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 (26.3%)Generative AI
Pilot: 15 (18.8%)Generative AI
Pre-deployment: 44 (55.0%)
80Deployed: 21 (26.3%)Generative AI
Pilot: 15 (18.8%)Generative AI
Pre-deployment: 44 (55.0%)
Classical / predictive ML
Classical / predictive ML
Deployed: 40 (52.6%)Classical / predictive ML
Pilot: 5 (6.6%)Classical / predictive ML
Pre-deployment: 31 (40.8%)
76Deployed: 40 (52.6%)Classical / predictive ML
Pilot: 5 (6.6%)Classical / predictive ML
Pre-deployment: 31 (40.8%)
Natural language processing
Natural language processing
Deployed: 19 (40.4%)Natural language processing
Pilot: 3 (6.4%)Natural language processing
Pre-deployment: 25 (53.2%)
47Deployed: 19 (40.4%)Natural language processing
Pilot: 3 (6.4%)Natural language processing
Pre-deployment: 25 (53.2%)
Sourcing by technology
Where reportedDeveloped in-houseContract and in-housePurchased from vendorNot reported
Generative AI
Generative AI
Developed in-house: 2 (2.5%)Generative AI
Contract and in-house: 5 (6.3%)Generative AI
Purchased from vendor: 29 (36.3%)Generative AI
Not reported: 44 (55.0%)
80Developed in-house: 2 (2.5%)Generative AI
Contract and in-house: 5 (6.3%)Generative AI
Purchased from vendor: 29 (36.3%)Generative AI
Not reported: 44 (55.0%)
Classical / predictive ML
Classical / predictive ML
Developed in-house: 4 (5.3%)Classical / predictive ML
Contract and in-house: 7 (9.2%)Classical / predictive ML
Purchased from vendor: 34 (44.7%)Classical / predictive ML
Not reported: 31 (40.8%)
76Developed in-house: 4 (5.3%)Classical / predictive ML
Contract and in-house: 7 (9.2%)Classical / predictive ML
Purchased from vendor: 34 (44.7%)Classical / predictive ML
Not reported: 31 (40.8%)
Natural language processing
Natural language processing
Developed in-house: 4 (8.5%)Natural language processing
Contract and in-house: 3 (6.4%)Natural language processing
Purchased from vendor: 15 (31.9%)Natural language processing
Not reported: 25 (53.2%)
47Developed in-house: 4 (8.5%)Natural language processing
Contract and in-house: 3 (6.4%)Natural language processing
Purchased from vendor: 15 (31.9%)Natural language processing
Not reported: 25 (53.2%)
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 | Not reported | n | |
|---|---|---|---|---|---|---|---|
| Law Enforcement | 27% | 36% | 27% | 7% | 2% | 107 | |
| Administrative Functions | 47% | 25% | 20% | 7% | 59 | ||
| Information Technology | 47% | 41% | 9% | 3% | 32 | ||
| Procurement and Financial Management | 33% | 67% | 6 | ||||
| Service Delivery | 40% | 40% | 20% | 5 | |||
| Human Resources | 25% | 50% | 25% | 4 | |||
| Cybersecurity | 67% | 33% | 3 | ||||
| Energy and the Environment | 100% | 1 | |||||
| Government Benefits Processing | 100% | 1 | |||||
| Science | 100% | 1 | |||||
| Transportation | 100% | 1 |
High-impact designation by technology
High-impactPresumed high-impact, determined notNot high-impactNot reported
Generative AI
Generative AI
High-impact: 12 (15.0%)Generative AI
Presumed high-impact, determined not: 11 (13.8%)Generative AI
Not high-impact: 57 (71.3%)
80High-impact: 12 (15.0%)Generative AI
Presumed high-impact, determined not: 11 (13.8%)Generative AI
Not high-impact: 57 (71.3%)
Classical / predictive ML
Classical / predictive ML
High-impact: 9 (11.8%)Classical / predictive ML
Presumed high-impact, determined not: 16 (21.1%)Classical / predictive ML
Not high-impact: 51 (67.1%)
76High-impact: 9 (11.8%)Classical / predictive ML
Presumed high-impact, determined not: 16 (21.1%)Classical / predictive ML
Not high-impact: 51 (67.1%)
Natural language processing
Natural language processing
High-impact: 8 (17.0%)Natural language processing
Presumed high-impact, determined not: 4 (8.5%)Natural language processing
Not high-impact: 35 (74.5%)
47High-impact: 8 (17.0%)Natural language processing
Presumed high-impact, determined not: 4 (8.5%)Natural language processing
Not high-impact: 35 (74.5%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 12 | 0 / 12 | 0 / 12 | 0 / 12 | 0 / 12 | 0 / 12 |
| Classical / predictive ML | 9 | 0 / 9 | 0 / 9 | 0 / 9 | 0 / 9 | 0 / 9 |
| Natural language processing | 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 | 0 / 8 |
| Computer vision | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Agentic AI | 0 | – | – | – | – | – |
| Not reported | 9 | 0 / 9 | 0 / 9 | 0 / 9 | 0 / 9 | 0 / 9 |
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
80 of 239 records name a vendor (33%)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 47% 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.