GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Technical Operations Predictive Maintenance
Agency: Department of Transportation - FAA AJO
Problem addressed: "Utilize equipment telemetry data, statistical modeling and Machine Learning to predict equipment failures before they occur.  This will improve operational efficiency and safety by reducing unscheduled outages and/or shortening outage times as replacemen
Purpose and benefits: Utilize equipment telemetry data, statistical modeling and Machine Learning to predict equipment failures before they occur.  This will improve operational efficiency and safety by reducing unscheduled outages and/or shortening outage times as replacement
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-11-21
US federal AI use case: Airborne Safety Metric (ASM)
Agency: Department of Transportation - FAA AJO
Problem addressed: The use of AI modeling can categorize the types of events based on the information provided in the accident and incident report and its result will support the safety metric calculation.
Purpose and benefits: The AI system can classify the event types such as mid-air collisions, flight into terrain accidents, and turbulence events for a given accident or incident reports.
Topic area: Transportation
Development stage: Deployed
First used: 2019-10-01
US federal AI use case: Surface Safety Metric (SSM)
Agency: Department of Transportation - FAA AJO
Problem addressed: Automated review of accident narratives.
Purpose and benefits: Classification of aircraft accidents as a
 - runway collision
 - taxiway collision
 - runway excursion
 - not a surface event
Topic area: Transportation
Development stage: Deployed
First used: 2018-10-01
US federal AI use case: Surface Report Classifiier (SCM/Auto-Class)
Agency: Department of Transportation - FAA AJO
Problem addressed: To classify surface Mandatory Occurrence Reports (MORs) to reduce the amount of manual event review.
Purpose and benefits: Classes  - runway excursion, runway incursion, surface incident.
Topic area: Transportation
Development stage: Deployed
First used: 2019-01-01
US federal AI use case: Pilot-Controller Voice to Text Transcription
Agency: Department of Transportation - FAA AJO
Problem addressed: To transcribe recorded radio communications between pilots and air traffic controllers operating within the national airspace.
Purpose and benefits: Text transcriptions and speaker role (pilot/controller)
Topic area: Transportation
Development stage: Pre-deployment
First used: 2020-10-21
US federal AI use case: Detection of Unmanned Aircraft Systems (UAS) Encounter
Agency: Department of Transportation - FAA AJO
Problem addressed: Unmanned Aircraft Systems (UAS) are a source of potential safety risk.  Manual data exploration by a human is labor and time consuming.  Automatic data mining can help to identify safety issues.
Purpose and benefits: Binary classification (yes/no) label for UAS event detection
Topic area: Transportation
Development stage: Pre-deployment
First used: 2022-05-10
US federal AI use case: Human Performance Taxonomy labeling
Agency: Department of Transportation - FAA AJO
Problem addressed: to label radio communication transcripts according to the HP Taxonomy labeling system.
Purpose and benefits: One of 400 HP taxonomy labels.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2022-12-15
US federal AI use case: Regulatory Compliance Mapping Tool
Agency: Department of Transportation - FAA AVS
Problem addressed: The AVS International office is required to identify means of compliance to ICAO Standards and Recommended Practices (SARPs).Â  Both SARPs and means of compliance evidence are text paragraphs scattered across thousands of pages of documents.Â  AOV identifie
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: JASC Code classification in Safety Difficulty Reports (SDR)
Agency: Department of Transportation - FAA AVS
Problem addressed: AVS identified a need to derive the joint aircraft system codes (JASC) chapter codes from the narrative description within service difficulty reports (SDR), a form of safety event reporting from aircraft operators. A team of graduate students at George Ma
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Offshore Precipitation Capability (OPC)
Agency: Department of Transportation - FAA ANG
Problem addressed: The AI/ML tool helps detect and predict where precipitation currently is and where it will be at the future at various altitudes in parts of the NAS that do not have traditional ground based weather radar. 
Benefits are better weather dectection and avoid
Purpose and benefits: The output of the system is meant to simulate weather radar where cooler colors (blues and greens) correspond to light precip where hotter colors (yellow, oranges and red) indicate heavy precip and/or more intense precipitation. A similar process is down
Topic area: Transportation
Development stage: Pre-deployment
First used: 2013-12-01
US federal AI use case: Course Deviation Identification for Multiple Airport Route Separation (MARS)
Agency: Department of Transportation - FAA AVS
Problem addressed: The Multiple Airport Route Separation (MARS) program is developing a safety case for reduced separation standards between Performance Based Navigation (PBN) routes in terminal airspace. These new standards may enable deconfliction of airports in high-dema
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Improve and Speed up the Certification Service Oversight Process Using Intelligent Document Review
Agency: Department of Transportation - FAA AVS
Problem addressed: Before digitization: 
* The Certification Services Oversight Process (CSOP) is a manually intensive paper process 

* Requires the public and small businesses to provide input for Certification requests

* No external applicant tracking which results in p
Purpose and benefits: "Before digitization: 

* The Certification Services Oversight Process (CSOP) is a manually intensive paper process 

* Requires the public and small businesses to provide input for Certification requests

* No external applicant tracking which results in
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-07-01
US federal AI use case: Determining Surface Winds with Machine Learning Software
Agency: Department of Transportation - FAA ANG
Problem addressed: Successfully demonstrated use of an AI capability to analyze camera images of a wind sock to produce highly accurate surface wind speed and direction information in remote areas that donâ€™t have a weather observing sensor.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Remote Oceanic Meteorological Information Operations (ROMIO)
Agency: Department of Transportation - FAA ANG
Problem addressed: ROMIO is an operational demonstration to evaluate the feasibility to uplink convective weather information to aircraft operating over the ocean and remote regions. Capability converted weather satellite data, lightning and weather prediction model data in
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Head Kinematics Prediction
Agency: Department of Transportation - NHTSA
Problem addressed: Description: Utilize deep learning models for predicting head kinematics directly from crash videos. The utilization of deep learning techniques enables the extraction of 3D kinematics from 2D views, offering a viable alternative for calculating head kine
Purpose and benefits: Angular velocity - injury prediction
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Crash Parameter Prediction
Agency: Department of Transportation - NHTSA
Problem addressed: Description: Utilize deep learning for predicting crash parameters, Delta-V (change in velocity) and PDOF (principal direction of force), directly from real-world crash images. Delta-V and PDOF are two most important parameters affecting injury outcome. D
Purpose and benefits: Delta-V & PDOF
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Crushed Aggregate Gradation Evaluation System
Agency: Department of Transportation - FRA
Problem addressed: Description: Deep learning computer vision algorithms aimed at analyzing aggregate particle size grading.
Input: Images of ballast cross sections
Purpose and benefits: Ballast fouling index
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Automatic Track Change Detection Demonstration and Analysis
Agency: Department of Transportation - FRA
Problem addressed: Description: DeepCNet-based neural network to identify and classify track-related  features (e.g., track components, such as fasteners and ties) for ""change detection"" applications. Input: Line-scan images from rail-bound inspection systems.
Purpose and benefits: Notification of changes from status quo or between different inspections based on geolocation.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Predictive Analytics Using Autonomous Track Geometry Measurement System (ATGMS) Data
Agency: Department of Transportation - FRA
Problem addressed: Description: Leveraging large volumes of these recursive track geometry measurements to develop and implement automated machine-learning-based processes for analyzing, predicting, and reporting track locations of concern, including those with significant
Purpose and benefits: Inspection report that includes the trending of track geometry measures and time to failure (i.e., maintenance and safety limits).
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Integration of small Unmanned Aircraft System (sUAS) Geospacial Information System (GIS) Technologie
Agency: Department of Transportation - FAA AJO
Problem addressed: This use case will be used to enable new methods and tools that support the maintenance of life cycle of National Airpsace System (NAS) infrastructure.
Purpose and benefits: Used to enable non-Georefereced data to be integrated into digital form.
Topic area: Transportation
Development stage: Deployed
US federal AI use case: Geolocating and Identifying Vehicle Hard Brake, Acceleration and Seat Belt Usage with CV Data
Agency: Department of Transportation - FHWA
Problem addressed: Description:  The ongoing work leverages AI/ML in big traffic data analytics to identify roadway geolocations where potential safety issue may exist through the integration of connected vehicle data, speed and Individual Vehicle Record data, historical cr
Purpose and benefits: Maps and Reports
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Path to Advanced Novel Data Analytics (PANDA), a data science lab.
Agency: Department of Transportation - FHWA Turner-Fairbank
Problem addressed: Description: A data science lab established at Turner-Fairbank Highway Research Center.
Purpose and benefits: Promote usage of AI/ML tools on Databricks platform and implement research use cases across multiple highway desciplines.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Exploratory Advanced Research group of projects
Agency: Department of Transportation - FHWA
Problem addressed: Description: The Exploratory Advanced Research (EAR) Program addresses the need for longer term, higher risk research with the potential for transformative improvements to transportation systems. The EAR Program currently funds several projects that focus
Purpose and benefits: Inputs and outputs vary across the projects.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Enterprise Knowledge Graph and Advanced AI Data Structures Collaboration Group
Agency: Department of Transportation - CAIO, FAA, OST-R, Volpe, OCIO
Problem addressed: DOT-Private Sector collaboration to develop advanced AI data structure expertise, lessons learned, and best practices to accelerate AI development across the agency.
Purpose and benefits: High-quality and efficient data structures increase AI model efficiency and accuracy.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-10-24
US federal AI use case: Transportation Use Case Knowledge Repository (TrUCKR)
Agency: Department of Transportation - CAIO
Problem addressed: TrUCKR is the CAIO managed DOT platform for tracking the Department's unclassified AI use case development, maturity, assessments, clearances, risk evaluations and mitigations, and authorities to operate across the use case lifecycle for operations, resea
Purpose and benefits: TrUCKR is the Department's data source for prioritizing and driving the acceleration of AI, monitoring AI governance and compliance, and internal and external collaboration.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-09-24
US federal AI use case: NETT Council AI Coordination and Activities (AICA) Working Group
Agency: Department of Transportation - CAIO, NETT
Problem addressed: The AICA Working Group consists of Operating Administration (OA) and Secretarial Offices AI leaders and subject matter experts across the Department.  It is chaired by the CAIO and vice-chaired by representatives from the DOT Office of Research and Techno
Purpose and benefits: Provides DOT with the necessary administrative infrastructure for coordinating Executive Order and other federal guidance and mandates, tracking AI activities, and collaborating on AI initiatives and compliance, governance, and guidance documents.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-09-24
US federal AI use case: DOT Workforce Acquisitions and Training Team
Agency: Department of Transportation - CAIO, OST-M, OCIO, OIE
Problem addressed: The Workforce Acquisitions and Training Team members are DOT OA and Secretarial Office leaders responsible for AI talent acquisition and training and inspiring the workforce to accelerate AI adoption.
Purpose and benefits: Provides DOT with coordination and collaboration on AI hiring and training initiatives.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-12-11
US federal AI use case: NETT Council Safety, Rights, and Security Review (SR2) Committee
Agency: Department of Transportation - CAIO, OCIO, OST-P, OST-M, NETT
Problem addressed: The SR2 Committee reviews and approves the operational deployment of all AI use cases and the public sharing of use case data, models, and code. The SR2 Committee is also responsible for performing the Security Review required by Executive Order 14110 Sec
Purpose and benefits: Provides the Department with expert oversight of AI use case safety, security, rights, privacy, and data.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-09-24
US federal AI use case: DOT AI Procurement Team
Agency: Department of Transportation - CAIO, OST-M, FHWA, OCIO
Problem addressed: The Procurement Team members are DOT OA and Secretarial Office leaders responsible for AI procurement and acquisitions.
Purpose and benefits: Provides DOT with internal and external coordination and collaboration on AI procurement and acquisition policy and procedures.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-09-24
US federal AI use case: DOT AI Emerging Technology Team
Agency: Department of Transportation - CAIO, OST-R, OCIO
Problem addressed: The AI Emerging Technology Team members are DOT OA and Secretarial Office leaders most knowledgeable in leading-edge AI technology, capacity, and capabilities.
Purpose and benefits: Provides DOT with internal and external coordination and collaboration on establishing and maintaining leading-edge AI technology, capacity, and capabilities.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2024-09-24
US federal AI use case: NHTSA Interim Generative AI (GenAI) Usage Guidance
Agency: Department of Transportation - NHTSA
Problem addressed: Guidelines on usage of commercially available Generative Artificial Intelligence (GenAI) Tools and Services.
Purpose and benefits: NHTSA guidelines (do's and don'ts) for using publicly available GenAI services while protecting security and privacy of DOT data.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: DOT Generative AI (GenAI) Policy
Agency: Department of Transportation - CAIO, OST-P, OCIO, OST-R
Problem addressed: Provide DOT-wide generative AI (GenAI) policy.
Purpose and benefits: Establish guidelines and safeguards for GenAI use within the Department.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Surface Safety Metric (DOT Key Performance Indicator)
Agency: Department of Transportation - FAA AJO
Problem addressed: Creating weighting scheme values based on safety data for measuring and monitoring safety risk in the National Air Space. The weights are used to generate safety indices for surface and airborne settings, for both commercial and non-commercial flights.
Purpose and benefits: Safety indices for Commercial and Non-Commercial accident and incident categories with the SSM Safety Performance Target.
Topic area: Transportation
Development stage: Deployed
First used: 2024-10-01
US federal AI use case: Power Platform Use
Agency: Department of Transportation - FAA AJO
Problem addressed: Speeds up the development of code structure.
Purpose and benefits: Provides programming language recommendations (C#) to support coding/software development.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-04-01
US federal AI use case: Tech Ops LLM Document Search
Agency: Department of Transportation - FAA AJO
Problem addressed: The AI system designed for Tech-Ops personnel is a comprehensive support tool that enhances efficiency by providing rapid, contextually accurate answers drawn from a central Knowledge Library. This Knowledge Library contains Technical Instruction Manuals,
Purpose and benefits: Direct Answers to Queries: When a technician inputs a question in plain language, the AI generates a concise, relevant answer drawn directly from the Technical Instruction Manuals, Maintenance Handbooks, or other materials in the Knowledge Library. This r
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-05-01
US federal AI use case: Human Resources Policy Manual (HRPM) LLM Document Search
Agency: Department of Transportation - FAA AHR
Problem addressed: To allow employees to quickly and easily (via natural language processing) ask Human Resource policy questions and quickly receive back an easy to understand response.
Purpose and benefits: The system outputs HR policy in response to questions which are input by users (employees).
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-04-01
US federal AI use case: Financials LLM Document Search
Agency: Department of Transportation - FAA AFN
Problem addressed: Improving efficiency and productivity in budget and finance.
Purpose and benefits: financial data retrieved from the text resources
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-02-01
US federal AI use case: Civil Aviation Registry Electronic Services (CARES) LLM Document Search
Agency: Department of Transportation - FAA AVS
Problem addressed: CARES will use AI (Azure chatbot) to scan thousands of pages and generate an accurate answer. The benefits will be a faster and deeper understanding of the business requirements so the FAA can speed up our development.
Purpose and benefits: The Azure chatbot will find the information requested and output the information in a clear, easy to understand answer.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-07-26
US federal AI use case: Financial Policy LLM Document Search
Agency: Department of Transportation - FAA AFN
Problem addressed: The Financial Policy Chatbot (AI system) will search a variety of financial policy documents to answer users' financial policy questions, and more importantly, will link to the document(s) where it found the answer.  The the chatbot will initially be used
Purpose and benefits: The Financial Policy Chatbot (AI system) will search a variety of financial policy documents to answer users' financial policy questions, and more importantly, will link to the document(s) where it found the answer.
Topic area: Transportation
Development stage: Deployed
First used: 2024-06-13
US federal AI use case: Spectrum Assignment & Engineering Team LLM Document Search
Agency: Department of Transportation - FAA AJO
Problem addressed: Intended purpose: To make the user experience of end users more easy and  interactive and engaging.

Benefits: Increased customer satisfaction of FAA's coordination systems' users
Purpose and benefits: Responses to help questions
Topic area: Transportation
Development stage: Deployed
First used: 2024-04-10
US federal AI use case: National Program Office LLM Document Search
Agency: Department of Transportation - FAA AVS
Problem addressed: The intended purpose is to provide answers to Element Design Data collection Tool (ED DCT) questions used during COS (continued operational safety), IC (initial certification), and configuration changes of a certificate holder's operation. The benefits in
Purpose and benefits: The outputs are aswers to Element Design Data collection Tool (EDDCT) questions.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-07-25
US federal AI use case: FAA Orders and associated supplemental change notices LLM Document Search
Agency: Department of Transportation - FAA AJO
Problem addressed: The intended purpose of the AI chatbot is to streamline the retrieval of information from multiple FAA Orders and change notices. The expected benefits include saving time, improving accuracy in finding specific citations, and reducing manual effort for u
Purpose and benefits: The chatbot outputs specific citations and relevant sections from multiple FAA Orders and change notices, which provides quick access to  information based on user queries. It delivers clear and concise responses by searching through documents like JO 711
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-05-20
US federal AI use case: Remote Maintenance Monitoring (RMM) Analyzer Copilot
Agency: Department of Transportation - FAA AJO
Problem addressed: The intended purpose of the AI is to serve as a "Copilot" for technical operations, allowing users to interact with complex data in plain language. This Copilot will retrieve, interpret, and present relevant information from the Instrument Landing Systems
Purpose and benefits: "Enhanced Accessibility: Users can pose questions in plain language, bypassing the need for technical know-how on data filtering or analysis, making data insights available to a broader range of personnel.
Time Efficiency: By quickly retrieving relevant d
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-10-30
US federal AI use case: National Airspace System (NAS) Safety Anomaly Metric with STAD integration (Safety Trend Analytics D
Agency: Department of Transportation - FAA AJO
Problem addressed: To measure and monitor the degree of anomaly or deviation from a baseline based on safety data in the National Air Space to the Service Area, district, and facility level.
Purpose and benefits: Day look ahead forecast of anomalous/non-anomalous conditions at levels from NAS-wide down to facilities.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-10-01
US federal AI use case: Using Generative AI to Automatically Tag Narratives with Human Performace Common Taxonomy Factors
Agency: Department of Transportation - FAA AJO
Problem addressed: Using genAI, automatically tag text narratives from Comprehensive Electronic Data Analysis and Reporting (CEDAR) Mandatory Occurrence Reports (MOR's) with factors from Human Performance Common Taxonomy (HP CT) at scale and with significant speed up.
Purpose and benefits: HP CT factors related to an MOR's narrative.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-08-01
US federal AI use case: Generative AI Use Case from Inventory
Agency: Department of Transportation - FAA AJO
Problem addressed: This use case will be used increase trust in new technology approach through this socialzation exercise. And increase buy-in and education of stakeholders with a useful product from their input.
Purpose and benefits: Execute a use case chosen from a collection of candidates from Safety Trend Analytics Dashboard (STAD) stakeholders.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-10-01
US federal AI use case: Human Performance (HP) Fatigue Recommendations
Agency: Department of Transportation - FAA AJO
Problem addressed: This use case will be used to develop and understanding of HP Fatigue recommendations and ties to safety risk.
Purpose and benefits: To be determined
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-11-18
US federal AI use case: Prognosis and Diagnosis Based on Contributing Factors to Safety Risk
Agency: Department of Transportation - FAA AJO
Problem addressed: To increase expainability and interpretability of changes in safety risk based on contributing factors from and LLM tuned to Human Factors Common Taxonomy.
Purpose and benefits: What are the Human Factors Common Taxonomy factors that are associated with a change in safety risk in the NAS.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Knowledge Representation for Deep Learning
Agency: Department of Transportation - FAA AJO
Problem addressed: The overall purpose of the effort is to establish Knowledge Graph usage to develop Deep Learning, starting with a specific problem statement. The problem statement is to "Enhance the management and resolution of deviation events by providing actionable in
Purpose and benefits: 1. Diagnostic Insights
-	Event Analysis: Contributing factors to a deviation, with root cause identification based on historical and real-time data.
-	Priority Assessment: Highlight of critical issues requiring immediate attention, ranked by severity and
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-06-03
US federal AI use case: Identification of Similar Continued Operational Safety (COS) Events
Agency: Department of Transportation - FAA AVS
Problem addressed: AIR is utilizing AI within Palantir Foundry (i.e., Foundry's "Artificial Intelligence Platform" (AIP)) to search, find and link similar types of events (e.g., Continued Operational Safety (COS) event reports, Notices of Noncompliance (NCN), semantic searc
Purpose and benefits: The output aggregates information from disparate sources to provide an integrated view of events that are similar.
Topic area: Transportation
Development stage: Deployed
First used: 2024-01-02
US federal AI use case: Flight Standards Inspector Readiness Program (FSIRP)
Agency: Department of Transportation - FAA AVS
Problem addressed: This use case will help automate current manual processes and reduce tasks.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-01-02
US federal AI use case: Court Reporting and Testimony Management Platform
Agency: Department of Transportation - FAA AGC
Problem addressed: This technology will provide solutions for remote, hybrid, and in-person proceedings that will include both transcription and translation services.
Purpose and benefits: The FAA Office of General Counsel (AGC) personnel would receive detailed summaries for both rough and certified transcripts from a variety of proceeding depositions, arbitrations, expert interviews, meetings, and more.  In addition, such a product would p
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: AI-Assisted Legal Research
Agency: Department of Transportation - FAA AGC
Problem addressed: The AI-Assisted Research will return a summarized overview or draft with detailed insights from top results, along with a list of key cases statutes and regulations.
Purpose and benefits: The ability to type a question, get an answer, and have all the supporting resources right underneath that answer.  Additionally, the AI-Assisted Research answer or draft generated is supported by case law that is already within the database.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Service Difficulty Reporting System (SDRS) Joint Aircraft System/Component Code (JASC) Code Picker
Agency: Department of Transportation - FAA AVS
Problem addressed: This use case will leverage AI to assist in sorting and assigning proper JASC codes within the SDRS environement as an addition into the next development cycle for SDRS.
Purpose and benefits: Modified original input of JASC codes to ensure correct and consistent JASC Codes within the system
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-11-25
US federal AI use case: Case and Document Management Copilot
Agency: Department of Transportation - FAA AGC
Problem addressed: The Copilot would be natively embedded across an application to deliver a consistent user experience that can answer questions, generate content, and dynamically automate any action.
Purpose and benefits: The copilot includes a library of actions, which are pre-programmed capabilities that enable the copilot to not only answer questions using business data, but also string together workflows to get things done in support of users.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: AI Freedom of Information Act (FOIA) Request Classification
Agency: Department of Transportation - FAA AFN
Problem addressed: Will strengthen the Intake and Assignment Branch's precision of FOIA request assignments at the first take, freeing up time to actively engage with requesters and customers alike.  Overall, these FOIA tools will allow for a more informed decision-making p
Purpose and benefits: The output will provide guidance for the FOIA Xpress end user in determining the routing assigment to the approprioate LOB/Organization.
Topic area: Transportation
Development stage: Pre-deployment
First used: 2024-09-16
US federal AI use case: Community Engagement Chat Bot
Agency: Department of Transportation - FAA APL
Problem addressed: Assisting the public in finding information on the FAA website.
Purpose and benefits: We are using our AI Chat Bot to better inform the public. In our efforts to engage with the public we wanted to use this tool to direct the public to the tremendous amount of information that is on the FAA Website.
Topic area: Transportation
Development stage: Deployed
First used: 2021-06-08
US federal AI use case: Public Comment Analyzer (PCA)
Agency: Department of Transportation - OST
Problem addressed: The business goal of this project is to use a powerful language model to analyze public comments. The model will:
•	Categorize comments into different topics
•	Detect the sentiment (positive, negative, or neutral) of the comments
•	Generate summaries of the comments
•	Provide daily updates on the comments
•	Allow secure access to the information
Purpose and benefits: The anticipated benefits are: Improve the accuracy of the analysis; reduction of human analysis required to review all public comments; streamline the process of reviewing comments; keep feedback up to date to help inform decision making. It will allow subject matter experts (SMEs) to more quickly review summaries of the submitted comments and obtain additional details where and as needed. It will also provide SMEs with the general sentiments of public comments
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-02-14
US federal AI use case: Conversational Multi-modal Chatbot (Google Gemini)
Agency: Department of Transportation - OST
Problem addressed: DOT needs a large language model that can integrate with word processing, email, presentations and shared drives.
Purpose and benefits: Google Gemini will allow DOT employees to Generate reports that automatically align with specific Operating Administration (Mode) format;  Generate custom visuals and slide outlines directly inside presentation.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-10-01
US federal AI use case: Report Sumarization (Google NotebookLM)
Agency: Department of Transportation - OST
Problem addressed: DOT employees need an environment to upload custum Retrieval-Augmented Generation (RAG).
Purpose and benefits: NotebookLM allows employees to upload documents that create a private RAG environment.  NotebookLM also condense long PDFs, articles, or transcripts into bulleted executive summaries, podcast style audio or action items
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-10-01
US federal AI use case: USAi (multi-model large language models)
Agency: Department of Transportation - OST
Problem addressed: DOT needs access to multi-model environment that allows to experiment with and compare top AI models.
