Federal AI Intelligence · U.S. Government AI Observatory

Federal AI landscape: executive briefing

Filtered: agency = DOE; stage = pre_deployment

Generated 2026-10-12 11:22 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 = DOE; stage = pre_deployment · 149 records

Key findings

149 use-case records from 1 agencies: 0 deployed (0%), 0 pilots, 149 in development or acquisition, 0 retired, 0 with no stage reported.
21 records are designated high-impact; 0 of them (0%) answered at least one minimum-practice question.
Sourcing is reported for 32% of use cases. Where reported, 90% involve in-house development.

Lifecycle

Stage	Records	Share
Pre-deployment	149	100%

AI technology

Classification	Records	Share
Generative AI	51	34%
Classical / predictive ML	71	48%
Natural language processing	9	6%
Computer vision	5	3%
Agentic AI	7	5%
Reinforcement learning	1	1%
Other	5	3%

Agencies

Agency	Records	Deployed	High-impact	Completeness
DOE · Department of Energy	149	0	21	72%

High-impact designation

Designation	Records
High-impact	21
Presumed high-impact, determined not	4
Not high-impact	124

Governance evidence: use cases designated high-impact

Practice	No answer	In scope
Pre-deployment testing	21	21
AI impact assessment	21	21
Independent review	21	21
Ongoing monitoring	21	21
Operator training	21	21
Fail-safe	21	21
Appeal process	21	21
User and public consultation	21	21

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

Record	Agency ID	Agency	Use case	Stage
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
R3278	DOE-218	DOE	To help automate data discovery and preparations to support a range of CS models, tools, and products	Pre-deployment
R3279	DOE-328	DOE	ORNL: Foundational AI Research	Pre-deployment
R3280	DOE-329	DOE	ORNL: AI for Materials Science	Pre-deployment
R3281	DOE-330	DOE	ORNL: AI for Experimental Facilities Operations	Pre-deployment
R3282	DOE-331	DOE	ORNL: AI for Transportation and Mobility	Pre-deployment
R3283	DOE-333	DOE	ORNL: AI for Energy Generation and Distribution	Pre-deployment
R3284	DOE-334	DOE	ORNL: AI for Advanced Manufacturing	Pre-deployment
R3285	DOE-336	DOE	ORNL: AI for Bio and Health Sciences	Pre-deployment
R3286	DOE-337	DOE	ORNL: AI for the Smart Laboratory	Pre-deployment
R3287	DOE-338	DOE	ORNL: AI for Neutron Science	Pre-deployment
R3288	DOE-339	DOE	ORNL: AI for Earth Systems	Pre-deployment
R3289	DOE-340	DOE	ORNL: AI for Fusion Energy	Pre-deployment
R3294	DOE-352	DOE	APT Analytics	Pre-deployment
R3296	DOE-354	DOE	Laboratory Automation	Pre-deployment
R3299	DOE-357	DOE	Managing curb allocation in cities	Pre-deployment
R3301	DOE-359	DOE	Use of developed ML techniques to parse opensource text-based information.	Pre-deployment
R3302	DOE-360	DOE	AI techniques for identification of suitable delivery parking spaces in an urban scenario	Pre-deployment
R3303	DOE-361	DOE	Surrogate models for probabilistic Bayesian inference	Pre-deployment
R3326	DOE-451	DOE	ServiceNow AI Search	Pre-deployment
R3329	DOE-454	DOE	AI for High Risk Property	Pre-deployment
R3330	DOE-455	DOE	Records Digitization	Pre-deployment
R3340	DOE-472	DOE	Argonne Resilience AI Assistant ARAIA (nee CALLM)	Pre-deployment
R3342	DOE-476	DOE	AI for Isotopes (Pellet) Inspection	Pre-deployment
R3348	DOE-485	DOE	Unleashing AI Transformer Models on FPGAs for Accelerating LHC and Particle Physics	Pre-deployment
R3366	DOE-506	DOE	Advanced Peer to Peer Transactive Energy Platform with Predictive Optimization	Pre-deployment
R3368	DOE-508	DOE	ServiceNow Predictive Intelligence	Pre-deployment
R3370	DOE-511	DOE	Ariculate 360 AI Assistant	Pre-deployment
R3372	DOE-513	DOE	Nanoparticle growth kinetics and mechanism	Pre-deployment
R3374	DOE-516	DOE	Center for Mesoscale Transport Properties	Pre-deployment
R3375	DOE-517	DOE	Develop a Machine Learning Framework for Optimal Computational Campaigns for Complex Uncertain Systems	Pre-deployment
R3378	DOE-520	DOE	NLP Data Analytics for Program and Portfolio Insights	Pre-deployment
R3379	DOE-521	DOE	Command Media	Pre-deployment
R3380	DOE-522	DOE	Ask CAS	Pre-deployment
R3381	DOE-523	DOE	AI/ML in High Energy Physics Research	Pre-deployment
R3388	DOE-531	DOE	IDAES-PSE	Pre-deployment

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