GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: IV&V Process Accelerators through Generative AI Capabilities
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project is a collection of utility tools that generated by personnel and early adopters at the NASA IV&V Program who have explored and prototyped generative AI applications that can streamline and enhance Independent Verification & Validation (IV&V) activities. Process accelerator efforts focus on automating common, high-effort tasks such as Technical Issue Memorandum (TIM) / formal IV&V issue generation, automated peer review support of technical issues, milestone review summaries, NASA program risk mapping to open IV&V issues, and automated assurance conclusion statements. Additional concepts include automated generation of value statements, status reports, sensitivity/content classification, Tier 1 security-related queries on NASA programs, and IV&V methods generation. These exploratory activities serve as feasibility studies, demonstrating how generative AI can reduce analyst workload, accelerate analysis and reporting, and provide a foundation for future funded capabilities. Insights gained from these concepts directly inform NASA IV&V’s broader AI strategy, guiding the maturation and adoption of advanced assurance tools.
Purpose and benefits: Results and positive outcomes from the NASA IV&V Process Accelerator efforts directly inform and strengthen larger, funded AI initiatives. These exploratory activities have demonstrated how generative AI can streamline traditionally resource and labor-intensive tasks. Even when lightly validated using synthetic or open-source data, these proofs of concept activities highlight that routine, high-effort activities can be effectively automated to a practical degree. This includes processing vast quantities of data in a deterministic manner and providing an end user product for human review and additional analysis, where needed. Collectively, these concepts showcase the potential of generative AI to enhance efficiency, reduce analyst workload, and provide a foundation for more advanced AI-driven capabilities in support of NASA's missions.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Estimating Ocean Color Properties from TEMPO
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Developing hourly ocean color retrievals across North America using geostationary TEMPO instrument
Purpose and benefits: First hourly ocean color retrievals across North America will improve monitoring of ocean health
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Development of a Next-Generation Ensemble Prediction System for Atmospheric Composition
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The project's goal is to reduce the computational burden of atmospheric composition modeling at the GMAO, by building an AI emulator for the GEOS Composition Forecast model.
Purpose and benefits: The project could lead to significant saving in the computational costs of atmospheric composition forecasts for NASA and could provide the public with longer range probabilistic forecasts of air quality.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: XMM-GPT
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: XMM-GPT is a domain-specialized AI assistant built by fine-tuning Google's Open-Source FLAN family of LLMs through transfer learning on up-to-date XMM-Newton Documentation. The model is then integrated into a retrieval-augmented generation (RAG) pipeline to ensure that responses are grounded in mission documentation. This reduces model hallucinations and introduces transparency in its decision making process by relaying where in the documentation it got its answer. XMM-GPT acts as an interactive research aide, supporting a wide-range of users (students, researchers, teachers, PIs) in learning about XMM-Newton and its software the SAS.
Purpose and benefits: Lower entry barriers for new users learning about XMM-Newton and its workflows. Coding assistant that helps to create scripts, explain SAS tasks, and point users to what is causing their coding bugs. Offloads "help desk" queries from mission staff.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SAS Vision
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: SAS VISION is a convolutional neural network (CNN)-based computer vision model designed to identify previously undocumented astronomical objects such as AGNs, NGCs, and stellar sources using large volumes of XMM-Newton observational data. This system fine-tunes open source CNN through transfer learning using XMM-Newton raw FITS files and their corresponding SAS pipeline-processed outputs. It performs a two-stage process: Denoising & Enhancement (using DnCNN, AutoEncoders, U-Net/W-Net, or DDPMs to preprocess and enhance noisy X-ray data), and Object Identification (through CNNs with instance segmentation (Mask R-CNN), enabling detection and classification of celestial objects. In addition to this, cross-observations will enable spatial matching of object features to confirm repeat detections. This model could enable large-scale automated cataloging of undocumented objects not present in SIMBAD, Vizier, or the databases queried by Aladin.
Purpose and benefits: Discovery of New Objects, Accelerates Identification tasks, Improved Catalogs
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Adaptive State-Space Control via Neuromorphic Computing
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Neuromorphic hardware for autonomous control logic adaptation in robotics/aerospace
Purpose and benefits: This use case addresses the limitations of traditional, fixed-logic control systems by implementing an adaptive state-space model on neuromorphic hardware. By representing a system's states and transitions as a network of spiking neurons and plastic synapses, the governing matrices of the control model are transformed from static constants into dynamic variables that evolve based on real-time sensory input and operational feedback.  This enables the system to autonomously learn from its environment, optimize its own control logic, and adapt to unforeseen conditions, creating a foundation for truly intelligent and resilient autonomous systems in fields such as advanced robotics and aerospace.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI-Enhanced Mission Operations Reporting & Decision Support System
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: NASA's Mission Control currently relies on a manual CHIT (Mission Action Request) system for real-time operational decisions, a process that places a high cognitive load on flight controllers who must complete forms, identify all stakeholders, and search historical data with basic tools, making it difficult to spot subtle trends. This use case proposes transforming this system by integrating an AI engine to streamline operations.
Purpose and benefits: The proposed solution would leverage Natural Language Processing (NLP) to enable controllers to initiate CHITs using plain language, while machine learning models would proactively analyze data to identify emerging trends and provide context-aware smart search results from historical mission data. Additionally, the AI would perform an initial automated impact assessment on new requests, flagging potential risks to enhance the speed and safety of decision-making.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Athena (EM32 Document Intelligence)
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Converts 490K+ EM32 repository files into a searchable knowledge fabric. Supports 691+ file extensions, builds cross-document graphs, enables text-to-image queries, OCR, and compliance-aware retrieval. Provides sub-second query response for cached content.
Purpose and benefits: Converts 490K+ EM32 repository files into a searchable knowledge fabric. Supports 691+ file extensions, builds cross-document graphs, enables text-to-image queries, OCR, and compliance-aware retrieval. Provides sub-second query response for cached content.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Autonomous Navigation Decision Making for Lunar/Martian Rovers
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Enable a rover (or rover swarm) to autonomously collect lunar regolith, haul it to a worksite, and execute construction tasks (e.g., berms, landing pad layers, trenching, or feedstock staging for sintering/printing) using AI for perception, mapping, planning, control, and health monitoring.
Purpose and benefits: Enable a rover (or rover swarm) to autonomously collect lunar regolith, haul it to a worksite, and execute construction tasks (e.g., berms, landing pad layers, trenching, or feedstock staging for sintering/printing) using AI for perception, mapping, planning, control, and health monitoring.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Autonomous Object Manipulation Decision Making for Lunar/Martian Rovers
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Allow a robotic arm(s) on the lunar surface (or inside a habitat/lander bay) to autonomously identify, retrieve, and swap between different tools and payloads, then execute a sequence of tasks (e.g., drilling, sample collection, regolith manipulation, structural assembly, or power connector mating).
Purpose and benefits: Allow a robotic arm(s) on the lunar surface (or inside a habitat/lander bay) to autonomously identify, retrieve, and swap between different tools and payloads, then execute a sequence of tasks (e.g., drilling, sample collection, regolith manipulation, structural assembly, or power connector mating).
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: BoundState (Compliance & Risk Engine)
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Validates engineering parts, materials, and BOMs against NASA, ASTM, and program standards. Flags PFAS/non-compliant risks, generates property-based alternative recommendations with confidence bounds. Converts compliance into proactive, evidence-based design intelligence.
Purpose and benefits: Validates engineering parts, materials, and BOMs against NASA, ASTM, and program standards. Flags PFAS/non-compliant risks, generates property-based alternative recommendations with confidence bounds. Converts compliance into proactive, evidence-based design intelligence.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Data analysis and pattern recognition
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: We are often found using optical evaluation on specimens that do not necessarily have a textbook approach to evaluation. By feeding into the knowledge base and creating an agent Chat GSFC can give some reasonable evaluation and cross reference our thoughts with related publications. It can also recommend test data and perform evaluations of the raw data, say from an XRD readout rather quickly as compared to doing it by hand. The key is to feed it known calibrated data for its knowledge base.
Purpose and benefits: We are often found using optical evaluation on specimens that do not necessarily have a textbook approach to evaluation. By feeding into the knowledge base and creating an agent Chat GSFC can give some reasonable evaluation and cross reference our thoughts with related publications. It can also recommend test data and perform evaluations of the raw data, say from an XRD readout rather quickly as compared to doing it by hand. The key is to feed it known calibrated data for its knowledge base.
Topic area: Science
Development stage: Deployed
First used: 2025-04-29
US federal AI use case: Dirty Vacuum rated 6-Axis Robotic Arm Toolpathing
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: MSFC has acquired a custom dirty vacuum rated 6-axis robotic arm from Motiv space systems. This robotic arm while purpose built for manufacturing does require custom toolpathing codes. I used Chat GSFC extensively to write a custom additive manufacturing slicer, gcode parser, and some quality of life upgrades for using the system. We completed various world wide first additive manufacturing builds on our home grown directed energy deposition system using these codes. It also was used to de-bug and work through any issues and errors that arose
Purpose and benefits: MSFC has acquired a custom dirty vacuum rated 6-axis robotic arm from Motiv space systems. This robotic arm while purpose built for manufacturing does require custom toolpathing codes. I used Chat GSFC extensively to write a custom additive manufacturing slicer, gcode parser, and some quality of life upgrades for using the system. We completed various world wide first additive manufacturing builds on our home grown directed energy deposition system using these codes. It also was used to de-bug and work through any issues and errors that arose
Topic area: Science
Development stage: Deployed
First used: 2025-04-29
US federal AI use case: Gap-filled and downscaled vegetation composited index derived from satellite optical observations
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: This project is in partnership with ARC and GeoNEX under the Ecological Conservation NASA Earth Action program. It seeks to leverage Harmonized Landsat Sentinel-2 (HLS) data to provide gap-filled/downscaled NDVI composite using NASA's Prithvi model.
Purpose and benefits: This project is in partnership with ARC and GeoNEX under the Ecological Conservation NASA Earth Action program. It seeks to leverage Harmonized Landsat Sentinel-2 (HLS) data to provide gap-filled/downscaled NDVI composite using NASA's Prithvi model.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Deep Learning for Communication-Limited Spacecraft
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The objective of this project is to investigate the feasibility of applying deep-learning algorithms to communication-limited spacecraft, an operational domain where a slow, restricted, or intermittent downlink bottleneck inhibits the generation of large training datasets on the ground. With novel complex sensors generating ever-increasing amounts of data, it is imperative to be able to autonomously and robustly classify scientifically useful data to maximize scientific utility per bit transmitted to the ground. This project studies two classification approaches, including supervised transfer learning and unsupervised feature extraction followed by clustering, to optimize selection of data products for download.
Purpose and benefits: Deep Learning for Communication-Limited Spacecraft
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Lunar Foundation Model for Planetary Sciences
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Foundation Model trained on LRO WAC, NAC, and RTM imagery to assist lunar scientists with AI applications regarding the surface processes of the moon
Purpose and benefits: Foundation Model trained on LRO WAC, NAC, and RTM imagery to assist lunar scientists with AI applications regarding the surface processes of the moon
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Lunar Rover Localization
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: AI to merge and analyze lunar reconnaissance data with terrain relative navigation techniques utilizing on board sensors to generate high resolution localized maps. Inputs are rover external sensor data & satellite imagery which generates a map for detailed navigation
Purpose and benefits: high resolution localized maps
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine learning for prediction and factor analysis of laser forming parameters
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Various machine learning techniques (neural network, support vector machine, etc.) employed to predict bending angle of sheet metal subjected to laser forming processes and to elucidate most pertinent factors via SHAP analysis
Purpose and benefits: Various machine learning techniques (neural network, support vector machine, etc.) employed to predict bending angle of sheet metal subjected to laser forming processes and to elucidate most pertinent factors via SHAP analysis
Topic area: Science
Development stage: Pilot
US federal AI use case: MIRA (MAPTIS NLP Interface)
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: NLP interface for querying legacy test reports in MAPTIS
Purpose and benefits: Transforms legacy MAPTIS test reports into structured, queryable data. Enables natural language queries across test classes with validated answers tied to MAPTIS IDs, test dates, and methods. Achieved semantic parity for Flammability and Arc Tracking classes; pathway established for remaining 22 classes.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Object classification, change detection, and anomaly detection in LiDAR and other point cloud datasets.
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Leveraging AI for point cloud analysis may advance accuracy and efficiency of:
•Point cloud classification, enhancing capabilities for LiDAR based autonomy and navigation. 
•Change detection, enriching scientific investigations of geomorphology and surface processes.
•Anomaly detection, enhancing inspection of hardware and monitoring of manufacturing processes.
Purpose and benefits: Leveraging AI for point cloud analysis may advance accuracy and efficiency of:
•Point cloud classification, enhancing capabilities for LiDAR based autonomy and navigation. 
•Change detection, enriching scientific investigations of geomorphology and surface processes.
•Anomaly detection, enhancing inspection of hardware and monitoring of manufacturing processes.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: RDUST Regolith Dust Universal System Tracker
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: AI for real-time dust environment simulation and analysis
Purpose and benefits: AI for real-time dust environment simulation and analysis
Topic area: Science
Development stage: Pilot
US federal AI use case: Research to Operations Platform
Agency: National Aeronautics and Space Administration - MSFC: Marshall Space Flight Center
Problem addressed: Platform that leverages foundation models created by NASA IMPACT AI for Science to allow users to inference on the fine-tuned models and visualize the results
Purpose and benefits: Platform that leverages foundation models created by NASA IMPACT AI for Science to allow users to inference on the fine-tuned models and visualize the results
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Development of new tools for detecting and assessing resilient agriculural systems farm performance
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work uses convolutional long short-term memory neural networks to aid in development of new tools for detecting and assessing resilient agriculural systems farm performance based on a variety of Earth and agricultural sensor data.
Purpose and benefits: Development of new tools for detecting and assessing resilient agriculural systems farm performance
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Moody’s Stress Test Modeling
Agency: National Credit Union Administration - NCUA|ONES
Problem addressed: Simulates economic scenarios to assess credit union financial resilience
Purpose and benefits: Enables NCUA to evaluate capital adequacy and risk exposure under stress scenarios
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: High-impact
First used: 2020-06-01
US federal AI use case: ADCo MBS/CMO Modeling
Agency: National Credit Union Administration - NCUA|ONES
Problem addressed: Provides inputs for MBS and CMO performance under stress scenarios
Purpose and benefits: Enhances accuracy of financial risk assessments
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: High-impact
First used: 2020-06-01
US federal AI use case: CoStar CRE Modeling
Agency: National Credit Union Administration - NCUA|ONES
Problem addressed: Provides CRE performance projections under economic stress
Purpose and benefits: Improves accuracy of stress test results for CRE exposures
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: High-impact
First used: 2020-06-01
US federal AI use case: Machine Learning Data Validation
Agency: National Credit Union Administration - NCUA|E&I
Problem addressed: Flags anomalies in Call Report data to improve data quality and reduce manual review
Purpose and benefits: Improve Call Report data quality
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2023-02-01
US federal AI use case: Supervisory Stress Testing
Agency: National Credit Union Administration - NCUA|ONES
Problem addressed: Forecasts loan performance (repayment, defaults) to support stress testing and risk analysis
Purpose and benefits: Estimate loan default probability for Supervisory Stress Testing.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: High-impact
First used: 2023-05-31
US federal AI use case: Risk Indicator Model
Agency: National Credit Union Administration - NCUA|ONES
Problem addressed: Identifies credit unions at risk of CAMEL downgrade to proactively manage NCUSIF exposure
Purpose and benefits: Support credit union supervision
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: High-impact
First used: 2025-01-01
US federal AI use case: Managing Cybersecurity Controls
Agency: National Endowment for the Arts - NEA
Problem addressed: expedite the time it takes to find maliscious packets or intent with the agency's network
Purpose and benefits: expedite the time it takes to find maliscious packets or intent with the agency's network
Topic area: Cybersecurity
Development stage: Pre-deployment
US federal AI use case: Managing Help Desk Tickets - Self Service
Agency: National Endowment for the Arts - NEA
Problem addressed: Reduce the number of IT help desk tickets
Purpose and benefits: Reduce the number of IT Help Desk tickets reducing the strain on the Service Desk technicians, and providing self-service opportunities to the NEA staff
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Categorizing Grant Subjects from Grant Applicants
Agency: National Endowment for the Arts - NEA
Problem addressed: Faster categorization of grants
Purpose and benefits: Faster categorization of grants
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: External "Public Facing" Chatbot
Agency: National Endowment for the Arts - NEA
Problem addressed: Public service chatbot to provide responses to grant applicant and NEA constituent questions
Purpose and benefits: Public service chatbot to provide responses to grant applicant and NEA constituent questions
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: LexisNexus
Agency: National Indian Gaming Commission
Problem addressed: Generative AI-assisted legal research tool
Purpose and benefits: Efficiency
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Adobe Photoshop AI (AI Plugin)
Agency: National Indian Gaming Commission
Problem addressed: Help researchers edit images, videos or other public affairs materials throughout the agency.
Purpose and benefits: Efficiency
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Microsoft Copilot
Agency: National Indian Gaming Commission
Problem addressed: Assist researchers to search and locate information germaine to the agency data for regulation, compliance and information technology within web grounded agency data.
Purpose and benefits: Efficiency
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Canva
Agency: National Indian Gaming Commission
Problem addressed: Assist trainers when creating and building out agency training video modules.
Purpose and benefits: Efficiency
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Articulate
Agency: National Indian Gaming Commission
Problem addressed: Assist trainers when creating and building out agency training modules.
Purpose and benefits: Efficiency
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Text Retrieval and Generation for NRC Staff Support
Agency: Nuclear Regulatory Commission - NRC
Problem addressed: NRC staff are required to discover, retrieve, and analyze information from internally-managed and externally-provided documents.  These documents can be lengthy as well as dense with content and often require cross-reference against multiple policies, standards, guidelines, and procedures. The process of discovery, retrieval, analysis, and cross-reference is time consuming and often represents the most significant bottleneck in carrying out agency activities and in making agency decisions.
Purpose and benefits: This use case enhances the NRC’s efficiency and consistency in licensing, oversight, and other regulatory activities. It enables more efficient capture and use of institutional knowledge to mitigate against the risks associated with staff attrition. The NRC expects this use case to improve the timeliness of its risk- and policy-informed decisions, supporting its mission to enable the safe and secure use and deployment of civilian nuclear energy technologies and radioactive materials.
Topic area: Energy and Environment
Development stage: Deployed
First used: 2025-08-01
US federal AI use case: Support routine administrative tasks and enhancing communications
Agency: Nuclear Regulatory Commission - NRC
Problem addressed: These AI use cases address inefficiencies in administrative workflows, such as scheduling, document drafting, and information retrieval. They help automate repetitive tasks, improve accuracy and accessibility of communications, streamline data analysis and reporting, prioritize service requests, and enhance cybersecurity. By reducing manual workload and error rates, they enable staff to focus on higher-value activities and improve overall productivity.
Purpose and benefits: AI improves agency efficiency, accuracy, and responsiveness by automating repetitive tasks, enhancing communication, and streamlining data analysis. These benefits free staff for higher-value work, accelerate decision-making, improve service delivery, and increase transparency, ultimately supporting the agency’s mission and strengthening public trust.
Topic area: Energy and Environment
Development stage: Deployed
First used: 2025-06-01
US federal AI use case: Unified, secure access to multiple advanced AI language models
Agency: Nuclear Regulatory Commission - NRC
Problem addressed: The NRC faces challenges accessing, integrating, and securely managing multiple advanced AI models from different providers. This creates inefficiencies, increases costs, and complicates compliance with federal security standards. The service addresses these problems by providing a single, secure platform for accessing, evaluating, and managing leading AI models, streamlining adoption while ensuring compliance and operational efficiency.
Purpose and benefits: The service enables NRC staff to securely access top AI models, improving research, analysis, and decision-making efficiency. This accelerates regulatory reviews, enhances data-driven insights, reduces costs, and strengthens NRC’s ability to protect public health and safety through more effective oversight and communication with stakeholders.
Topic area: Energy and Environment
Development stage: Pre-deployment
First used: 2026-01-01
US federal AI use case: Testing industry leading generative AI foundational large language models
Agency: Nuclear Regulatory Commission - NRC
Problem addressed: The AI is intended to address gaps in organizational understanding of how generative AI can support knowledge work. The sprint helps identify potential benefits, limitations, and risks of AI tools, enabling informed decisions on future use, governance, and policy before any deployment.
Purpose and benefits: Improved staff understanding of generative AI capabilities and limitations; increased efficiency in drafting, summarizing, and organizing non-sensitive information; identification of appropriate use cases and risks; and informed development of policies, guidance, and governance for potential future AI use.
Topic area: Energy and Environment
Development stage: Pre-deployment
First used: 2025-12-01
US federal AI use case: Voice to text transcription
Agency: National Transportation Safety Board - NTSB
Problem addressed: Speed and efficiency of screening vehicle voice recorders
Purpose and benefits: Improved timeliness of screening recorders, directing resources to potentially relevant information for further investigation
Topic area: Other
Development stage: Pilot
First used: 2024-09-11
US federal AI use case: Semantic Similarity Text Search
Agency: National Transportation Safety Board - NTSB
Problem addressed: Identifying relevent information in extensive library of historic accident investigation reports, evidence, and safety recommendations
Purpose and benefits: Improved search for potentially relevant safety information
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Dataminr
Agency: National Transportation Safety Board - NTSB
Problem addressed: Timely awareness of transportation safety events
Purpose and benefits: Improved notification and response time for transportation safety events
Topic area: Emergency Mgmt
Development stage: Pilot
First used: 2025-09-30
US federal AI use case: FOIAXpress AI assistant
Agency: National Transportation Safety Board - NTSB
Problem addressed: Timely review of documents for public release
Purpose and benefits: Improved timeliness of response to records requests
Topic area: Admin Functions
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: Regulatory Reform Tool
Agency: Department of the Treasury - General Counsel
Problem addressed: Prepares draft proposed and final regulations; reviews statutes for potential deregulatory actions
Purpose and benefits: The AI improves operational efficiency, reduces manual burden, and enhances service delivery to the public.
Topic area: Admin Functions
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Copilot Studio
Agency: Office of Special Counsel - OSC
Problem addressed: Help complainants select the most likely case type based on a description provided by the user.
Purpose and benefits: Reduction of incorrectly submitted complaints and ease of use for the OSC Online Portal.
Topic area: Benefits Processing
Development stage: Pre-deployment
First used: 2025-10-01
US federal AI use case: Westlaw Edge
Agency: Occupational Safety and Health Review Commission - OSHRC
Problem addressed: Addresses inefficiencies and inaccuracies in legal research.
Purpose and benefits: Reduces the time necessary to perform legal research and provides a tool to check for accuracy in legal writing.
Topic area: Other
Development stage: Deployed
Impact assessment: The use of this AI tool by agency personnel is limited.  If used, the results are subject to extensive review by humans.  In addition, the agency's Generative AI policy limits the information that can be entered into this tool (for example, materials that would be covered by the deliberative process privilege cannot be uploaded).
First used: 2023-10-01
US federal AI use case: Synthetic Data Generation
Agency: Pension Benefit Guaranty Corporation - ITIOD/OGC Privacy
Problem addressed: Automation, Content Creation
Addressing the need to test systems using non-production data
Purpose and benefits: Efficient creation of test data that closely mimics production data, while protecting PII/CUI/etc. (scheduled and/or on-demand), to increase speed and accuracy of system testing
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Media Content Generation
Agency: Pension Benefit Guaranty Corporation - OPEA / COLAD
Problem addressed: Content Creation
Media Content ideation and drafting can be labor intensive
Purpose and benefits: Generation of draft news releases, talking points, speeches, testimony, articles, photo captions and headlines
Generation of images, videos, music, stock photos, and infographics for campaigns, stories, events, and articles
To better serve and inform internal and external audiences
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: AI-Assisted Market Financial Health and Credit Analysis
Agency: Pension Benefit Guaranty Corporation - ONR
Problem addressed: Research, Content Generation
Monitoring and documenting the financial health and credit ratings of thousands of distressed companies is a complex and information-dense analysis process.
Purpose and benefits: AI-Assisted financial health and credit analysis; to include the extraction of key insights from relevant articles, earnings reports, contractual/covenant analysis, and even private credit content to improve discovery, provide more robust information/research, and improve the productivity of financial analysts.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
US federal AI use case: IT Security Monitoring
Agency: Pension Benefit Guaranty Corporation - ITIOD - Security Operations
Problem addressed: Automation, Data Analysis
Safeguarding PBGC data and content
Purpose and benefits: Machine Learning capabilities to enhance IT Security monitoring to protect PBGC assets, people, and data.
Topic area: Cybersecurity
Development stage: Deployed
US federal AI use case: Intelligent, auto-generated meeting summaries, notes, and tasks
Agency: Pension Benefit Guaranty Corporation - PBGC Enterprise
Problem addressed: Automation, Content GenerationMeeting and task documentation, post meeting analysis, decision tracking, etc. are manual administrative actions that may take time and resources away from focusing on higher benefit activities.
