Federal AI Intelligence · U.S. Government AI Observatory

Federal AI landscape: executive briefing

Filtered: agency = DOI; AI type = classical_ml; sourcing = unknown

Generated 2026-10-13 18:32 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: agency = DOI; AI type = classical_ml; sourcing = unknown · 61 records

Key findings

61 use-case records from 1 agencies: 0 deployed (0%), 0 pilots, 61 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.
Sourcing is reported for 0% of use cases. Where reported, n/a involve in-house development.

Lifecycle

Stage	Records	Share
Pre-deployment	61	100%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	61	100%	0

Agencies

Agency	Records	Deployed	High-impact	Completeness
DOI · Department of the Interior	61	0	0	83%

High-impact designation

Designation	Records
Not high-impact	61

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

First 40 of 61 records, deployed first. The full list is available as CSV from the explorer with the same filters.

Record	Agency ID	Agency	Use case	Stage
R2500	DOI-0266	DOI	Use of AI to Enhance Flash Flood Forecast Tool [2024 Inv#WO0000000110323]	Pre-deployment
R2502	DOI-0264	DOI	Remote Sensing Coastal Change - Shoreline Change	Pre-deployment
R2527	DOI-0236	DOI	Avian population estimates from passive acoustic monitoring [2024 INV#WO0000000109725]	Pre-deployment
R2528	DOI-0235	DOI	FEMA mixed population flood-frequency analysis [2024 INV#WO0000000109723]	Pre-deployment
R2532	DOI-0231	DOI	Mapping sagebrush from drones to satellites [2024 INV#WO0000000109568]	Pre-deployment
R2535	DOI-0228	DOI	Reinforcement Learning for Helmholtz Coil Operation and Simulation [2024 INV#WO0000000109497]	Pre-deployment
R2546	DOI-0217	DOI	Development of a Strategic Framework for Use and Implementation of Machine Learning in Energy Resource Program Workflows [2024 INV#WO0000000109216]	Pre-deployment
R2552	DOI-0211	DOI	Predicting Sparse (Geothermal) Resources Availability by using Machine Learning [2024 INV#WO0000000109195]	Pre-deployment
R2558	DOI-0205	DOI	Mapping wildfire fuels in previously burned landscapes [2024 INV#WO0000000109121]	Pre-deployment
R2560	DOI-0203	DOI	Advancing image-based surveys to support sea duck conservation along the Pacific Flyway [2024 INV#WO0000000109096]	Pre-deployment
R2563	DOI-0200	DOI	Predicting inundation dynamics of small forested wetlands [2024 INV#WO0000000109089]	Pre-deployment
R2565	DOI-0198	DOI	Environmental streamflows in the United States: historical patterns and predictions [2024 INV#WO0000000109075]	Pre-deployment
R2574	DOI-0189	DOI	Zero shot segmentation to expedite Quaternary geologic mapping [2024 INV#WO0000000108739; 2024 INV#WO0000000109150]	Pre-deployment
R2576	DOI-0187	DOI	Tracking wetlands and water movement across watersheds [2024 INV#WO0000000108734]	Pre-deployment
R2577	DOI-0186	DOI	Everglades-Flux, Digital Surveys [2024 INV#WO0000000108630]	Pre-deployment
R2580	DOI-0183	DOI	Cell Phone Application for Oil Spill Detection [2024 INV#WO0000000108285]	Pre-deployment
R2582	DOI-0181	DOI	National Wildlife Disease Database (NWDD) [2024 INV#WO0000000108149; 2024 INV#WO0000000109192]	Pre-deployment
R2596	DOI-0164	DOI	Mapping ecohydrological headwater refugia	Pre-deployment
R2599	DOI-0167	DOI	Machine learning for streamflow forecasting [2024 INV#WO0000000109317]	Pre-deployment
R2602	DOI-0157	DOI	Remote sensing of particulate and filter passing mercury species: models and proxies	Pre-deployment
R2605	DOI-0154	DOI	An integrated sensor network and data driven approach to satellite remote sensing of Dissolved Organic Matter	Pre-deployment
R2608	DOI-0153	DOI	Machine Learning for Rapid Earthquake Magnitude Estimation	Pre-deployment
R2609	DOI-0152	DOI	Lead attribution model	Pre-deployment
R2613	DOI-0146	DOI	Machine learning for stream velocity prediction	Pre-deployment
R2616	DOI-0143	DOI	Predictive AI applications for estimating water quality constituents as causal factors of harmful algal blooms.	Pre-deployment
R2617	DOI-0142	DOI	Biotic and abiotic drivers of the prevalence of a tick and associated vector-borne disease	Pre-deployment
R2618	DOI-0141	DOI	Enhancing Community and Wildlife Resilience to Sea?Level Rise and Infrastructure Development in the San Pablo Baylands	Pre-deployment
R2619	DOI-0139	DOI	Predicting from the past - identifying characteristics of invasion-resistant and invasion-prone waterbodies to aid horizon scanning	Pre-deployment
R2621	DOI-0138	DOI	Using High-Resolution Imagery and Artificial Intelligence to Support Climate Change Resilience in Agroforestry Across the Pacific	Pre-deployment
R2624	DOI-0134	DOI	Machine Learning for High-Resolution Downscaling in the Hawaiian Islands	Pre-deployment
R2626	DOI-0133	DOI	Global Detection of Historical Harmful Algal Blooms via combined Satellite Data and Deep Learning Methods	Pre-deployment
R2628	DOI-0131	DOI	Feature extraction and characterization for update of The National Map	Pre-deployment
R2629	DOI-0130	DOI	Supervised learning for phycocyanin estimation from Sentinel-2 MSI in inland waters with independent validation	Pre-deployment
R2631	DOI-0125	DOI	MAGMA-net: Multimodal analysis for geophysical monitoring of activity	Pre-deployment
R2636	DOI-0124	DOI	Animas River Metals Surrogate	Pre-deployment
R2637	DOI-0121	DOI	[Un]supervised clustering of [non-]earthquake signals commonly recorded on regional seismic networks	Pre-deployment
R2638	DOI-0110	DOI	Inference of PFAS precursor compositions	Pre-deployment
R2639	DOI-0118	DOI	Using Graph Neural Networks for development of nonergodic earthquake ground-motion models	Pre-deployment
R2649	DOI-0107	DOI	Storm Induced Erosion Response Network	Pre-deployment
R2652	DOI-0104	DOI	Improving recreation opportunities and access to public lands through machine learning and transportation planning	Pre-deployment

Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory