Federal AI Intelligence · U.S. Government AI Observatory

Custom inventory report

Filter: topic = Energy and the Environment; AI type = classical_ml

Generated 2026-10-12 14:43 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 = Energy and the Environment; AI type = classical_ml · 49 records

Key findings

49 use-case records from 4 agencies: 10 deployed (20%), 8 pilots, 31 in development or acquisition, 0 retired, 0 with no stage reported.
2 records are designated high-impact; 0 of them (0%) answered at least one minimum-practice question.
DOE, DOI, USDA report 48 of 49 use cases (98%).
0% of reported deployed use cases (0 of 10) are classified as generative or agentic AI.
Sourcing is reported for 43% of use cases. Where reported, 76% involve in-house development.

Lifecycle

Stage	Records	Share
Deployed	10	20%
Pilot	8	16%
Pre-deployment	31	63%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	49	100%	10

Agencies

Agency	Records	Deployed	High-impact	Completeness
DOE · Department of Energy	23	4	2	71%
DOI · Department of the Interior	17	1	0	89%
USDA · Department of Agriculture	8	4	0	90%
EPA · Environmental Protection Agency	1	1	0	100%

High-impact designation

Designation	Records
High-impact	2
Presumed high-impact, determined not	2
Not high-impact	45

Governance evidence: use cases designated high-impact

Practice	No answer	In scope
Pre-deployment testing	2	2
AI impact assessment	2	2
Independent review	2	2
Ongoing monitoring	2	2
Operator training	2	2
Fail-safe	2	2
Appeal process	2	2
User and public consultation	2	2

"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 49 records, deployed first. The full list is available as CSV from the explorer with the same filters.

Record	Agency ID	Agency	Use case	Stage
R1321	USDA-014	USDA	Ecosystem Management Decision Support System (EMDS)	Deployed
R1347	USDA-046	USDA	Rangeland Analysis Platform	Deployed
R1380	USDA-080	USDA	BirdNET to detect bird vocalizations for research and species monitoring	Deployed
R1388	USDA-089	USDA	AI for regional forest mapping and monitoring	Deployed
R2052	77	EPA	Using machine learning methods to analyze drivers of water quality	Deployed
R2627	DOI-0135	DOI	Classifying CWD-Infected Elk Using Recurrent Neural Networks on GPS Movement Data	Deployed
R3277	DOE-195	DOE	Machine Learning for geophysical data inversion	Deployed
R3300	DOE-358	DOE	Regional waste feedstock conversion to biofuels	Deployed
R3501	DOE-645	DOE	Georeference Figures	Deployed
R3515	DOE-659	DOE	Groundwater Modeling	Deployed
R1445	USDA-147	USDA	Late seral forest complexity	Pre-deployment
R1449	USDA-151	USDA	Predicting fire severity potential in future wildfires in California	Pilot
R1451	USDA-153	USDA	Forest demographic modelling	Pre-deployment
R1456	USDA-158	USDA	Washington state forest inventory maps	Pilot
R2507	DOI-0256	DOI	Well Activity Report Classification	Pre-deployment
R2596	DOI-0164	DOI	Mapping ecohydrological headwater refugia	Pre-deployment
R2600	DOI-0163	DOI	Determining the resource potential of critical minerals in seafloor massive sulfide deposits [2024 INV#WO0000000109311]	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
R2625	DOI-0137	DOI	Random forest models for predicting water quality of inland waters from remotely sensed imagery	Pilot
R2701	DOI-0048	DOI	Using Machine Learning to Automate Crack Mapping and Structural Health Monitoring	Pre-deployment
R2702	DOI-0047	DOI	Seasonal Water Supply Forecasting: Pyforecast [2024 INV#DOI-69]	Pilot
R2703	DOI-0046	DOI	Piloting Machine Learning Inflow Forecasts Across Reclamation [2024 INV#DOI-58]	Pilot
R2704	DOI-0045	DOI	Machine Learning Refines Quagga Habitat Suitability [2024 INV#DOI-74 (NEW)]	Pre-deployment
R2705	DOI-0044	DOI	Machine Learning Applied to Geotechnical Engineering: Statistical Methods Applied to Seismic Analysis [2024 INV#DOI-71 (NEW)]	Pre-deployment
R2706	DOI-0043	DOI	Machine Learning for Chemical Savings at Reverse Osmosis Plants [2024 INV#DOI-73 (NEW)]	Pre-deployment
R2707	DOI-0041	DOI	Improved Processing and Analysis of Test and Operating Data from Rotating Machines [2024 INV#DOI-62]	Pre-deployment
R2709	DOI-0039	DOI	Geothermal Energy Assessments	Pre-deployment
R2710	DOI-0038	DOI	Lithium Potential Assessments	Pre-deployment
R2711	DOI-0037	DOI	Mineral Resource Assessments	Pre-deployment
R3272	DOE-118	DOE	Natural Language Processing	Pre-deployment
R3273	DOE-120	DOE	DOE AI Data Infrastructure System	Pre-deployment
R3274	DOE-125	DOE	Creation of polymer datasets and inverse design of polymers with targeted backbones having High CO2 permeability and high CO2/N2 selectivity.	Pre-deployment
R3275	DOE-150	DOE	To use AI to calibrate the simulation model by matching simulation data with production history data.	Pre-deployment
R3276	DOE-179	DOE	Data discovery, processing, and generation using machine learning for a range of CCS data and information	Pre-deployment
R3388	DOE-531	DOE	IDAES-PSE	Pre-deployment
R3389	DOE-532	DOE	NRAP-Open-IAM	Pre-deployment
R3396	DOE-539	DOE	Drone Imagery Analysis	Pilot
R3401	DOE-545	DOE	Soil Moisture Modeling	Pilot
R3452	DOE-597	DOE	Offshore AIIM Dashboard	Pre-deployment

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