Custom inventory report
Filter: function = chat_assistant; monitoring = blank
Key findings
- 214 use-case records from 26 agencies: 62 deployed (29%), 35 pilots, 94 in development or acquisition, 16 retired, 7 with no stage reported.
- 5 records are designated high-impact; 0 of them (0%) answered at least one minimum-practice question.
- HHS, TREAS, DOE report 87 of 214 use cases (41%).
- 71% of reported deployed use cases (44 of 62) are classified as generative or agentic AI.
- Of 1 deployed high-impact use cases, 0 report a completed AI impact assessment and 1 leave every minimum-practice field blank.
- Interpretation: Blank practice fields are missing evidence, not evidence that a safeguard is absent. They mark where follow-up questions to the agency are most useful.
- Sourcing is reported for 46% of use cases. Where reported, 77% involve in-house development.
- 7 use cases (3%) have no reported development stage.
Lifecycle
| Stage | Records | Share |
|---|---|---|
| Deployed | 62 | 29% |
| Pilot | 35 | 16% |
| Pre-deployment | 94 | 44% |
| Retired | 16 | 7% |
| Not reported | 7 | 3% |
AI technology
| Classification | Records | Share | Deployed |
|---|---|---|---|
| Generative AI | 127 | 59% | 42 |
| Classical / predictive ML | 8 | 4% | 4 |
| Natural language processing | 42 | 20% | 13 |
| Computer vision | 1 | 0% | 0 |
| Agentic AI | 11 | 5% | 2 |
| Reinforcement learning | 1 | 0% | 0 |
| Other | 1 | 0% | 0 |
| Not reported | 23 | 11% | 1 |
Agencies
| Agency | Records | Deployed | High-impact | Completeness |
|---|---|---|---|---|
| HHS · Department of Health and Human Services | 52 | 20 | 0 | 80% |
| TREAS · Department of the Treasury | 18 | 5 | 0 | 72% |
| DOE · Department of Energy | 17 | 6 | 0 | 83% |
| VA · Department of Veterans Affairs | 16 | 2 | 4 | 56% |
| DOJ · Department of Justice | 15 | 1 | 1 | 72% |
| DHS · Department of Homeland Security | 14 | 4 | 0 | 70% |
| GSA · General Services Administration | 11 | 3 | 0 | 33% |
| USDA · Department of Agriculture | 11 | 3 | 0 | 81% |
| NASA · National Aeronautics and Space Administration | 10 | 0 | 0 | 67% |
| SEC · Securities and Exchange Commission | 7 | 2 | 0 | 63% |
| DOT · Department of Transportation | 7 | 3 | 0 | 74% |
| DOC · Department of Commerce | 7 | 0 | 0 | 2% |
| STATE · Department of State | 5 | 2 | 0 | 75% |
| DOI · Department of the Interior | 4 | 0 | 0 | 87% |
| SSA · Social Security Administration | 4 | 3 | 0 | 100% |
| DOL · Department of Labor | 3 | 1 | 0 | 61% |
| HUD · Department of Housing and Urban Development | 2 | 1 | 0 | 79% |
| FDIC · Federal Deposit Insurance Corporation | 2 | 2 | 0 | 100% |
| NARA · National Archives and Records Administration | 2 | 1 | 0 | 100% |
| NSF · National Science Foundation | 1 | 1 | 0 | 25% |
High-impact designation
| Designation | Records |
|---|---|
| High-impact | 5 |
| Presumed high-impact, determined not | 2 |
| Not high-impact | 188 |
| Not reported | 19 |
Governance evidence: use cases designated high-impact
| Practice | In place | In progress | Not in place | N/A or precluded | Other | No answer | In scope |
|---|---|---|---|---|---|---|---|
| Pre-deployment testing | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
| AI impact assessment | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
| Independent review | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
| Ongoing monitoring | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
| Operator training | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
| Fail-safe | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
| Appeal process | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
| User and public consultation | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
"In place" means the agency reported the practice as established. "No answer" means the field was blank; it says nothing about whether the practice exists.
Method
All figures are computed directly from the normalised inventory records. Category labels are normalised from the source answer options; original values are kept on each record. Governance answers are classified as in place, in progress, not in place, not applicable, precluded, waived, other or no answer, and blanks are never read as "no". See the Methodology page for full rules.
Limitations
- Figures are what agencies reported in the 2025 inventory. They are not verified, and registration of use cases is not complete for every agency.
- A blank answer is missing evidence, not evidence that a practice or safeguard is absent.
- Counts are use-case records. A larger portfolio does not indicate better performance or greater AI maturity, and agencies differ in how they split systems into use cases.
- The inventory has no budget, cost, accuracy or outcome data. None is shown or inferred here.
- This is a disclosure analysis. It is not an audit, a compliance determination or an official government assessment.
Source records
First 40 of 214 records, deployed first. The full list is available as CSV from the explorer with the same filters.
Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory