Federal AI Intelligence · U.S. Government AI Observatory

Federal AI landscape: executive briefing

Filtered: stage = pilot; appeal = in_progress

Generated 2026-10-13 10:03 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: stage = pilot; appeal = in_progress · 11 records

Key findings

11 use-case records from 1 agencies: 0 deployed (0%), 11 pilots, 0 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 100% of use cases. Where reported, 100% involve in-house development.

Lifecycle

Stage	Records	Share
Pilot	11	100%

AI technology

Classification	Records	Share
Generative AI	1	9%
Classical / predictive ML	6	55%
Natural language processing	1	9%
Computer vision	3	27%

Agencies

Agency	Records	Deployed	High-impact	Completeness
DOI · Department of the Interior	11	0	0	100%

High-impact designation

Designation	Records
Not high-impact	11

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

All 11 records.

Record	Agency ID	Agency	Use case	Stage
R2588	DOI-0175	DOI	Deep Learning based image segmentation [2024 INV#WO0000000107975]	Pilot
R2591	DOI-0172	DOI	Foundation Models to Advance Earth Science [2024 INV#WO0000000107153]	Pilot
R2642	DOI-0109	DOI	LLM-Assisted Volcanic Alert Monitoring	Pilot
R2665	DOI-0088	DOI	Reducing elevation error in coastal wetland digital elevation models	Pilot
R2666	DOI-0087	DOI	Developing land cover maps for barrier islands using satellite imagery	Pilot
R2671	DOI-0061	DOI	Invasive Carp Harvest Predictive Model	Pilot
R2677	DOI-0079	DOI	Flor-AI: Developing a Remotely Sensed Image Classification Method for Inventory and Monitoring of Flora in Digital UAS Imagery	Pilot
R2678	DOI-0075	DOI	Automating the Detection and Classification of Wildlife in Aerial Imagery [2024 INV#WO0000000109409]	Pilot
R2679	DOI-0074	DOI	Automated identification of ducks from hunter-submitted photos using deep learning models	Pilot
R2680	DOI-0073	DOI	Automated photographic identification of Eastern box turtles	Pilot
R2682	DOI-0071	DOI	Pathogen identification in salmon	Pilot

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