GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: In-storage computing for multi-messenger astronomy in neutrino experiments and cosmological surveys
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This project aims to address the big-data challenges and stringent time constraints facing multi-messenger astronomy (MMA) in neutrino experiments and cosomological surveys. Instead of following the traditional computing paradigm of moving data to th
Purpose and benefits: The purpose is to enhance the ability of large scale neutrino experiments like DUNE to detect neutrinos from core-collapse supernovas (CCSNs) and to extract useful information about their source in real time to provide prompt multi-messenger alerts to other observatories.  Aside from enabling prompt SN pointing that is also precise, this will cut down the rate of fake SN triggers (curretnly estimated at ~1/month) and therefore offer potential savings from a reduction in the hardware resources required for storing the large amounts of data associated with CCSN candidates.
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: HR Job Postings
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Generic or ineffective job postings
Purpose and benefits: Enhances applicant diversity and job match quality by optimizing language and structure in postings, improving recruitment outcomes.
Topic area: HR
Development stage: Pre-deployment
Risk category: High-impact
Impact assessment: Not available
First used: 2025-12-01
US federal AI use case: Visual Studio Community
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: Invoice Scanning System
Agency: Department of Energy - RL - Hanford - Richland Operations Office - Hanford (EM)
Problem addressed: Missing information in subcontract submittals
Purpose and benefits: greater accuracy of information on required forms
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Impact assessment: This AI system does not provide a principle basis for decisions or actions with legal, material binding, or have any significat effects of those items listed in OMB-25-21 in the definition of High Impact.
First used: 2024-09-04
US federal AI use case: Facilities Visual Inspection
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Detect hazards and assess facility conditions through AI-enhanced visual inspections.
Purpose and benefits: Improves workplace safety and hazard detection in facilities.
Topic area: Science
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: Microsoft OneDrive
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: QuantomVision
Agency: Department of Energy - ORNL - Oak Ridge National Laboratory (SC43 OIM)
Problem addressed: Workforce upskilling
Purpose and benefits: Predict workforce evolution  and analiyze skills mapping.
Topic area: IT
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: ServiceNow Cluster Analysis
Agency: Department of Energy - LANL - Los Alamos National Lab (LAFO)
Problem addressed: Identify patterns of tickets created to determine workflow and automated solutions
Purpose and benefits: Improved automation
Topic area: IT
Development stage: Deployed
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: ChatSRS
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: General text-oriented chat (e.g., summarization, generation)
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: high level synthesis for machine learning (previously hls4ml)
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This project develops hardware-software AI codesign tools for FPGAs and ASICs for algorithms running at the extreme edge.
Purpose and benefits: hls4ml is used to implement specialized AI algorithms in embedded hardware.  This is valuable across a wide range of scientific applications for enabling real-time processing capabilitles.  This can accelerate sientific discovery and time to science thus enabling large cost savings and DOE scientific prestige.
Topic area: Science
Development stage: Deployed
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: Knowledge Capture Agent (KCA)
Agency: Department of Energy - ORNL - Oak Ridge National Laboratory (SC43 OIM)
Problem addressed: Capturing tenured employees experience based knowledge.
Purpose and benefits: To pass down this experience and make it into a database
Topic area: IT
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: AI Support Agent Chatbot
Agency: Department of Energy - EE HQ - EE Headquarters (EE)
Problem addressed: Manual helpdesk tickets consume staff time and delay issue resolution.
Purpose and benefits: Reduce Tier-1 support time, improve satisfaction with 24/7 responses, free staff for complex issues.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Provides advisory information only; does not make binding decisions.
US federal AI use case: UNSPSC Codes
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: Map requisition items to UNSPSC codes
Purpose and benefits: Maxmize use of Strategic Agreements and facilitate SCM reporting
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2024-10-01
US federal AI use case: ServiceNow Similarity Analysis
Agency: Department of Energy - LANL - Los Alamos National Lab (LAFO)
Problem addressed: Identify patterns of tickets created to determine workflow and automated solutions
Purpose and benefits: Improved automation
Topic area: IT
Development stage: Deployed
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: Accelerating HEP Science: Inference and Machine Learning at Extreme Scales
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Developing Galaxy Image deblending with the gravitational lensing effects and scaling AI/ML algorithms
Purpose and benefits: Developing Galaxy Image deblending with the gravitational lensing effects and scaling AI/ML algorithms
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Georeference Figures
Agency: Department of Energy - LM HQ - Office of Legacy Management (LM)
Problem addressed: AI-powered georeferencing will improve automation, speed, and efficiency. Manual georeferencing is expensive.
Purpose and benefits: Georeferenced of historical paper maps.
Topic area: Energy and Environment
Development stage: Deployed
Impact assessment: This AI use case does not serve as a principal basis for decisions or actions with legal, material, and binding
First used: 2025-03-01
US federal AI use case: Microsoft Discovery
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Speed up transparent, governed R&D discovery with AI agents and graph knowledge engines.
Purpose and benefits: Accelerates innovation with transparent, AI-driven R&D processes.
Topic area: Science
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: CyberSearch
Agency: Department of Energy - Pantex - PanTeXas Deterrence Pantex (PFO)
Problem addressed: The AI is intended to improve the efficiency of plant personnel by giving them an easy way to access and search cybersecurity documents.
Purpose and benefits: The expected benefit is increased effiency for plant personnel.
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: The AI output is not a basis for decisions or actions. It is for information retrieval.
US federal AI use case: O365 Copilot
Agency: Department of Energy - SLAC - SLAC National Accelerator Laboratory (SC43 OIM)
Problem addressed: Improve day-to-day business functionality
Purpose and benefits: Automation of redundant business functions to increase efficiency
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21
US federal AI use case: Streamining intelligent detectors for sPHENIX/EIC
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: his project develops real-time algorithms for event filtering with tracking detectors for nuclear physics collider experiments.
Purpose and benefits: AI tools are developed for embedded inference in real-time processing systems for scientific experiments  such as sPHENIX and upcoming EIC. 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: Critical Materials
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: AI will make discoveries of unknown combinations of rare earth elements and ligands used in critical materials that are currently impossible to separate
Purpose and benefits: Make discoveries of combinations of rare earth elements and ligands used in critical materials efficiently, faster, less costly
Topic area: Science
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Microsoft 365
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: Advanced Fuels Campaign
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Accelerates fuel development cycles and improves performance predictions, reducing R&D costs and time to deployment.
Topic area: Other
Development stage: Pilot
Risk category: High-impact
US federal AI use case: Hanford Ai Liaison (HAL) 2.0
Agency: Department of Energy - RL - Hanford - Richland Operations Office - Hanford (EM)
Problem addressed: connecting business data streams to AI for increased analysis and work efficiency
Purpose and benefits: cost savings, increased efficiency, increased productivity, greater analytics of data
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: This AI system does not provide a principle basis for decisions or actions with legal, material binding, or have any significat effects of those items listed in OMB-25-21 in the definition of High Impact.
First used: 2025-09-01
US federal AI use case: ATLAS
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Make processes in Technology Deployment more effective and efficient
Purpose and benefits: Enables faster data discovery and analysis across large datasets, improving research productivity and insight generation.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Used for technology deployment function.
US federal AI use case: Continuous Structure Descriptors for XANES Interpretation
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Seeking a continuous local structure motif that correlates to X-ray spectral signatures
Purpose and benefits: Seeking a continuous local structure motif that correlates to X-ray spectral signatures
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Azure AI Search
Agency: Department of Energy - NR HQ - Naval Reactors (NR)
Problem addressed: Enhance the capability to find structured and unstructured data within databases and datalakes.
Purpose and benefits: Improve efficiency of finding a data source. Azure AI Search also creates an internal knowledge base for use in a LLM.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Azure AI Search indexes data.  The end user is responsible for the verification of the data and the final use. This does not meet the criteria outlined in section 5 of OMB Memorandum M-25-21.
US federal AI use case: ServiceNow Now Assist
Agency: Department of Energy - LANL - Los Alamos National Lab (LAFO)
Problem addressed: Provide Agentic AI capabilities for use in multiple use cases for the IT divisions at LANL
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: pComply-AI-High Risk
Agency: Department of Energy - ANL - Argonne National Laboratory (SC43 OIM)
Problem addressed: Preemptive procurement compliance: daily scans of recent PARIS requisition records.
Purpose and benefits: Developed as a rapid response for a corrective action to eliminate incidents when high-risk materials are procured inadvertently without the required review processes and handling.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Impact assessment: Impact of system failure or erroneous results is negligible.
First used: 2024-01-01
US federal AI use case: Groundwater Modeling
Agency: Department of Energy - LM HQ - Office of Legacy Management (LM)
Problem addressed: Solves critical challenges in water modeling by providing forward-looking monitoring.
Purpose and benefits: Forward cast ground water behavior
Topic area: Energy and Environment
Development stage: Deployed
Impact assessment: This AI use case does not serve as a principal basis for decisions or actions with legal, material, and binding
First used: 2003-01-02
US federal AI use case: In-pixel AI for future tracking detectors
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This project explores novel  AI-on-chip technology for intelligent detectors embedded with sensing technology
Purpose and benefits: AI algorithms are implemented in on-detector electronics in order to reduce data size and enable processing at high rates.
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: Instrument Documentation Search
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Provide quick answers about instrument operations by searching ingested manuals.
Purpose and benefits: Speeds troubleshooting and learning of lab instruments.
Topic area: Science
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: MOOSE-LLM
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Tools like MOOSE for multiphysics modeling have a steep learning curve. They demand considerable domain-specific knowledge, which makes it hard for newcomers to get started.
Purpose and benefits: Improves user experience and reduces training time by enabling natural language assistance within the MOOSE simulation framework.
Topic area: Energy and Environment
Development stage: Pilot
Impact assessment: Focusing on the application of generative AI on modeling and simulation tasks
First used: 2025-08-26
US federal AI use case: DevSec Ops AI
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: accelerates secure software delivery
Topic area: Other
Development stage: Deployed
Impact assessment: Used for general purpose IT M&O.
US federal AI use case: Machine Learning for Autonomous Control of Scientific User Facilities
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: BNL will work alongside SLAC, to implement ML algorithm(s) into NSLS-II Operations to interpret accelerator data more intelligently.  We intend to train said algorithms with 5+ years of archived device-data from accelerator components, records of pre
Purpose and benefits: BNL will work alongside SLAC, to implement ML algorithm(s) into NSLS-II Operations to interpret accelerator data more intelligently.  We intend to train said algorithms with 5+ years of archived device-data from accelerator components, records of previous fault causes (to connect to data-symptoms) and stored beam current.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Preventive Maintenance Procedure Development
Agency: Department of Energy - Pantex - PanTeXas Deterrence Pantex (PFO)
Problem addressed: The AI is intended to solve the inefficiency and potential for error in manually developing and updating preventive maintenance procedures. It addresses the challenge of synthesizing information from multiple, diverse data sources to ensure complianc
Purpose and benefits: The expected benefits include, increased productivity, reduced manual research time, minimized errors, improved compliance, enhanced equipment uptime, and cost savings.
Topic area: Admin Functions
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: OpenText for Records Management (File share auto-classification)
Agency: Department of Energy - EE HQ - EE Headquarters (EE)
Problem addressed: Records retention and disposition.
Purpose and benefits: Reduce time required to classify legacy records accumulated over 20 years.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not fall within the requirements for high impact.
First used: 2022-10-24
US federal AI use case: Towards Edge Computing: A Software and Hardware Co-Design Methodology for Application-Specific Integrated
Circuit (ASIC)-based Scientific Neuromorphic Computing (NC)
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: One project: Current deep neural network (DNN)-based Artificial Intelligence (AI) algorithms have already been successfully applied to particle physics applications. This project will develop a co-design approach for methodologies and their implement
Purpose and benefits: One project: Current deep neural network (DNN)-based Artificial Intelligence (AI) algorithms have already been successfully applied to particle physics applications. This project will develop a co-design approach for methodologies and their implementation of edge computing for optimal handling of data streams
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Microsoft Visual C++ Minimum Runtime
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 for Applications in High Energy adn Nuclear Physics
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Develop state-of-the-art cycle-consistent GANs to bridge the gap between simulations and experimental data; develop real-time particle tracking with deep learning on field programmable gate arrays; explore the challenges of deploying ML modeling onto
Purpose and benefits: Develop state-of-the-art cycle-consistent GANs to bridge the gap between simulations and experimental data; develop real-time particle tracking with deep learning on field programmable gate arrays; explore the challenges of deploying ML modeling onto real-time inference hardware - for High Energy or Nuclear Physics
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Microsoft OneDrive 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: SONIC: AI acceleration as a service
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This project focuses on integration of AI hardware for at-scale inference acceleration for particle physics experiments.
Purpose and benefits: SONIC is used to accelerate AI workloads on coprocessors in 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: 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: RAPIDS3: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: A SciDAC computer science institute and BNL is co-leading AI team
Purpose and benefits: A SciDAC computer science institute and BNL is co-leading AI team
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: LISA Chatbot Pilot
Agency: Department of Energy - LLNL - Lawrence Livermore National Laboratory (LFO)
Problem addressed: Quick access to specific mission data sets and documentation
Purpose and benefits: Ability to quickly find and access relevant mission data and experiment documentation
Topic area: IT
Development stage: Pilot
Impact assessment: Not being used to make critical runtime decisions.
First used: 2025-06-30
US federal AI use case: Use AI/ML to Enhance the Bioimaging Capabilities at Brookhaven National Laboratory (BNL)
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Three projects: use expertise in ML as one element in building an integrated multiscale bioimaging capability at BNL; use AI/ML tools use to accelerate the analysis of protein structure and function; develop a high-resolution AI-led analysis of nondi
Purpose and benefits: Three projects: use expertise in ML as one element in building an integrated multiscale bioimaging capability at BNL; use AI/ML tools use to accelerate the analysis of protein structure and function; develop a high-resolution AI-led analysis of nondisrputive, time-resolved light microscopy images measured at lower spatial resolutions
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Climate Weather Data
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: speeds up climate model analysis
Topic area: Other
Development stage: Deployed
Impact assessment: Targeted for weather and climate related use cases
US federal AI use case: Scripting
Agency: Department of Energy - LM HQ - Office of Legacy Management (LM)
Problem addressed: AI-powered scripting will improve automation, speed, and efficiency.
Purpose and benefits: Reduced cost in alteration of ground water models improving ground water outcomes.
Topic area: Energy and Environment
Development stage: Deployed
Impact assessment: This AI use case does not serve as a principal basis for decisions or actions with legal, material, and binding
First used: 2025-01-30
US federal AI use case: Microsoft Copilot Pilot
Agency: Department of Energy - LLNL - Lawrence Livermore National Laboratory (LFO)
Problem addressed: Quick access to self-generated (Documents, Emails, Files) knowledge and business productivity.
Purpose and benefits: Ability to quickly find and access general knowledge and business productivity
Topic area: IT
Development stage: Pilot
Impact assessment: General chatbot knowledge and business productivity capabilities
First used: 2025-06-30
US federal AI use case: EES&T Document Processing
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Cuts manual processing time and improves data accessibility by automating document classification and extraction, increasing operational efficiency.
Topic area: Other
Development stage: Pilot
Impact assessment: Used for general purpose use cases within EES&T organization
US federal AI use case: Smart CO2 Transport-Route Planning Tool
Agency: Department of Energy - NETL - National Energy Technology Laboratory (FECM)
Problem addressed: Identify potential routes or evaluate existing corridors for carbon transport based on current legislation, best construction practices, and more.
Purpose and benefits: Inform planning and development
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: Limited to a specific area of research
US federal AI use case: PermitAI
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Streamline permitting processes with AI-powered environmental review tools.
Purpose and benefits: Speeds up Federal permitting while improving transparency.
Topic area: Energy and Environment
Development stage: Pilot
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2024-10-01
US federal AI use case: High-Velocity AI: Generative Models
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Development stage: Retired
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: AI for Vendor Compliance
Agency: Department of Energy - ORNL - Oak Ridge National Laboratory (SC43 OIM)
Problem addressed: Making sure vendors are in compliance with regulations.
Purpose and benefits: More streamlined risk mitigation process with vendors.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: Doesn't meet criteria.
US federal AI use case: ServiceNow LANL AI Portal Integration
Agency: Department of Energy - LANL - Los Alamos National Lab (LAFO)
Problem addressed: Provide GenAI capabilities for use in multiple use cases for the IT divisions at LANL
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: Microsoft AI Builder
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Microsoft AI Builder solves the problem that most businesses want to use AI but lack the technical expertise and resources to build it themselves. It provides easy-to-use, pre-built AI tools that business users can implement without needing data scie
Purpose and benefits: Microsoft AI Builder democratizes AI development by enabling business users to easily create custom AI models and automation solutions without extensive coding. It integrates with the Microsoft Power Platform to add AI capabilities like document processing and predictions directly into workflows, accelerating digital transformation through accessible, low-code AI solutions that enhance business processes and decision-making.
Topic area: IT
Development stage: Pilot
Impact assessment: Used for general purpose generative AI within a COTS product.
First used: 2025-06-20
US federal AI use case: LexisNexis
Agency: Department of Energy - RL - Hanford - Richland Operations Office - Hanford (EM)
Problem addressed: searching legal database for case law
Purpose and benefits: Better case law material for legal team
Topic area: Other
Development stage: Pre-deployment
Impact assessment: This AI system does not provide a principle basis for decisions or actions with legal, material binding, or have any significat effects of those items listed in OMB-25-21 in the definition of High Impact.
First used: 2025-08-01
US federal AI use case: Microsoft 365 Apps for Enterprise
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: Automated sorting of high repetition rate coherent diffraction data from XFELS
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: 'Coherent X-rays are routinely provided today by the latest Synchrotron and X-ray Free-electron Laser Sources. When these diffract from a crystal containing defects, interference leads to the formation of a modulated diffraction pattern called ''spec
Purpose and benefits: 'Coherent X-rays are routinely provided today by the latest Synchrotron and X-ray Free-electron Laser Sources. When these diffract from a crystal containing defects, interference leads to the formation of a modulated diffraction pattern called ''speckle''. When the defects move around, they can be quantified by a correlation analysis technique called X-ray Photon Correlation Spectroscopy. But the speckles also change when the beam moves on the sample. By scanning the beam in a controlled way, the overlap between the adjacent regions gives redundancy to the data, which allows a solution of the inherent phase problem. This is the basis of the coherent X-ray ptychography method which can achieve image resolutions of 10nm, but only if the probe positions are known.
The goal of this proposal will be to separate ''genuine'' fluctuations of a material sample from the inherent beam fluctuations at the high data rates of XFELs. Algorithms will be developed to calculate the correlations between all the coherent diffraction patterns arriving in a time series, then used to separate the two sources of fluctuation using the criterion that the ''natural'' thermal fluctuations do not repeat, while beam ones do.  We separate the data stream into image and beam ''modes'' automatically.'
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Software Implementation Assistant
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: This AI solution is designed to streamline the process of software implementation by automating crucial planning tasks, technical documentation, and risk assessments, which typically demand significant time and effort. The solution aims to support ju
Purpose and benefits: The AI solution is expected to enhance the agency's mission by reducing the time, effort, and complexity involved in software implementation efforts, leading to significant cost savings. It will improve project planning, standardize technical documentation, aid junior resources, and allow subject matter experts to focus on critical tasks, ultimately improving project outcomes and resource utilization for the public's benefit.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: This AI use case primarily assists in planning and document-related tasks for software implementations and does not significantly affect legal, material, or binding rights or critical access to services.
First used: 2024-10-01
US federal AI use case: Uncertainty Quantification and Instrument Automation to enable next generation cosmological discoveries
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This project will develop AI-based tools to enable critical sectors for near-future cosmic applications. Uncertainty quantification is essential for performing discovery science now, and simulation-based inference offers a new approach. The automated
Purpose and benefits: create new methods for uncertainty quantification in AI
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: Copilot Studio
Agency: Department of Energy - NR HQ - Naval Reactors (NR)
Problem addressed: Copilot Studio will help NNL build custom AI agents that automate repetitive tasks, answer common questions, and streamline workflows across departments, all without requiring deep coding expertise.
Purpose and benefits: Copilot Studio delivers significant benefits by enabling NNL to build custom AI agents that automate routine tasks, enhance decision-making, and improve operational efficiency, all through a low-code interface.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Output of generative AI is administratively prohibited from being used as principal responses in the various scenarios considered high impact.
US federal AI use case: Microsoft Outlook 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: HALO AI
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Increases operational awareness and response speed by automating data analysis, leading to faster, more informed decision-making.
Topic area: Other
Development stage: Pre-deployment
Impact assessment: Demonstration of AI agents for dense data interfaces at a specific installation. The impact is limited to a small number of operational functions but could expand if succsessul.
US federal AI use case: COREII
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Support decision making for critical infrastructure OT cybersecurity operations. Provides a secure environment where senstive data can be ingested and retreived reliably. Reduces the time it takes to perform threat analysis, supply chain analysis, kn
Purpose and benefits: Making information and knowledge easier to retreive and digest. Signficantly improves analysis time. Maximizes the transfer of knowledge to users of various backgrounds. Translates complex and complicated research results into accessible practical solutions.
Topic area: Cybersecurity
Development stage: Pilot
Impact assessment: This AI system leverages Retreival Augmented Generation (RAG) enabled Generative AI over large corpuses of OT/ICS cybersecurity data and information to support decision making within critical infrastructure operations.
First used: 2025-04-01
US federal AI use case: GenAI for Classified Subject Area Categorization
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Help users, DCs and classification analysts more quickly determine Classified Subject Areas.
Purpose and benefits: Help users, DCs and classification analysts more quickly determine Classified Subject Areas.
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: Pilot to help derivative classifiers, classification analysts, researchers and others communicating conent externally to quickly determine CSA.
First used: 2025-09-19
US federal AI use case: Sindri
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: Automate code assignment at requisition generation time
Purpose and benefits: Maxmize use of Strategic Agreements and facilitate SCM reporting
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
US federal AI use case: AI Safety Tool
Agency: Department of Energy - ORNL - Oak Ridge National Laboratory (SC43 OIM)
Problem addressed: Mitigate workplace ncidents
Purpose and benefits: To reduce workplace incidents and increase employee safety.
Topic area: Energy and Environment
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Microsoft Co-Pilot
Agency: Department of Energy - KCNSC - Kansas City National Security Campus (KCFO)
Problem addressed: Overloaded workers often must perform routine, tedious administrative tasks that waste their valuable time
Purpose and benefits: Eliminate tedious, time consuming adminstrative tasks, reduce time to accomplish routine tasks
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: Output does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety. Just productivity enhancement.
First used: 2027-10-01
US federal AI use case: Microsoft Edge
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: Anthropic Claude
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Generative AI tools like this solve the problem of time-consuming knowledge work by instantly providing expert-level assistance with writing, coding, research, and analysis. They automate repetitive tasks that require human expertise, allowing people
Purpose and benefits: Boost employee productivity by helping to create content, assist in coding, and complex problem-solving tasks, while democratizing access to sophisticated capabilities like writing and design. This technology promises to accelerate innovation by serving as an intelligent collaborator, freeing humans to focus on higher-level strategic and creative work
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: Used for general purpose generative AI within a COTS product.
First used: 2025-07-03
US federal AI use case: READS: Real-time Edge AI for Distributed Systems
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This project will develop and deploy low-latency controls and prediction algorithms at the Fermilab accelerator complex
Purpose and benefits: READS has two sub-projects. The first project created the means to stream live Main Injector and Recycler accelerator beam loss monitor data. This data is then fed to an AI model deployed on an FPGA so that it can infer, in realtime, the origin of beam loss, either Main Injector or Recycler, for each beam loss monitor in the tunnel enclosure. The second project aimed to improve upon traditional resonant beam extraction regulation techiniques using AI for use in the Fermilab Delivery Ring and Mu2e.
Topic area: Science
Development stage: Deployed
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: Amazon Q Chatbot Pilot
Agency: Department of Energy - LLNL - Lawrence Livermore National Laboratory (LFO)
Problem addressed: Quick access to AWS cloud engineering reference architectures and general AWS services information
Purpose and benefits: Ability to quickly find and access AWS architecture and services information
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Not being used to make critical runtime decisions.
First used: 2025-06-30
US federal AI use case: Agentic AI for cybersecurity change request reviews
Agency: Department of Energy - NR HQ - Naval Reactors (NR)
Problem addressed: Use AI Agents for cybersecurity review of documents and structured data to improve processes and to help identify areas of risk.
Purpose and benefits: AI agents automate routine tasks, enhance decision-making, and improve operational efficiency.  In addition, the AI Agent for cybersecurity will allow for a customized approach to tie specific requirement documents with structured data from a database.
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: Output of generative AI is administratively prohibited from being used as principal responses in the various scenarios considered high impact.
US federal AI use case: HPC OpenAI-Compatible API
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Enhances scientific productivity by providing OpenAI-compatible APIs to the laboratory, allowing for rapid development of software and small code modifications for larger model usage.
Topic area: Other
Development stage: Deployed
Impact assessment: Provides an LLM API to the entire laboratory free of charge
First used: 2023-09-08
US federal AI use case: Anthropic Claude Pilot
Agency: Department of Energy - LLNL - Lawrence Livermore National Laboratory (LFO)
Problem addressed: Quick access to general knowledge.
Purpose and benefits: Ability to quickly find and access general knowledge and business productivity
Topic area: IT
Development stage: Pilot
Impact assessment: General chatbot knowledge and capabilities
First used: 2025-06-30
US federal AI use case: Ask HR
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: GenAI-RAG on HR content
Purpose and benefits: Productivity Tool
Topic area: HR
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2024-10-01
US federal AI use case: AI-Enabled Tech Desk Agent
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Allow staff to resolve tech issues through an AI-enabled self-service chat agent.
Purpose and benefits: Improves staff support with faster, AI-driven issue resolution.
Topic area: IT
Development stage: Pilot
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2024-10-01
US federal AI use case: mass3
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: GenAI on STEM-optimized LLMs
Purpose and benefits: Productivity Tool
Topic area: Science
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2025-09-01
US federal AI use case: Microsoft 365 Copilot
Agency: Department of Energy - NR HQ - Naval Reactors (NR)
Problem addressed: Modern enterprise search among Microsoft 365 content, AI integration into daily business applications, improvemed management of email and messaging.
Purpose and benefits: Increased efficiency for routine tasks using Microsoft 365 applications
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Output of generative AI is administratively prohibited from being used as principal responses in the various scenarios considered high impact.
US federal AI use case: AI-Form and Questionnaire Assistant
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: "To automate and enhance the completion of standard  forms and questionnaires used onsite at SRNS. This AI aims to reduce time, effort, errors, and enhance context understanding, consistent formatting, and independent outputs from the system."
Purpose and benefits: Reduced development time, reduced errors and rework,  better context understanding, and improved standardization and formatting, reducing time spent on forms by half.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: This AI use case does not significantly affect legal, material, or binding rights or critical access to services. It is focused on enhancing the completion of standard forms and questionnaires, and does not involve decisions with legal, material, bin
First used: 2024-10-01
US federal AI use case: Simulation-based inference for cosmology
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This project will develop and use simulation-based inference to estimate cosmological parameters related to cosmic acceleration in the early and late universe — via the cosmic microwave background and strong gravitational lensing, respectively. This
Purpose and benefits: DOE ECA award. apply SBI to strong lensing and cmb to infer cosmological parameters
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