Purpose and benefits: It accelerates DOT modernization by providing a trusted, standards-aligned environment for tasks like document summarization and code generation while ensuring cross-agency collaboration and security.  This will also allow tech-savy users access to API for further automation.
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-11-15
US federal AI use case: AI Answers in Clearinghouse Network
Agency: Election Assistance Commission - Clearinghouse Division
Problem addressed: AI Answers is a chatbot integrated into the Civic Roundtable platform that allows election officials to query AI about discussions and resources within the Clearinghouse Network. It provides a natural language method for navigating community content without searching the broader internet.
Purpose and benefits: This tool improves access to EAC-provided resources, reduces barriers to finding relevant content, and enables election officials to quickly locate discussions, documents, and guidance within the Clearinghouse Network.
Topic area: Service Delivery
Development stage: Pilot
First used: 2025-08-01
US federal AI use case: Internal Generative AI Tools for Staff Productivity
Agency: Election Assistance Commission - All EAC divisions where staff elect to use approved tools
Problem addressed: These tools allow EAC staff to draft, edit, summarize, and rewrite documents; brainstorm ideas; and support plain-language improvements while adhering to the EAC’s AI Use Policy. The EAC uses widely available commercial AI capabilities embedded in productivity and communication tools, such as Microsoft 365 Copilot features for drafting and editing, AI-enabled meeting summarization and captioning, translation features, and document refinement. These tools provide general-purpose assistance to staff and do not make or influence decisions about individuals or rights.
Purpose and benefits: Improved staff productivity, clearer written communication, faster drafting workflows, and enhanced research and summarization support.
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-09-01
US federal AI use case: Grants Lifecycle Application System
Agency: Election Assistance Commission - Grants Division
Problem addressed: This tool is built into the Grants Division's grants management system, and it allows EAC staff to review high level trends among grant applications.
Purpose and benefits: Improved staff productivity, faster data anaylisis, and enhanced research and summarization support.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-08-01
US federal AI use case: Routing emails from shared inboxes
Agency: Election Assistance Commission - All EAC Divisions
Problem addressed: This EAC uses a single inbox for the majority of its public communications. This tool is intended to route those incoming messages to the appropriate individuals or inboxes immediately to decrease response time and improve custormer engagement.
Purpose and benefits: Improved staff productivity, faster drafting workflows, and enhanced customer service.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-01-01
US federal AI use case: Loan information retrieval
Agency: Farm Credit Administration
Problem addressed: Democratizes data access by allowing users to find information using conversational language.
Purpose and benefits: More efficient workflows and reduced timelines by allowing business users to find and access data more quickly.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: User training in agency learning management system
Agency: Farm Credit Administration
Problem addressed: Current professional development training options do not completely meet emerging staff needs for softskill development.
Purpose and benefits: More comprehensive options for the development of staff professional skills.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Examination knowledge assistant
Agency: Farm Credit Administration
Problem addressed: Addresses challenges staff face in quickly searching across multiple documents and locating relevant references.
Purpose and benefits: More efficient workflows and reduced timelines by allowing staff to select and search multiple references at once.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: AI-Enhanced ULS Reverse Engineering
Agency: Federal Communications Commission - Wireless Telecommunications Bureau (WTB)
Problem addressed: Accelerates legacy code analysis and documentation that would otherwise require extensive manual review.
Purpose and benefits: Accelerates the analysis and documentation of legacy code in the Universal Licensing System (ULS), reducing manual workload and enabling timely reverse engineering. This supports long-term modernization of the ULS application, helping the agency maintain a scalable, reliable licensing system and improving service delivery to the general public.
Topic area: IT
Development stage: Pilot
First used: 2025-01-03
US federal AI use case: ECFS Comment Analytics Deduplication Services (CADS)
Agency: Federal Communications Commission - Wireline Competition Bureau (WCB)
Problem addressed: Solves the challenge of efficiently analyzing large volumes of public comments by automating sentiment analysis, deduplication, and summarization.
Purpose and benefits: Accelerated and more accurate analysis of public comments, enabling the FCC to better understand public sentiment on net neutrality, reduce manual review burdens, and make more informed, transparent, and timely regulatory decisions that reflect public input.