Purpose and benefits: Automatic, auto-generated information and actions to provide benefit to the personnel in realizing time savings, instant accessibility to summary information, and many additional productivity gains.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AI-Assisted Training Development
Agency: Pension Benefit Guaranty Corporation - OMA / QMD / LDD
Problem addressed: Content GenerationContent (text, audio, image/graphic) generation is a manual activities.
Purpose and benefits: AI-Assisted training/instructional design creation to boost productivity, reduce training development time, and improve effectiveness and efficiencies of training deployment.
Topic area: HR
Development stage: Pre-deployment
US federal AI use case: AI-Assisted Contracting Content/Document Creation
Agency: Pension Benefit Guaranty Corporation - OMA/Procurement Dept.
Problem addressed: Automation, Content Generation
Federal contracting/procurement can be an arduous and time-consuming task.
Purpose and benefits: AI-Assisted contract/acquisition document drafting to save time, resource, and provide better alignment to Agency policies, FAR, etc.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: AI-Assisted Recognition and Redaction for Legal/Litigation Matters
Agency: Pension Benefit Guaranty Corporation - OGC/GLOD
Problem addressed: Automation, Data Analysis
Manual review and redaction of audio and images can be labor intensive and potentially error prone.
Purpose and benefits: AI-Assisted review of audio, image, video, etc. to aid identification of personnel identifiable information (PII) in efforts to provide ethical, privacy protected evidence/information that is searchable and review-ready of legal personnel.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: AI-Powered General Productivity
Agency: Pension Benefit Guaranty Corporation - Enterprise
Problem addressed: Research, Automation, Content Generation
Manual processes, research, data analysis, etc. can be inefficient and time consuming, resulting in resource suboptimization and productivity losses.
Purpose and benefits: Automation of routine tasks, deeper more effective research, more in-depth and efficient data and information analysis, and support of content creation – to enhance overall productivity, freeing up personnel to address more meaningful, critical work.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Pension Plan Summary Report Assistance
Agency: Pension Benefit Guaranty Corporation - OBA
Problem addressed: Automation, Research, Data Analysis
Complex, cumbersome, and often labor intensive/long time duration to evaluate large documents and data stores
Purpose and benefits: Summarization of Pension Plan information to serve as a context for summary report drafting.
Recognition, potential conversion of unstructured data into machine readable data, evaluation/calculation, etc. of Pension Plan Data.
Topic area: Benefits Processing
Development stage: Pre-deployment
US federal AI use case: Federal Accounting Statutes and Guidelines Alignment
Agency: Pension Benefit Guaranty Corporation - OCFO
Problem addressed: Research, Data Analysis
Alignment to Federal Executive Orders, Memorandums, Guidelines etc. to PBGC policies can be intricate, multi-faceted, and may need many sources at once to fully address Federal Financial Management requirements.
Purpose and benefits: Dynamic, efficient evaluation resulting in summary information for PBGC CFO-Federal Staff to quickly and effectively address updates to Federal guidelines.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: IT Security and Privacy Control Assessment
Agency: Pension Benefit Guaranty Corporation - OIT/Privacy
Problem addressed: Research, Data Analysis, Content Creation
Referencing Federal Cybersecurity and Privacy guidelines, evaluation of controls, interruption of supporting evidence, and generation of meaning outputs is a constant/fluid initiative that requires substantial resource allocation and time.
Purpose and benefits: Increase volume of control assessment, reduce manual labor, automate findings generation in efforts – to reduce control tailoring process time, reduce control implementation time, automate selection of application controls, and support rapid drafting of implementation statements.
Topic area: Cybersecurity
Development stage: Pre-deployment
US federal AI use case: Legislative and Regulatory Analysis / Automation (Actuarial/Financial)
Agency: Pension Benefit Guaranty Corporation - OPEA / PRAD
Problem addressed: Automation, Research, Data Analysis
Collection, review, and evaluation of large unstructured data stores/sources
Purpose and benefits: More automated review/evaluation of unstructured data, allowing for augmented technology and human analysis, and aid in the generate of actuarial/financial projections and reports
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Legislative and Regulatory Analysis / Automation (Actuarial/Financial)
Agency: Pension Benefit Guaranty Corporation - OPEA / PRAD
Problem addressed: Coding
Complex code management
Purpose and benefits: Projection modeling code creation, enhancement, and management; to potentially increase speed and accuracy of stochastic modeling
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: IT Service Management Virtual Agent and Automation
Agency: Pension Benefit Guaranty Corporation - ITIOD/Enterprise
Problem addressed: Automation, Content Generation
Information Technology (IT) incident management is a continuous and at times repetitive,  resource-consuming task.
