Federal AI Intelligence · U.S. Government AI Observatory
Federal AI landscape: executive briefing
Filtered: topic = Science; AI type = classical_ml; function = forecasting
Generated 2026-10-13 10:45 UTCDataset: 2025 Federal Agency AI Use Case Inventory (individually reported use cases), Office of Management and Budget (OMB). Version 2025-inventory@62d5baa65fa3, processed 2026-10-11, rules 2026.10.1. Static snapshot.Applied filters: topic = Science; AI type = classical_ml; function = forecasting · 34 records
Key findings
- 34 use-case records from 6 agencies: 2 deployed (6%), 11 pilots, 21 in development or acquisition, 0 retired, 0 with no stage reported.
- 0 records are designated high-impact; 0 of them (n/a) answered at least one minimum-practice question.
- NASA, DOI, DOE report 29 of 34 use cases (85%).
- 0% of reported deployed use cases (0 of 2) are classified as generative or agentic AI.
- Sourcing is reported for 41% of use cases. Where reported, 93% involve in-house development.
Lifecycle
| Stage | Records | Share |
|---|
| Deployed | 2 | 6% |
| Pilot | 11 | 32% |
| Pre-deployment | 21 | 62% |
AI technology
| Classification | Records | Share | Deployed |
|---|
| Classical / predictive ML | 34 | 100% | 2 |
Agencies
| Agency | Records | Deployed | High-impact | Completeness |
|---|
| NASA · National Aeronautics and Space Administration | 13 | 0 | 0 | 62% |
| DOI · Department of the Interior | 12 | 2 | 0 | 95% |
| DOE · Department of Energy | 4 | 0 | 0 | 79% |
| USDA · Department of Agriculture | 3 | 0 | 0 | 86% |
| HHS · Department of Health and Human Services | 1 | 0 | 0 | 58% |
| VA · Department of Veterans Affairs | 1 | 0 | 0 | 58% |
High-impact designation
| Designation | Records |
|---|
| Not high-impact | 34 |
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 | 0 | 0 |
| AI impact assessment | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Independent review | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Ongoing monitoring | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Operator training | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Fail-safe | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Appeal process | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| User and public consultation | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
"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
All 34 records.
| Record | Agency ID | Agency | Use case | Stage |
|---|
| R2533 | DOI-0230 | DOI | Delineating sub-surface drainage using satellite imagery [2024 INV#WO0000000109525] | Deployed |
| R2658 | DOI-0100 | DOI | Predicting post-fire tree mortality | Deployed |
| R0195 | NASA-317 | NASA | Geostationary Satellite Sounder Pathfinder Project for 4-D Atmospheric Thermodynamics and Winds | Pre-deployment |
| R0264 | NASA-506 | NASA | SERVIR Applied Deep Learning Book | Pre-deployment |
| R0304 | NASA-583 | NASA | Bias correction of GEOS S2S forecast using machine learning | Pre-deployment |
| R0315 | NASA-602 | NASA | Development of a machine learning emulator for CAM-CMAQ to improve wildfire smoke forecasts | Pre-deployment |
| R0329 | NASA-620 | NASA | Generating localized air quality forecasts for US embassies using ML and GEOS-FP | Pre-deployment |
| R0339 | NASA-636 | NASA | Localized air quality forecasts for cities in Africa and South America | Pre-deployment |
| R0368 | NASA-667 | NASA | Rangelands Water Monitoring and Forecasting System | Pre-deployment |
| R0433 | NASA-814 | NASA | Time Series Forecasting, Evaluation and Deployment (Time-FED) | Pilot |
| R0467 | NASA-865 | NASA | A Forecasting Scheme For Accelerated Harmful Algal Bloom Monitoring (FASTHAB) | Pre-deployment |
| R0470 | NASA-868 | NASA | Integrating Explainable Machine Learning with Physics for Enhanced Wildfire Detection in Observation-Constrained Environments | Pre-deployment |
| R0481 | NASA-879 | NASA | Data-Mining Similar Scenarios for Uncertainty Quantification of Solar Wind Predictions at L1 | Pre-deployment |
| R0491 | NASA-893 | NASA | Nominal Forecast Sub System for the NASA Coastal Zone Digital Twin | Pre-deployment |
| R0510 | NASA-915 | NASA | Development of a Next-Generation Ensemble Prediction System for Atmospheric Composition | Pre-deployment |
| R0873 | | HHS | AI-Enhanced FDA Regulated Commodity Consumption Pattern Analysis | Pre-deployment |
| R1378 | USDA-077 | USDA | Regression, Classification, Clustering with Hilbert Curves | Pilot |
| R1458 | USDA-160 | USDA | Long-term monitoring of northern spotted owl habitat and populations | Pilot |
| R1468 | USDA-170 | USDA | Preseason Corn and Soybeans Forecasts | Pre-deployment |
| R2196 | VA-25-4094 | VA | PestScore VA: AI-Driven Pest Risk Forecasting | Pre-deployment |
| R2529 | DOI-0234 | DOI | Data-Driven Streamflow Drought [2024 INV#WO0000000109714] | Pilot |
| R2540 | DOI-0223 | DOI | Oceanographic, coastal, and geomorphic change analysis: data generation, QC/QA, and data management [2024 INV#WO0000000109310] | Pilot |
| R2585 | DOI-0178 | DOI | Machine learning based shoreline time-series imputation, classification and forecasting (time-series analyses) [2024 INV#WO0000000108117] | Pilot |
| R2598 | DOI-0165 | DOI | Tsunami Hazard Analysis [2024 INV#WO0000000108470] | Pre-deployment |
| R2599 | DOI-0167 | DOI | Machine learning for streamflow forecasting [2024 INV#WO0000000109317] | Pre-deployment |
| R2604 | DOI-0158 | DOI | HOTLink: identifying elevated thermal anomalies at volcanoes [2024 INV#WO0000000109217] | Pilot |
| R2649 | DOI-0107 | DOI | Storm Induced Erosion Response Network | Pre-deployment |
| R2654 | DOI-0105 | DOI | Experimental Forecast for River Chlorophyll | Pilot |
| R2671 | DOI-0061 | DOI | Invasive Carp Harvest Predictive Model | Pilot |
| R2717 | DOI-0031 | DOI | Nutrient, Salinity, sediment, temperature, and drought model development | Pre-deployment |
| R3386 | DOE-529 | DOE | NIF Shot Analytics & Predictive Maintenance Support Pilot | Pilot |
| R3428 | DOE-572 | DOE | Use ML as part of an integrated strategy for forecasting renewable energy resources | Pre-deployment |
| R3447 | DOE-592 | DOE | Use AI/ML for Climate Prediction | Pre-deployment |
| R3588 | DOE-73 | DOE | Machine Learning for Accelerator Operations Using Big Data Analytics / L-CAPE | Pilot |
Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory