Federal AI Intelligence · U.S. Government AI Observatory

Federal AI landscape: executive briefing

Filtered: agency = NASA

Generated 2026-10-12 02:58 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 = NASA · 425 records

Key findings

425 use-case records from 1 agencies: 26 deployed (6%), 21 pilots, 373 in development or acquisition, 5 retired, 0 with no stage reported.
1 records are designated high-impact; 0 of them (0%) answered at least one minimum-practice question.
15% of reported deployed use cases (4 of 26) are classified as generative or agentic AI.
Of 1 deployed high-impact use cases, 0 report a completed AI impact assessment and 1 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 11% of use cases. Where reported, 98% involve in-house development.

Lifecycle

Stage	Records	Share
Deployed	26	6%
Pilot	21	5%
Pre-deployment	373	88%
Retired	5	1%

AI technology

Classification	Records	Share	Deployed
Generative AI	37	9%	0
Classical / predictive ML	282	66%	13
Natural language processing	27	6%	4
Computer vision	47	11%	5
Agentic AI	23	5%	4
Reinforcement learning	4	1%	0
Other	4	1%	0
Not reported	1	0%	0

Agencies

Agency	Records	Deployed	High-impact	Completeness
NASA · National Aeronautics and Space Administration	425	26	1	63%

High-impact designation

Designation	Records
High-impact	1
Presumed high-impact, determined not	3
Not high-impact	421

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

First 40 of 425 records, deployed first. The full list is available as CSV from the explorer with the same filters.

Record	Agency ID	Agency	Use case	Stage
R0117	NASA-25	NASA	NextGen Advanced Methods: ATCSCC Webinar Speech2Text and Analysis	Deployed
R0118	NASA-26	NASA	NextGen Data Analytics: Letters of Agreement	Deployed
R0161	NASA-214	NASA	AEGIS: Autonomous Exploration for Gathering Increased Science	Deployed
R0163	NASA-217	NASA	ASPEN Mission Planner	Deployed
R0164	NASA-221	NASA	CLASP Coverage Planning & Scheduling	Deployed
R0166	NASA-225	NASA	Enhanced AutoNav for Perseverance Rover on Mars	Deployed
R0169	NASA-232	NASA	Mars2020 Rover (Perseverance)	Deployed
R0170	NASA-234	NASA	MLNav (Machine Learning Navigation)	Deployed
R0172	NASA-237	NASA	Perseverance Rover on Mars - Terrain Relative Navigation	Deployed
R0175	NASA-244	NASA	SPOC (Soil Property and Object Classification)	Deployed
R0204	NASA-334	NASA	NASA OCIO STI Concept Tagging Service	Deployed
R0216	NASA-365	NASA	GCMD Keyword Recommender (GKR)	Deployed
R0226	NASA-398	NASA	Autonomous WAiting Room Evaluation (AWARE)	Deployed
R0227	NASA-401	NASA	Pre-trained microscopy image neural network encoders	Deployed
R0248	NASA-450	NASA	Onboard Planner for Mars2020 Rover (Perseverance)	Deployed
R0249	NASA-451	NASA	SensorWeb: Volcano, Flood, Wildfire, and others.	Deployed
R0393	NASA-708	NASA	Global, Seasonal Mars Frost Maps	Deployed
R0397	NASA-715	NASA	Purchase Card Management System (PCMS)	Deployed
R0398	NASA-716	NASA	New Technology and Software Reporting (NTR)	Deployed
R0416	NASA-753	NASA	Intelligent Camera Analytics for Security and Operations	Deployed
R0453	NASA-848	NASA	Near-real-time aerosol retrievals from OMPS Limb Profiler measurements	Deployed
R0454	NASA-849	NASA	Retrieving stratospheric water vapor from OMPS Limb Profiler measurements	Deployed
R0458	NASA-854	NASA	Application of ML to Detection of Anomalies in Spacecraft Health and Status Data	Deployed
R0497	NASA-900	NASA	GLOBE Program - Image processing	Deployed
R0519	NASA-927	NASA	Data analysis and pattern recognition	Deployed
R0520	NASA-928	NASA	Dirty Vacuum rated 6-Axis Robotic Arm Toolpathing	Deployed
R0104	NASA-1	NASA	Anomaly Detection and Precursor Identification in UAV flight data	Pre-deployment
R0105	NASA-5	NASA	Autoresolver/Tailored Arrival Manager	Pre-deployment
R0106	NASA-8	NASA	Deep Learning for Flood Mapping (DELTA)	Retired
R0107	NASA-9	NASA	Distributed Spacecraft Autonomy	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
R0110	NASA-13	NASA	Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model	Pilot
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

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