Federal AI Intelligence · U.S. Government AI Observatory
Agency AI profile
Department of Housing and Urban Development (HUD)
Key findings
- HUD reported 11 use cases (0.3% of all records): 5 deployed, 1 pilots, 3 in development or acquisition, 2 retired, 0 with no stage.
- 0 are designated high-impact; 0 of those answered at least one minimum-practice question.
- Mean disclosure completeness is 77% of 12 core fields, ranking 20 of 41 agencies.
- Sourcing is reported for 64% of records.
Portfolio
Technology
| Classification | Records | Share |
|---|---|---|
| Generative AI | 4 | 36% |
| Classical / predictive ML | 2 | 18% |
| Natural language processing | 3 | 27% |
| Not reported | 2 | 18% |
Topic areas
| Topic | Records | Share |
|---|---|---|
| Service Delivery | 3 | 27% |
| Administrative Functions | 2 | 18% |
| Other | 2 | 18% |
| Human Resources | 1 | 9% |
| Procurement and Financial Management | 1 | 9% |
| Not reported | 2 | 18% |
Governance disclosures (high-impact records)
No use cases designated high-impact.
Field completeness
| Field | HUD | All agencies |
|---|---|---|
| Stage | 100% | 91% |
| High-impact designation | 100% | 88% |
| Topic area | 82% | 83% |
| AI classification | 82% | 82% |
| Problem | 82% | 84% |
| Benefits | 82% | 82% |
| Outputs | 82% | 79% |
| Sourcing | 64% | 44% |
| Start date | 64% | 39% |
| ATO | 64% | 43% |
| PII | 64% | 42% |
| Custom code | 64% | 47% |
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
All 11 records.
| Record | Agency ID | Agency | Use case | Stage |
|---|---|---|---|---|
| R0537 | HUD-2024-001 | HUD | Counterparty Risk Anomaly Detection | Deployed |
| R0538 | HUD-2024-002 | HUD | Subledger Data Quality Machine Learning | Deployed |
| R0539 | HUD-2024-003 | HUD | Automated Draft Narrative Reports Previously "Automating Draft Counterparty Credit Narrative Reports"; have since expanded to general enterprise use. | Deployed |
| R0540 | HUD-2024-004 | HUD | Voice of the Customer | Deployed |
| R0546 | HUD-2025-004 | HUD | CAISY - Workforce Training Conversation Simulator | Deployed |
| R0541 | HUD-2024-005 | HUD | Quantitative Text Analysis | Retired |
| R0542 | HUD-2024-006 | HUD | Translation of Digital Media | Retired |
| R0543 | HUD-2025-001 | HUD | Microsoft Copilot | Pilot |
| R0544 | HUD-2025-002 | HUD | Amazon Textract for automatic signature identification | Pre-deployment |
| R0545 | HUD-2025-003 | HUD | Email Assistant | Pre-deployment |
| R0547 | HUD-2025-005 | HUD | FHA Resource Center Chatbot | Pre-deployment |
Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory