GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: SQL Server Management Studio
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Microsoft Exchange Server
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Collaborative Machine learning platform for Scientific Discovery
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: New advances in scientific applied machine learning (ML) offer an opportunity to leverage the commonalities, scientific insights and collected experience of the larger scientific user facility community across different experiments and facilities.  T
Purpose and benefits: New advances in scientific applied machine learning (ML) offer an opportunity to leverage the commonalities, scientific insights and collected experience of the larger scientific user facility community across different experiments and facilities.  The result will be a shared platform that lowers the barrier to enter by leveraging the advances in machine learning methods across user facilities thus empowering domain scientists and data scientists to discover new science using existing and new data with new tools
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Performance Monitoring at the Salt Waste Processing Facility (SWPF)
Agency: Department of Energy - SRS - SRNL - Savannah River Site - Savannah River National Laboratory (EM)
Problem addressed: With data from SWPF instrumentation, train neural networks to consider process parameters and chemical speciation data from incoming salt batches to predict filtration rate performance.
Purpose and benefits: Proactive monitoring of complex facility processes with only select instrumentation data to guide prediction process operations
Topic area: Energy and Environment
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: In development stage and impact has not been determined
US federal AI use case: Advanced Long Term Environmental Monitoring Systems (ALTEMIS)
Agency: Department of Energy - SRS - SRNL - Savannah River Site - Savannah River National Laboratory (EM)
Problem addressed: Reduce the cost of long-term monitoring using integrated sensing technologies and AI/ML to forecast groundwater plume migration and anomalies.
Purpose and benefits: Proactive, rather than reactive, monitoring of complex geochemical systems.
Topic area: Energy and Environment
Development stage: Pilot
Risk category: High-impact
First used: 2022-09-01
US federal AI use case: Custom GenAI for ATR Fuel Conversion Project (FuelGPT)
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Engineers spending extensive time manually iterating through documents during the fuel conversion project.
Purpose and benefits: Engineers save time by using the AI to quickly find answers and relevant source documents.
Topic area: Other
Development stage: Pilot
Impact assessment: AI Pilot specific to one project related to HEU to LEU fuel conversion.
First used: 2025-08-04
US federal AI use case: Denoising Diffusion to Accelerate Detector Simulation
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This program aims to develop generative models for quickly simulating showers of particles in calorimeters for LHC experiments
Purpose and benefits: This effort is exploring generative AI to replace costly detector simulation. This would enable faster, more accurate simulation, accelerating and enhancing scientific results and allowing easier use of GPU coprocessors at HPC centers.
Topic area: Science
Development stage: Pilot
Impact assessment: The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public  safety
First used: 2025-09-25
US federal AI use case: Tackling Solid-State Electrochemical Interfaces from Structure to Function Utilizing HPC and Machine Learning Tools
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Applying AI/ML methods to discover better solid state battery interface material using HPC
Purpose and benefits: Applying AI/ML methods to discover better solid state battery interface material using HPC
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: 5G-enabled Reliable and Decentralized IoT Framework with Blockchain
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: We propose to develop an end-to-end, 5G-enabled, reliable, and decentralized IoT framework that improves data collection and communication among edge computing devices for science applications.
Purpose and benefits: We propose to develop an end-to-end, 5G-enabled, reliable, and decentralized IoT framework that improves data collection and communication among edge computing devices for science applications.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: ServiceNow AI Search
Agency: Department of Energy - LANL - Los Alamos National Lab (LAFO)
Problem addressed: Provide better search experience for finding knowledge articles, tickets, and other data. Ability to pull from sources external to ServiceNow
Purpose and benefits: Improved automation
Topic area: IT
Development stage: Pre-deployment
Impact assessment: ServiceNow's AI output is NOT a principal driver of a government decision that would meaningfully affects people's rights, safety, critical access, or strategic assets. AI in ServiceNow is strictly used to help manage IT resources for LANL.
First used: 2024-11-01
US federal AI use case: Extreme data reduction for the edge
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This projects develops AI algorithms and tools for near-sensor data reduction in custom hardware.
Purpose and benefits: AI tools are developed for embedded inference in real-time processing systems for scientific experiments. This can accelerate sientific discovery and time to science thus enabling large cost savings and DOE scientific prestige.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public  safety
US federal AI use case: Hub Biography Builder
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Automatically generate draft professional bios from resumes, CVs, and other inputs.
Purpose and benefits: Saves time and improves quality of professional staff bios.
Topic area: HR
Development stage: Pilot
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2025-07-01
US federal AI use case: Microsoft Skype App
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Cloud Knowledge Hub
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Answer staff questions on cloud services and best practices via an AI knowledge hub.
Purpose and benefits: Expands staff expertise and accelerates cloud adoption.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2024-10-01
US federal AI use case: Crickets
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: Facilitate analysis and user intent on detect of access to potentially inappropriate web content
Purpose and benefits: Reduce analysis cycle and response times
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2024-10-01
US federal AI use case: Neptune
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: Content analysis across domains and structured/unstructured content
Purpose and benefits: Productivity Tool
Topic area: International Affairs
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
US federal AI use case: AccessAI
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: GenAI-RAG on corporate content
Purpose and benefits: Productivity Tool
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2024-10-01
US federal AI use case: Machine Learning to support Operational Efficiency
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: Reducing risk of losing institutional knowledge associated with an aging workforce.  Ensure time to proficiency for junior operators is minimized through storing and accessing institutional knowledge to understand how operational efficiency can be im
Purpose and benefits: Less unanticipated downtime, improved decision making and improved manufacturing processes with optimized output.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The AI use case focuses on capturing operational metrics and institutional knowledge and does not significantly affect legal, material, or critical access to services rights.
First used: 2024-10-01
US federal AI use case: DNA-P Use Cases Leaverging Artificial Intellegence (Deployed)
Agency: Department of Energy - NA-IM (Enterprise) - Office of the Associate Administrator for Information Management and Chief Information Officer (HQ-Enterprise) (NNSA HQ)
Problem addressed: "- identify data clusters/trend analysis - identify data discrepencies/data enrichment - generate suggestions (including generating reports, data linkages, and courses of action) - generate graphical and natural language analyses"
Purpose and benefits: '-save time for DOE DNA-P users
- improve DOE/NNSA data quality
- improve DOE/NNSA safety operations
Topic area: Other
Development stage: Deployed
Impact assessment: This use case is determined to be not high-impact base on definitoin in Section 5 of the OMB Memorandum M-25-21.
First used: 2024-04-01
US federal AI use case: Create Statement of Work (SOW) for Procurement
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Ensure procurement SOWs meet standards through guided AI review and alignment checks.
Purpose and benefits: Ensures compliance and quality in procurement processes.
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2025-05-01
US federal AI use case: AI workflow to process voicemail user service requests and translate them into actionable IT service tickets
Agency: Department of Energy - PPPL - Princeton Plasma Physics Laboratory (SC43 OIM)
Problem addressed: Faster user support and an additional chain of communication for users to report issues. More organized and consistent information extraction from a message.
Purpose and benefits: Faster user support and an additional chain of communication for users to report issues. More organized and consistent information extraction from a message.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: AI workflow to process voicemail user service requests and translate them into actionable IT service tickets
First used: 2025-09-01
US federal AI use case: Machine Learning for Accelerator Operations Using Big Data Analytics / L-CAPE
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: Big data analytics for anomaly prediction and classification, enabling automatic mitigation, operational savings, and predictive maintenance of the Fermilab LINAC
Purpose and benefits: ML models are deployed for the FNAL's Linac.to detect, label andact upon faults.  The usage of ML will jimporve our fault labelingand detection.  This will allow for improved operatioal efficeincy, fault statistics, and preventitive maintenance. To my knowledge this is the first global accelerator operations ML system.
Topic area: Science
Development stage: Pilot
Impact assessment: The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public  safety
First used: 2025-09-25
US federal AI use case: AI/ML in Particle Accelerator Controls System
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Improving the safety and performance of particle accelerator operations through artificial intelligence assisted control systems.
Purpose and benefits: Improving the safety and performance of particle accelerator operations through artificial intelligence assisted control systems.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Expanding Consumer Participation in Consumer Electronics Recycling Programs Utilizing Targeted Marketing Campaigns
Agency: Department of Energy - EE HQ - EE Headquarters (EE)
Problem addressed: Identify laws and regulations related to e-waste
Purpose and benefits: Consolidate and provide a more convenient venue to find laws and regulations related to e-waste disposal and recycling
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: Provides advisory information only. AI use will be used once to compile laws and regulations and will not be ongoing use.
US federal AI use case: Chatlab
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Not available
Topic area: Other
Development stage: Pilot
Impact assessment: Used for general purose generative AI within a COTS product.
US federal AI use case: CoPilot
Agency: Department of Energy - OREM - ORCC - Oak Ridge EM - Oak Ridge Cleanup Contract (EM)
Problem addressed: Communication suggestions
Purpose and benefits: better communication
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: Not full deployment; testing only in preparation for forced Microsoft roll out in October 2025
First used: 2025-08-19
US federal AI use case: Microsoft OneNote MUI
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Identifying Controlling Variables for Mercury Vapor Release at Y-12's Alpha-4
Agency: Department of Energy - SRS - SRNL - Savannah River Site - Savannah River National Laboratory (EM)
Problem addressed: Correlate indoor/outdoor meteorological conditions with mercury vapor releases such that PEL exceedances can be forecasted, improving respiratory worker safety and enhancing work planning
Purpose and benefits: Intraday forecast of mercury concentration in buildings given past chronology of meteorological conditions
Topic area: Energy and Environment
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: FY19 Lab Call – Livewire Data Sharing Platform
Agency: Department of Energy - EE HQ - EE Headquarters (EE)
Problem addressed: Lack of easily-accessible, organized information on transportation and mobility-related projects.
Purpose and benefits: Enable time savings and quick answers to queries
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Exploring different methods to aid data discovery and providing data information within the platform
US federal AI use case: Microsoft Copilot Studio
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Build enterprise conversational AI agents that securely connect to data and workflows.
Purpose and benefits: Enables secure, scalable automation of enterprise workflows.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2024-10-01
US federal AI use case: Geo Threat Observable for structure cyber threat related to the energy sector
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Correlations, recommendations and predictions for improved cyber response.
Topic area: Other
Development stage: Deployed
Impact assessment: Not available
US federal AI use case: AI for Financial Analysis
Agency: Department of Energy - ORNL - Oak Ridge National Laboratory (SC43 OIM)
Problem addressed: Productivity enhancement for financial activities
Purpose and benefits: Enhance automation of  data processing for financial professionals.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: Doesn't meet criteria.
US federal AI use case: Microsoft PowerPoint MUI
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: AI/ML to design and optimize materials and their properties
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Design and optimize materials and their properties for Quantum Information Science and clean energy using AI/ML
Purpose and benefits: Design and optimize materials and their properties for Quantum Information Science and clean energy using AI/ML
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: EES&T Communications Impact
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Provides actionable insights into communication effectiveness, enabling data-driven improvements in outreach and stakeholder engagement.
Topic area: Other
Development stage: Pilot
Impact assessment: Used for general purpose uses cases within EES&T organization
US federal AI use case: NA-CI Salesforce
Agency: Department of Energy - NA-IM (Enterprise) - Office of the Associate Administrator for Information Management and Chief Information Officer (HQ-Enterprise) (NNSA HQ)
Problem addressed: Solves inefficiency and inconsistency of manual data capture of stakeholder activities.
Purpose and benefits: Improve efficiency, data quality, and visibility.
Topic area: Other
Development stage: Deployed
Impact assessment: This use case is determined to be not high-impact base on definitoin in Section 5 of the OMB Memorandum M-25-21.
First used: 2025-09-16
US federal AI use case: Microsoft Power BI Desktop
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Microsoft Azure PowerShell
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Road Conditions from IBM Watson for INL
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Not available
Topic area: Other
Development stage: Deployed
Impact assessment: Deemed not genAI per last year's submission. NA
US federal AI use case: Intelligent Acquisition and Reconstruction for Hyper-Spectral Tomography Systems
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: We will develop artificial intelligence (AI) and machine learning (ML) algorithms to enable dramatic improvements in the throughput and performance of hyperspectral (i.e., multiple energies) computed tomography (HSCT) beamlines at DOE BES Scientific
Purpose and benefits: We will develop artificial intelligence (AI) and machine learning (ML) algorithms to enable dramatic improvements in the throughput and performance of hyperspectral (i.e., multiple energies) computed tomography (HSCT) beamlines at DOE BES Scientific User Facilities (SUFs).  We will demonstrate the utility of our algorithms by carefully designing experiments for energy materials at HSCT beamlines available at the Spallation Neutron Source (SNS) and the National Synchrotron Light Source II (NSLS-II).  We will also develop AI driven data acquisition algorithms that will optimize the scanning strategy on-the-fly, in order to obtain the fewest yet most informative set of measurements (i.e. reducing beam time and/or number of projections in a data set).  Our team will provide ML based reconstruction algorithms that can produce high quality reconstructions from incomplete, sparse and low signal-to-noise ratio datasets enabling real-time feedback and ensuring best possible reconstruction on completion of the experiment.  Finally, our efforts will be available to the user community at both facilities via a general user interface.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Deep Learning Malware Analysis for reusable cyber defenses.
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Identify commonalities in malware
Topic area: Other
Development stage: Deployed
Impact assessment: Not available
US federal AI use case: To  drive insights on the power system reliability, cost, and operations during the energy transition with and without FECM technologies
Agency: Department of Energy - NETL - National Energy Technology Laboratory (FECM)
Problem addressed: To  drive insights on the power system reliability, cost, and operations during the energy transition with and without FECM technologies
Purpose and benefits: Generate predictive scenarios
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: Limited to a specific area of research
US federal AI use case: To  drive insights on the dependencies between the natural gas and electricity sectors to increase reliability of the NG system
Agency: Department of Energy - NETL - National Energy Technology Laboratory (FECM)
Problem addressed: To  drive insights on the dependencies between the natural gas and electricity sectors to increase reliability of the NG system
Purpose and benefits: Generate predictive scenarios
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: Limited to a specific area of research
US federal AI use case: Data platform to expedite access and reuse of carbon ore data for materials, manufacturing and research
Agency: Department of Energy - NETL - National Energy Technology Laboratory (FECM)
Problem addressed: Data platform to expedite access and reuse of carbon ore data for materials, manufacturing and research
Purpose and benefits: Expedite access and reuse of carbon ore data
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: Limited to a specific area of research
US federal AI use case: Integration of AI, specifically CoPilot for GitHub,  for BTFA Software Development Life Cycle Process for DME and O&M applications.
Agency: Department of the Interior - BTFA
Problem addressed: Reductions in BTFA Application and Database Staff has lead to increase workload for remaining staff for DME and O&M activities of applications and servers. This code is considered sophisticated in nature, code resides in Azure App Workspace (Web/Function/Logic Apps) and standalone applications and servers.
Purpose and benefits: Due to reduce staffing, BTFA needs to integrate AI for its DME and O&M of  applications and servers (database/application/web/etc) which has sophisticated code base.  AI has show promise in reducing staff hours for all phases of SDLC.
Topic area: IT
Development stage: Pilot
First used: 2025-10-01
US federal AI use case: Non-Generative AI use for Trust Information Analysis and Reporting Tool [2024 INV#WO0000000110516]
Agency: Department of the Interior - BTFA
Problem addressed: When records are scanned, both front and back pages are captured, even though back pages are generally blank. For Department of Justice litigation or other records research and discovery, this doubles the page count, making manual review unnecessarily time-consuming and resource-intensive. Automating blank page detection with Non-Generative AI (e.g., Azure Computer Vision and Document Intelligence Read) helps streamline this process and reduce burden.
Purpose and benefits: Staff no longer need to inspect and remove every blank page. Fewer pages to review means lower costs for records storage and contract/labor expenses. Enables BTFA to respond more quickly to DOJ litigation requests by streamlining document preparation.
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-08-25
US federal AI use case: Automate Project Execution of FI&R (Facilities Improvement and Repair) Projects
Agency: Department of the Interior - ASIA
Problem addressed: Strategically prioritize Facilities Improvement and Repair (FI&R) projects based on a set of criteria developed in concert with policy, customer need, and capacity.
Project funding based on compliance with policy and funding parameters. Process based on an objective and holistic review of IA-wide priorities.
Aggregate data across systems and apply analysis to develop a schedule, reporting, identify dependencies, and develop notifications.
Purpose and benefits: Strategic use of funds, allocation of multiple needs (projects), transparency of status, identification of dependency on external factors.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: BIA-GEO Assist
Agency: Department of the Interior - BIA
Problem addressed: The Branch of Geospatial Support (BOGS) and Regional Geospatial Coordinators (RGCs) offer technical support to BIA and Tribal GIS users that participate in the DOI Enterprise Licensing Agreement (ELA). The BOGS ELA Helpdesk Coordinator and RGCs allocates several hours per week to address helpdesk submissions that are rudimentary in nature and/or concerning licensing offered and the process and requirements for participation in the ELA.

RGCs allocate significant amounts of their time to the creation of Map layouts to provide data products to BIA, stakeholders and Tribes. We would like to expand the use of this tool

to see if it can be customized to reference data sets maintained by DRIS to automate the creation of these maps in a predefined template to eliminate the workload of map creation from existing data.

This tool also has the potential to assist with responding to FOIA requests, Our Division answers or collaborates on numerous FOIA requests each year. I would like to develop an internal version that could scour a FOIA repository to determine if this request is a duplication of a previous request and have it provide the dataset that was provided as a response.
Purpose and benefits: Reducing the number of basic questions concerning rudimentary processes and/or general licensing and eligibility question will reduce the number of hours spent on helpdesk tickets and allow the RGCs ELA helpdesk coordinator to focus more time on managing
Topic area: Service Delivery
Development stage: Pilot
First used: 2026-01-30
US federal AI use case: IA Tribal Consultation Clearinghouse
Agency: Department of the Interior - ASIA
Problem addressed: The Tribal Consultation Clearinghouse is envisioned by Indian Affairs (business owner) as a centralized, user-friendly webpage that enhances the accessibility of Tribal consultation information across the federal government.
Purpose and benefits: As a federal website, the Tribal Consultation Clearinghouse will be designed in alignment with the BIA’s existing approved website template, adhering to federal web design standards to ensure consistency, accessibility, and security.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Effects of vehicle traffic on space use and road crossings of caribou in the Arctic [2024 INV#WO0000000110111]
Agency: Department of the Interior - USGS
Problem addressed: Assessing the effects of industrial development on wildlife is a key objective of managers and conservation practitioners. However, wildlife responses are often only investigated with respect to the footprint of infrastructure, even though human activity can strongly mediate development impacts. In Arctic Alaska, there is substantial interest in expanding energy development, raising concerns about the potential effects on barren-ground caribou (Rangifer tarandus granti). While caribou generally avoid industrial infrastructure, little is known about the role of human activity in moderating their responses, and whether managing activity levels could minimize development effects. To address this uncertainty, we examined the influence of traffic volume on caribou summer space use and road crossings in the Central Arctic Herd within the Kuparuk and Milne Point oil fields on the North Slope of Alaska.
Purpose and benefits: gradient-boosted machine learning models to predict hourly traffic volumes for road segments across the study and generalized additive models (GAMs) to assess effects of traffic volume on caribou fine-scale summer movements
Topic area: Science
Development stage: Deployed
First used: 2025-10-03
US federal AI use case: DiscoverDOI
Agency: Department of the Interior - USGS
Problem addressed: Users need to find outdated content or prohibited content on DOI websites
Purpose and benefits: Users can ask questions and receive answers and links to the websites in question
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-06-02
US federal AI use case: Anomaly Detection for Financial Transactions Related to DOI Programs
Agency: Department of the Interior - OIG
Problem addressed: Use traditional machine analytics with difficult-to-manually-analyze financial data for DOI programs to create analytical methods that are scalable and repeatable. Additionally, the analytics must be aligned with quality standards required for OIG audits and investigations.
Purpose and benefits: It allows OIG to identify anomalies or trends that would be undetectable via manual analysis or would require endless IT and staff resources to manually process and analyze information.
Topic area: Other
Development stage: Pilot
First used: 2025-04-01
US federal AI use case: ePermits: Cognitive Search Capability
Agency: Department of the Interior - FWS
Problem addressed: Current native search capabilities don't offer semantic search features that understand user intent and content meaning.
Purpose and benefits: Enhance functionality, improve user experience, assist in acceleration of ePermits processing and align with modern technological and security standards to support mission of conserving, protecting, and enhancing fish, wildlife, plants, and their habitats
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: ePermits: Chatbot Implementation and Permit Application Wizard Tools
Agency: Department of the Interior - FWS
Problem addressed: Users face significant difficulties when interacting with the system due to a lack of personalized experiences, delayed and imprecise query responses, poor application form guidance, and an overall unintuitive user interface—resulting in reduced efficiency, increased errors, and lower user satisfaction.
Purpose and benefits: Enhance functionality, improve user experience, assist in acceleration of ePermits processing and align with modern technological and security standards to support mission of conserving, protecting, and enhancing fish, wildlife, plants, and their habitats
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Comprehensive Condition Assessment
Agency: Department of the Interior - USGS
Problem addressed: Find deficiencies in our real property assets and to formulate reports from field and machine gathered data for the purpose of making a deferred maintenance backlog and for capitalized planning on real property assets in the USGS
Purpose and benefits: Saving staff time, travel costs, and will speed report creation to address maintenance needs in a more timely manner
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: AI data harvesting project – ECOSphere [2024 INV#DOI-62]
Agency: Department of the Interior - FWS
Problem addressed: How can we automate the extraction, classification, and transformation of heterogeneous species-related files (e.g., PDFs, Word docs, Excel sheets, images) uploaded by biologists into structured formats compatible with the ETK system, while ensuring accuracy, scalability, and minimal manual intervention?
Purpose and benefits: The data ingestion process will be automated, enabling the system to extract, classify, and transform data from various file types without human intervention. Species data will become available more quickly, allowing for faster decision-making.
Topic area: Science
Development stage: Pilot
First used: 2024-02-14
Agency: Department of the Interior - FWS
Purpose and benefits: Supports research and analysis using publicly available information to assist agency mission needs.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2024-03-01
US federal AI use case: Performance Modeling / Performance Forecasting
Agency: Department of the Interior - SOL
Problem addressed: Estimating the number or requests that can be processed by a FOIA processor or bureau each year.
Purpose and benefits: Expected performance. Identifying important variables. Assists in resource allocation decisions.
Topic area: Admin Functions
Development stage: Deployed
First used: 2022-11-30
US federal AI use case: FOIA Request Conceptual Similarity Tool (Semantic Similarity Score Generation)
Agency: Department of the Interior - SOL
Problem addressed: Identifying contextually similar FOIA requests received by the department.
Purpose and benefits: Reduces duplication of work when similar requests are submitted to multiple offices. These requests can be coordinated for uniform responses.
Topic area: Other
Development stage: Deployed
First used: 2023-11-03
US federal AI use case: FOIA Request Clustering Tool (Embedding based clustering alongside Term Frequency – Inverse Document Frequency (TF-IDF))
Agency: Department of the Interior - SOL
Problem addressed: Identifying FOIA requests that share lexical and/or semantic similarity.
Purpose and benefits: Quickly identifying similar FOIA requests.
Topic area: Other
Development stage: Deployed
First used: 2023-11-03
US federal AI use case: Enabling FOIA Request Clustering Capability in the Document Review Platform (Density Based Algorithm alongside Term Frequency – Inverse Document Frequency (TF-IDF))
Agency: Department of the Interior - SOL
Problem addressed: Identifying Contextually Similar FOIA Requests. Developed in house is selected because while the document review platform is used it is not an out of the box functionality.
Purpose and benefits: This tool assists in identifying contextually similar FOIA requests but has some limitations such as editing stop words. While we still use this tool sometimes, we primarily use our in-house built tool.
Topic area: Other
Development stage: Deployed
First used: 2023-08-01
US federal AI use case: FOIA Request Lexical Similarity Tool in the Document Review Platform (Term Frequency – Inverse Document Frequency (TF-IDF) - Cosine)
Agency: Department of the Interior - SOL
Problem addressed: Identifying Similar FOIA Requests
Purpose and benefits: Identifying requests that are nearly identical assists in reducing the duplication of work. Records that are identical or nearly identical can be deduplicated out or processed in bulk.
Topic area: Other
Development stage: Deployed
First used: 2023-08-01
US federal AI use case: Microsoft eDiscovery Attorney-Client Privilege Detection
Agency: Department of the Interior - SOL
Problem addressed: Identifying information subject to a FOIA exemption.
Purpose and benefits: Improving speed and accuracy in identifying information that may fall under a FOIA exemption. This tool has recently been activated and we are gathering data to see if it is useful in FOIA operations
Topic area: Other
Development stage: Deployed
First used: 2025-09-01
US federal AI use case: Document Review Platform Generative AI
Agency: Department of the Interior - SOL
Problem addressed: FOIA Request Processing is the resource intensive step of the process that would benefit from AI assistance and optimization.
Purpose and benefits: Potential benefits include increasing the speed and accuracy of document review.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: FOIA Record Pre-Review Tool
Agency: Department of the Interior - SOL
Problem addressed: Records review is a the resource intensive step of FOIA request processing that would most benefit from AI assistance and optimization.
Purpose and benefits: Improve the speed and accuracy of records review.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Atlas.ti (Qualitative Data Analysis Software)
Agency: Department of the Interior - FWS
Problem addressed: Existing software, such as Excel, was insufficient for the increasing quantity of qualitative data collected, necessitating the need for specialized software geared towards qualitative data analyses.
Purpose and benefits: The software increases the efficiency of qualitative data analyses and could automate labor-intensive tasks. A qualified social scientist is involved in the final analysis and insights to ensure accuracy and compliance with regulatory standards.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Implementation of Artificial Intelligence to Estimate Population Size of a Migratory Bird from Aerial Thermal Imagery [2024 INV#DOI-67]
Agency: Department of the Interior - FWS
Problem addressed: Current survey approach has excessive variability that cannot be biologically explained. New approach is warranted to inform harvest management
Purpose and benefits: Improved safety for pilots and crew because of remote sensing methods, potential for cost efficiency if approach is fully implemented, and improves data quality to inform harvest management.
Topic area: Service Delivery
Development stage: Pilot
First used: 2025-03-24
US federal AI use case: AI-Powered Wildlife Monitoring on National Wildlife Refuges
Agency: Department of the Interior - FWS
Problem addressed: For whooping crane, traditional survey approach is to estimate population size by conducting 6 repeated surveys due to limits of human observer surveys.  Surveys of waterfowl are experimental and looking to improve the accuracy of surveys and to obtain improved measures of population sizes.
Purpose and benefits: Cost efficiency and improved data quality.
Topic area: Service Delivery
Development stage: Pilot
First used: 2025-01-22
US federal AI use case: Water Use Model Development [2024 INV#WO0000000109669]
Agency: Department of the Interior - USGS
Problem addressed: Estimate multiple categories of water use across the U.S.
Purpose and benefits: Outcome is quantified water demand across the U.S. to meet agency mission of assessing water availability
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Nutrient, Salinity, sediment, temperature, and drought model development
Agency: Department of the Interior - USGS
Problem addressed: Simulate nutrients (phosphorus and nitrate), temperature, sediment, and salinity in streams across the U.S.
Purpose and benefits: Includes prototype streamflow drought forecasts using data-driven, machine learning approaches for USGS gage locations across  continental U.S. Provides model results on water quality aspects to meet agency mission of assessing water availability
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: CONUS EcoFlows Planning & Prototype [2024 INV#WO0000000109732]
Agency: Department of the Interior - USGS
Problem addressed: National-scale ecological-flow response models
Purpose and benefits: Predictions of flow conditions of sites with anthropogenic influences where biological data exists to meet agency mission of assessing water availability
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI for Offshore Energy: Innovative Technology Streamlines Marine Wildlife Surveys [2024 INV#DOI-66]
Agency: Department of the Interior - FWS
Problem addressed: Offshore energy development requires environmental assessments; using AI technology to conduct offshore migratory bird counts can efficiently provide such data, however, substantial automation is required
Purpose and benefits: Counts of migratory birds with a high degree of accuracy, long-term cost efficiency, safety is improved for pilots due to remote sensing technology
Topic area: Energy and Environment
Development stage: Pilot
First used: 2021-08-15
US federal AI use case: Using advanced computing techniques for image-based monitoring [2024 INV#WO0000000109674]
Agency: Department of the Interior - USGS
Problem addressed: Provide tools and methods for leveraging image-based monitoring and machine learning approaches for measuring surface water properties.
