Federal AI Intelligence · U.S. Government AI Observatory

Federal AI landscape: executive briefing

Filtered: AI type = classical_ml; sourcing = unknown; feedback = blank

Generated 2026-10-12 20:59 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: AI type = classical_ml; sourcing = unknown; feedback = blank · 555 records

Key findings

555 use-case records from 18 agencies: 5 deployed (1%), 0 pilots, 550 in development or acquisition, 0 retired, 0 with no stage reported.
43 records are designated high-impact; 0 of them (0%) answered at least one minimum-practice question.
NASA, DOE, DOI report 380 of 555 use cases (68%).
0% of reported deployed use cases (0 of 5) are classified as generative or agentic AI.
Of 3 deployed high-impact use cases, 0 report a completed AI impact assessment and 3 leave every minimum-practice field blank.
Interpretation: Blank practice fields are missing evidence, not evidence that a safeguard is absent. They mark where follow-up questions to the agency are most useful.
Sourcing is reported for 0% of use cases. Where reported, n/a involve in-house development.

Lifecycle

Stage	Records	Share
Deployed	5	1%
Pre-deployment	550	99%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	555	100%	5

Agencies

Agency	Records	Deployed	High-impact	Completeness
NASA · National Aeronautics and Space Administration	258	0	0	58%
DOE · Department of Energy	64	0	6	61%
DOI · Department of the Interior	58	0	0	83%
VA · Department of Veterans Affairs	34	0	21	58%
DOJ · Department of Justice	31	0	7	58%
HHS · Department of Health and Human Services	29	0	1	58%
USDA · Department of Agriculture	19	0	3	58%
DHS · Department of Homeland Security	15	0	1	58%
FDIC · Federal Deposit Insurance Corporation	10	0	0	58%
SEC · Securities and Exchange Commission	7	0	0	58%
FRB · Federal Reserve Board	7	0	0	58%
GSA · General Services Administration	6	2	0	33%
EPA · Environmental Protection Agency	5	0	1	58%
TREAS · Department of the Treasury	4	0	0	60%
NCUA · National Credit Union Administration	3	3	3	75%
DOT · Department of Transportation	2	0	0	58%
STATE · Department of State	2	0	0	58%
NEA · National Endowment for the Arts	1	0	0	58%

High-impact designation

Designation	Records
High-impact	43
Presumed high-impact, determined not	13
Not high-impact	493
Not reported	6

Governance evidence: use cases designated high-impact

Practice	No answer	In scope
Pre-deployment testing	43	43
AI impact assessment	43	43
Independent review	43	43
Ongoing monitoring	43	43
Operator training	43	43
Fail-safe	43	43
Appeal process	43	43
User and public consultation	43	43

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

Record	Agency ID	Agency	Use case	Stage
R1179		GSA	Elastic Machine Learning Threat Detection (Phase 1)	Deployed
R1180		GSA	Elastic Machine Learning Threat Detection (Phase 2)	Deployed
R2437	NCUA-12-2025	NCUA	Moody’s Stress Test Modeling	Deployed
R2438	NCUA-13-2025	NCUA	ADCo MBS/CMO Modeling	Deployed
R2439	NCUA-14-2025	NCUA	CoStar CRE Modeling	Deployed
R0009	SEC-9	SEC	CF Staff Review Comments	Pre-deployment
R0019	SEC-21	SEC	Identification of Potentially Manipulative Trading Activity in Registrant Data	Pre-deployment
R0047	SEC-54	SEC	Enhancing Analysis of SEC Web Engagement Trends Using AI	Pre-deployment
R0048	SEC-55	SEC	Identifying Citations of SEC Publications to Measure Market Impact	Pre-deployment
R0049	SEC-56	SEC	Analyzing Registered Offerings Data to Understand Capital Formation Trends	Pre-deployment
R0050	SEC-57	SEC	Analyzing EDGAR Search Trends to Understand Public Interest in Filings	Pre-deployment
R0051	SEC-58	SEC	Analyzing Form ADV Data to Identify Trends Among Investment Advisers	Pre-deployment
R0104	NASA-1	NASA	Anomaly Detection and Precursor Identification in UAV flight data	Pre-deployment
R0108	NASA-10	NASA	ExoMiner discovery of ExoPlanets via data from the Kepler and TESS space telescopes	Pre-deployment
R0109	NASA-11	NASA	Explainable and robust deep semi-supervised model for multi-class anomaly detection in flight data	Pre-deployment
R0111	NASA-18	NASA	Machine Learning Airport Surface Model: Airport Configuration Prediction	Pre-deployment
R0112	NASA-19	NASA	Machine Learning Airport Surface Model: Arrival Runway Prediction	Pre-deployment
R0113	NASA-20	NASA	Machine Learning Airport Surface Model: Departure Runway Prediction	Pre-deployment
R0114	NASA-21	NASA	Machine Learning Airport Surface Model: Estimated ON Time Prediction	Pre-deployment
R0115	NASA-22	NASA	Machine Learning Airport Surface Model: Taxi-in Prediction	Pre-deployment
R0116	NASA-23	NASA	Machine Learning Airport Surface Model: Taxi-out Prediction	Pre-deployment
R0119	NASA-33	NASA	Unsupervised anomaly detection in flight data with deep variational autoencoders	Pre-deployment
R0120	NASA-35	NASA	Aero-Engines AI - a machine-learning app for aircraft engine system-performance prediction	Pre-deployment
R0121	NASA-40	NASA	Graph Neural Networks for Airfoil Performance Prediction	Pre-deployment
R0122	NASA-41	NASA	Inverse Design of Materials	Pre-deployment
R0124	NASA-64	NASA	Applying machine learning techniques to enhance the sensitivity and selectivity of multifunctional sensor platform	Pre-deployment
R0125	NASA-66	NASA	Artificial Intelligent Co-Processor Slice with Google Coral TPU For Automated Onboard Data-Product Generation	Pre-deployment
R0128	NASA-93	NASA	Deep Learning for Communication-Limited Spacecraft	Pre-deployment
R0129	NASA-97	NASA	Development of new tools for detecting and assessing resilient agriculural systems farm performance	Pre-deployment
R0130	NASA-100	NASA	Earth Information System Fire Pilot	Pre-deployment
R0131	NASA-105	NASA	Enabling Autonomous Differential Drag Control and Attitude Maneuvering Using Onboard Artificial Intelligence (AI) for SmallSat Distributed Space Missions (DSMs)	Pre-deployment
R0132	NASA-113	NASA	Forest responses to climate change	Pre-deployment
R0133	NASA-123	NASA	From Single Spacecraft to Synchronized Swarms: Fault Diagnosis for Distributed Spacecraft Mission Resilience	Pre-deployment
R0134	NASA-127	NASA	HEASARC X-ray Spectra and Light Curve data sets	Pre-deployment
R0135	NASA-128	NASA	High Rate Digital Spectrometer Enhancement with Neural Network AI	Pre-deployment
R0136	NASA-129	NASA	High Resolution Earth and Planetary Atmospheric Predictions using Machine Learning	Pre-deployment
R0137	NASA-133	NASA	HyperMapping with Hyperspectral Precise Pointing Optical Sensor (HYPPOS) for Decadal Survey Mission Pathfinder Activities	Pre-deployment
R0139	NASA-148	NASA	M4OPT: Multi-Mission Multi-Messenger Observation Planning Toolkit	Pre-deployment
R0140	NASA-149	NASA	Machine Intelligence for Small Satellites	Pre-deployment
R0141	NASA-154	NASA	Machine Learning of Ocean Worlds Laboratory Analog Seawater Volatiles	Pre-deployment

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