)
57High-impact: 13 (22.8%)Classical / predictive ML
Presumed high-impact, determined not: 20 (35.1%)Classical / predictive ML
Not high-impact: 24 (42.1%)
Natural language processing
Natural language processing
High-impact: 8 (25.0%)Natural language processing
Presumed high-impact, determined not: 5 (15.6%)Natural language processing
Not high-impact: 19 (59.4%)
32High-impact: 8 (25.0%)Natural language processing
Presumed high-impact, determined not: 5 (15.6%)Natural language processing
Not high-impact: 19 (59.4%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 |
| Classical / predictive ML | 13 | 11 / 13 | 10 / 13 | 10 / 13 | 11 / 13 | 10 / 13 |
| Natural language processing | 8 | 3 / 8 | 1 / 8 | 1 / 8 | 1 / 8 | 2 / 8 |
| Computer vision | 26 | 18 / 26 | 15 / 26 | 15 / 26 | 16 / 26 | 16 / 26 |
| Agentic AI | 0 | – | – | – | – | – |
| Not reported | 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 | 0 / 4 |
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
97 of 238 records name a vendor (41%)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 34% 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.