Purpose and benefits: USGS Water Mission Area improvements in operational efficiencies and reduction in safety incidents with application of image-based monitoring techniques and methods. Enhance data portfolio and provide new data types to stakeholders at reduced cost
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Using advanced computing techniques for mobile monitoring platforms [2024 INV#WO0000000109492]
Agency: Department of the Interior - USGS
Problem addressed: Support the navigation and swarming capabilities of autonomous vehicle platforms for water monitoring
Purpose and benefits: Improved spatial data collection on water quality and quantity
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Water Time-Series Record Automation Framework development
Agency: Department of the Interior - USGS
Problem addressed: Leverage AI/ML to streamline USGS time-series data processing workflows
Purpose and benefits: The AI/ML algorithms would be leveraged to help streamline data correction processes associated with time-series data record production in extensible software automation solutions.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Mineral Resource Assessments
Agency: Department of the Interior - USGS
Problem addressed: Mineral resource assessments require a significant amount of data evaluation and processing.
Purpose and benefits: AI-assisted methods are used to modernize assessment methodologies and capacity, allowing the USGS to meet the increasing demand for critical mineral assessments more efficiently.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Lithium Potential Assessments
Agency: Department of the Interior - USGS
Problem addressed: Identifying sources of lithium is critical to our Nations economic prosperity and security.
Purpose and benefits: The USGS has used machine learning to find new domestic sources of critical minerals including lithium.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Geothermal Energy Assessments
Agency: Department of the Interior - USGS
Problem addressed: Executive Order 14156 – Declaring a National Energy Emergency, directs federal agencies to identify and develop our Nations energy resources.
Purpose and benefits: New methodology developed by the USGS leverages AI and advanced machine learning (ML) techniques to accelerate how we assess geothermal energy potential and brings new speed and precision to our science to unleash American energy.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: DocketScope
Agency: Department of the Interior - FWS
Problem addressed: Facilitate review of public comments.
Purpose and benefits: Use AI capabilities and detections to assist with timely and accurate comment analysis.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-08-04
US federal AI use case: Improved Processing and Analysis of Test and Operating Data from Rotating Machines [2024 INV#DOI-62]
Agency: Department of the Interior - BOR
Problem addressed: This research strives to aid in the development of condition-based maintenance (CBM) and predictive maintenance (PdM) tools for Hydroelectric Facilities (Generators and Pumps) by exploring, testing, and developing software tools to process data collected from rotating machines. These software tools use various forms of AI/ML.
Purpose and benefits: CBM and PdM aim to increase hydropower generation by reducing outages for maintenance and lower O&M costs by only performing maintenance when needed.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Machine Learning for Chemical Savings at Reverse Osmosis Plants [2024 INV#DOI-73 (NEW)]
Agency: Department of the Interior - BOR
Problem addressed: Water treatment processes are often complex and maintenance activities present tradeoffs between cost and system performance.
Purpose and benefits: This project aims to use machine learning to optimize the usage of membrane cleaning chemicals in a water treatment plant using a reverse osmosis process. This would lower the unit cost of water produced.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Machine Learning Applied to Geotechnical Engineering: Statistical Methods Applied to Seismic Analysis [2024 INV#DOI-71 (NEW)]
Agency: Department of the Interior - BOR
Problem addressed: The process for determining the seismic risk for dams is time intensive.
Purpose and benefits: Leveraging machine learning, high-level facility seismic risk screening can be conducted more efficiently.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Machine Learning Refines Quagga Habitat Suitability [2024 INV#DOI-74 (NEW)]
Agency: Department of the Interior - BOR
Problem addressed: Invasive mussels pose a number of challenges for the continuous operation of water infrastructure.
Purpose and benefits: Understanding habitat suitability can inform management actions.
Topic area: Energy and Environment
Development stage: Pre-deployment