Federal AI Intelligence · U.S. Government AI Observatory

Custom inventory report

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

Generated 2026-10-12 13:45 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 = Energy and the Environment; AI type = classical_ml; sourcing = unknown · 28 records

Key findings

28 use-case records from 3 agencies: 0 deployed (0%), 0 pilots, 28 in development or acquisition, 0 retired, 0 with no stage reported.
1 records are designated high-impact; 0 of them (0%) 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	28	100%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	28	100%	0

Agencies

Agency	Records	High-impact	Completeness
DOE · Department of Energy	16	1	58%
DOI · Department of the Interior	10	0	83%
USDA · Department of Agriculture	2	0	58%

High-impact designation

Designation	Records
High-impact	1
Presumed high-impact, determined not	2
Not high-impact	25

Governance evidence: use cases designated high-impact

Practice	No answer	In scope
Pre-deployment testing	1	1
AI impact assessment	1	1
Independent review	1	1
Ongoing monitoring	1	1
Operator training	1	1
Fail-safe	1	1
Appeal process	1	1
User and public consultation	1	1

"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 28 records.

Record	Agency ID	Agency	Use case	Stage
R1445	USDA-147	USDA	Late seral forest complexity	Pre-deployment
R1451	USDA-153	USDA	Forest demographic modelling	Pre-deployment
R2596	DOI-0164	DOI	Mapping ecohydrological headwater refugia	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
R2701	DOI-0048	DOI	Using Machine Learning to Automate Crack Mapping and Structural Health Monitoring	Pre-deployment
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
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
R3452	DOE-597	DOE	Offshore AIIM Dashboard	Pre-deployment
R3478	DOE-622	DOE	Enhancing the circularity: Cost effective battery de-energization, disassembly, and pre-processing (CEBDDP)	Pre-deployment
R3485	DOE-629	DOE	Consolidated Nuclear Waste Glass Database	Pre-deployment
R3535	DOE-678	DOE	Smart CO2 Transport-Route Planning Tool	Pre-deployment
R3570	DOE-713	DOE	Performance Monitoring at the Salt Waste Processing Facility (SWPF)	Pre-deployment
R3594	DOE-736	DOE	Identifying Controlling Variables for Mercury Vapor Release at Y-12's Alpha-4	Pre-deployment
R3608	DOE-93	DOE	To drive insights on the power system reliability, cost, and operations during the energy transition with and without FECM technologies	Pre-deployment
R3609	DOE-94	DOE	To drive insights on the dependencies between the natural gas and electricity sectors to increase reliability of the NG system	Pre-deployment
R3610	DOE-98	DOE	Data platform to expedite access and reuse of carbon ore data for materials, manufacturing and research	Pre-deployment

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