Purpose and benefits: Automation of routine tasks/IT incident response, providing expanded self service, and provided enhanced data-driven support in efforts to focus human agents to address more complex issues, provide more efficient IT support, enhancing overall productivity and satisfaction, and to provide a more streamline IT incident management capability resulting in potential cost savings/avoidance.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Position Description Analysis and Consolidation
Agency: Pension Benefit Guaranty Corporation - OMA/HRD
Problem addressed: Research, Data AnalysisRepository of current Position Descriptions may have redundant and/or overly specific distinctions of various positions.
Purpose and benefits: Potential reduction of total number of Position Descriptions to better organize and accomplish workforce management and allocation.
Topic area: HR
Development stage: Pre-deployment
US federal AI use case: Analysis of Plan Benefits for Standard Terminations
Agency: Pension Benefit Guaranty Corporation - ONR
Problem addressed: Research, Data Analysis
Complex and often intricate plan benefits review and analysis.
Purpose and benefits: Analysis of pension plan documentation for liability and standard termination preparation.
Topic area: Benefits Processing
Development stage: Pre-deployment
US federal AI use case: eDiscovery Software
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Contextual Research (Exploring policies, regulations, trends, and strategic topics)
Purpose and benefits: OCC is investigating the potential use of eDiscovery software. Reaching out to other Bureaus to get a preliminary interest/requirements list put together.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
US federal AI use case: Large Language Model (LLM) Chatbot for Proof of Concept
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Code Assistance - Generating, debugging, and documenting code
Purpose and benefits: Test PaaS to assist developers.  The LLM will generate comments and documentation alongside code snippets, promoting better code understanding and maintainability.  The LLM works with COBOL, JAVA, Angular, and other programming languages, making it versatile for multi-language projects.  The LLM has the ability to catch common mistakes, improving code quality and reducing debugging time, thereby accelerating the overall coding process.
Topic area: IT
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2024-06-10
US federal AI use case: Large Language Model (LLM) Coding for Proof of Concept
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Searching for agency information using a knowledge retrieval system. Contextual Research (Exploring policies, regulations, trends, and strategic topics).
Purpose and benefits: Test standing up an LLM and train it using documentation such as PDF files and web pages to be knowledgeable of internal systems.  Subsequently use the LLM to power a chat bot that will be used by internal customers.  Test to see if the chatbot can provide fast and consistent answers to customer queries.
Topic area: IT
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2024-06-10
US federal AI use case: USAspending Natural Language Search Initiative
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Contextual Research (Exploring policies, regulations, trends, and strategic topics)
Purpose and benefits: Pilot a free text search function for new users to access insights from USAspending.gov in a more intuitive way.  This effort will also accelerate technical solutioning and future development, and aid in PO decision-making/prioritization.  This pilot will use LLM best practices and technology to accept natural language searches from users.  Test Use of Natural Language Cloud Service to enhance User Search Experience in USA Spending
Topic area: Procurement and Finance Mgmt
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2024-08-29
US federal AI use case: GenAI Productivity Tool Pilot
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Transcribing, summarizing, or other efforts that improve the accessibility of a virtual meeting or interview using AI. (Meeting Summarization and Recap - Generating notes, action items, and summaries from meetings). Generating first drafts of documents, briefing, or communication materials using AI. Content Generation & Refinement (Drafting, rewriting, and improving documents, emails, presentations, and plans). Summarizing the key points of a lengthy report using AI. (Content Summarization - Summarizing emails, chats, meetings, and documents). Code Assistance - Generating, debugging, and documenting code. Data Analysis and Manipulation (Excel formulas, macros, forecasting, and SQL-based data work). Contextual Research (Exploring policies, regulations, trends, and strategic topics). Knowledge Retrieval (Locating known information and presenting or analyzing the information). Document Comparison and Alignment (Comparing versions of documents for consistency and alignment). Email and Task Automation (Automating rules, reminders, and scheduling). How-To Guidance (Step-by-step instructions for tools and workflows). Editing images, videos, or other public affairs materials using AI. Scheduling and managing social media posts using AI.
Purpose and benefits: Pilot AI productivity solution in Fiscal Service tenant to determine business value
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2024-09-05