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Snapshot · OMB 2025 inventory · processed Oct 11, 2026
Federal AI Intelligence · U.S. Government AI Observatory

Custom inventory report

Filter: agency = DOI; stage = pilot

Generated 2026-10-12 07:57 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 = DOI; stage = pilot · 89 records

Key findings

  • 89 use-case records from 1 agencies: 0 deployed (0%), 89 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, 90% involve in-house development.

Lifecycle

StageRecordsShare
Pilot89100%

AI technology

ClassificationRecordsShareDeployed
Generative AI67%0
Classical / predictive ML6371%0
Natural language processing11%0
Computer vision1315%0
Agentic AI33%0
Other33%0

Agencies

AgencyRecordsDeployedHigh-impactCompleteness
DOI · Department of the Interior8900100%

High-impact designation

DesignationRecords
Not high-impact89

Governance evidence: use cases designated high-impact

PracticeIn placeIn progressNot in placeN/A or precludedOtherNo answerIn scope
Pre-deployment testing0000000
AI impact assessment0000000
Independent review0000000
Ongoing monitoring0000000
Operator training0000000
Fail-safe0000000
Appeal process0000000
User and public consultation0000000

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

RecordAgency IDAgencyUse caseStage
R2515DOI-0248DOIVoiceAtlas no-code chatbot frameworkPilot
R2518DOI-0245DOIMachine Learning approach to predict the composition of seafloor massive sulfide deposits [2024 INV#WO0000000108420]Pilot
R2520DOI-0243DOIGoogle Cloud Vision [2024 INV#WO0000000154445]Pilot
R2521DOI-0242DOIGoogle Vertex AI Document workbench [2024 INV#WO0000000154393]Pilot
R2522DOI-0241DOIUSGS Azure OpenAI ChatGPT [2024 INV#WO0000000154392]Pilot
R2526DOI-0237DOIPrioritized Constituents: Sediment [2024 INV#WO0000000109726]Pilot
R2529DOI-0234DOIData-Driven Streamflow Drought [2024 INV#WO0000000109714]Pilot
R2530DOI-0233DOINational-Extent Groundwater Quality Prediction for the National Water Census and Regional Integrated Water Availability Assessments [2024 INV#WO0000000109709]Pilot
R2531DOI-0232DOIUse of artificial intelligence tools for optimization and documentation for computer codes [2024 INV#WO0000000109681]Pilot
R2534DOI-0229DOIVegetation mapping on the Hawaiian island of Lanai [2024 INV#WO0000000109501]Pilot
R2536DOI-0227DOIPopulation and critical habitat modeling of overwintering monarch butterflies [2024 INV#WO0000000109410]Pilot
R2538DOI-0225DOIAutomated otolith aging using image processing [2024 INV#WO0000000109315]Pilot
R2539DOI-0224DOIMachine learning for tsunami source zones [2024 INV#WO0000000109313]Pilot
R2540DOI-0223DOIOceanographic, coastal, and geomorphic change analysis: data generation, QC/QA, and data management [2024 INV#WO0000000109310]Pilot
R2541DOI-0222DOIQuantifying the effects of land-use change and bioenergy crop production on pollinators, wildlife, and ecosystem services in the Northern Great Plains [2024 INV#WO0000000109305]Pilot
R2542DOI-0221DOIComputationally efficient emulation of spheroidal elastic deformation sources using machine learning [2024 INV#WO0000000109302]Pilot
R2544DOI-0219DOIMachine Learning to evaluate water quality [2024 INV#WO0000000109241]Pilot
R2545DOI-0218DOIEcological niche models for bat species [2024 INV#WO0000000109233]Pilot
R2547DOI-0216DOIQuantifying Watershed Controls on Fine Sediment Flux to Lake Tahoe, California/Nevada [2024 INV#WO0000000109215]Pilot
R2548DOI-0215DOISeismology of Magmatic Injection [2024 INV#WO0000000109214]Pilot
R2549DOI-0214DOIEarthquake Catalog Development [2024 INV#WO0000000109208]Pilot
R2550DOI-0213DOISeedling Identification and Percent Growth Analysis [2024 INV#WO0000000109200]Pilot
R2551DOI-0212DOIGulf Coast Geologic Energy Machine Learning [2024 INV#WO0000000109198]Pilot
R2553DOI-0210DOIUsing machine learning to detect invasive bullfrogs [2024 INV#WO0000000109159]Pilot
R2554DOI-0209DOIDeep Learning application for automated mapping of surficial landforms, surficial geological deposits, and abandoned mine sites from lidar-derived topography [2024 INV#WO0000000109153]Pilot
R2559DOI-0204DOILava lake thermal pattern classification using self organizing maps and relationships to eruption processes at Kilauea Volcano, Hawaii [2024 INV#WO0000000109098]Pilot
R2561DOI-0202DOIInSAR and other geodetic studies at Volcanoes [2024 INV#WO0000000109093]Pilot
R2564DOI-0199DOIMachine-learning model to delineate sub-surface agricultural drainage from satellite imagery [2024 INV#WO0000000109078]Pilot
R2566DOI-0197DOIExtracting robust, searchable data from narrative geologic descriptions [2024 INV#WO0000000109022]Pilot
R2567DOI-0196DOIClassifying GPS data to understand flight behavior of birds [2024 INV#WO0000000109015]Pilot
R2568DOI-0195DOIWhole-lake indexing of round goby abundances with photographic catch data [2024 INV#WO0000000109010]Pilot
R2569DOI-0194DOIPredicting PFAS in shallow soils in northern New England [2024 INV#WO0000000108973]Pilot
R2570DOI-0193DOIImproving accuracy and precision of sonar-based estimates of fish abundance [2024 INV#WO0000000108800]Pilot
R2571DOI-0192DOIMachine learning-based landscape feature classification using satellite and airborne imagery [2024 INV#WO0000000108791; 2024 INV#WO0000000108794]Pilot
R2572DOI-0191DOIPredicting PFAS occurrence in groundwater using machine learning [2024 INV#WO0000000108780]Pilot
R2573DOI-0190DOIMachine Learning Image Classification of Wetlands and Soil moisture [2024 INV#WO0000000108779]Pilot
R2575DOI-0188DOIInventorying landforms with convolutional neural networks [2024 INV#WO0000000108738; WO0000000109117]Pilot
R2578DOI-0185DOIShoreline modeling [2024 INV#WO0000000108297; 2024 INV#WO0000000109312]Pilot
R2581DOI-0182DOIWave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies [2024 INV#WO0000000108262]Pilot
R2584DOI-0179DOISediment Transport in Coastal Environments [2024 INV#WO0000000108118]Pilot

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