Topic area: IT
Development stage: Retired
US federal AI use case: Comment Legal Analysis & Review Assistant
Agency: Federal Communications Commission - Wireless Telecommunications Bureau (WTB)
Problem addressed: Manually reviewing and analyzing large volumes of public comments, which is time-consuming, inconsistent, and resource-intensive for legal teams by automating the identification of key legal themes, sentiment, and duplicate content to support faster, more consistent, and scalable legal analysis.
Purpose and benefits: Improves the agency’s ability to review public comments quickly and consistently, supporting more efficient, informed, and legally grounded rulemaking.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: SharePoint Copilot for Workflow Automation
Agency: Federal Communications Commission - Office of Engineering and Technology (OET)
Problem addressed: Enables staff to efficiently manage and organize SharePoint content by automating repetitive tasks such as document tagging, summarization, and permission updates, reducing manual effort and improving consistency across teams.
Purpose and benefits: Improves internal collaboration and operational efficiency across FCC bureaus and offices by streamlining content workflows. This allows staff to focus on higher-value work, supports faster service delivery, and enhances responsiveness to public needs through better-organized and accessible information.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Automated Complaint Categorization for Enforcement Bureau
Agency: Federal Communications Commission - Enforcement Bureau (EB)
Problem addressed: The AI model is designed to automate the categorization of consumer complaints received by the Enforcement Bureau. Currently, this process is performed manually by staff, which is time-consuming and prone to inconsistency. The model uses natural language processing (NLP) to classify complaint text based on predefined categories, streamlining the workflow and improving accuracy.
Purpose and benefits: The AI system improves operational efficiency by reducing the manual workload for staff, allowing them to focus on higher-priority enforcement activities. It enhances consistency in complaint categorization, which supports more accurate reporting and analysis. Faster processing of complaints enables quicker responses to public concerns, and the structured data output supports better decision-making and policy development aligned with the agency’s mission.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Automated Attachment Intelligence for FCC Licensing Systems
Agency: Federal Communications Commission - Wireless Telecommunications Bureau (WTB)
Problem addressed: The AI system is designed to solve the challenge of extracting and organizing critical information embedded in attachments submitted across FCC licensing systems. Currently, staff must manually search through multiple systems and thousands of documents to locate ownership details and other key data, which is inefficient and time-consuming. The proposed solution automates this process using AI-powered OCR and entity recognition, enabling faster and more accurate access to relevant information.
Purpose and benefits: The AI solution will enhance the FCC’s ability to research telecommunications entities, particularly in support of national security efforts such as identifying foreign ownership and monitoring compliance with the Covered List. It will reduce the time and effort required to compile case materials, improve the accuracy of investigations, and enable faster decision-making. These improvements support the FCC’s mission to protect the communications infrastructure and ensure regulatory compliance, ultimately benefiting the public by strengthening oversight and enforcement capabilities.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Student Monitoring
Agency: Federal Deposit Insurance Corporation - Corporate University
Development stage: Retired
US federal AI use case: Plagiarism Detection
Agency: Federal Deposit Insurance Corporation - Corporate University
Development stage: Retired
US federal AI use case: Student Outcome Projection
Agency: Federal Deposit Insurance Corporation - Corporate University
Development stage: Retired
US federal AI use case: Facilitating Status
Agency: Federal Deposit Insurance Corporation - Corporate University
Development stage: Retired
US federal AI use case: AI Assisted Data Collection
Agency: Federal Deposit Insurance Corporation - Division of Depositor and Consumer Protection
Problem addressed: The current process to extract structured data from bank provided PDF documents of consumer loan applications takes FDIC personnel a significant amount of time to manually transcribe data from PDF loan applications, leading to delays, high error rates, and increased costs. The AI solution is intended to automate data extraction, reducing transcription errors and enabling faster, more accurate collection of loan application data.
Purpose and benefits: The FDIC will more effectively and efficiently utilize its examination resources by reducing manual components of data collection, reducing data transcription error rates and increasing speed of data collection.
Topic area: IT
Development stage: Deployed
First used: 2025-01-01
US federal AI use case: AI Assisted Time Management
Agency: Federal Deposit Insurance Corporation - Division of Depositor and Consumer Protection
Development stage: Retired
US federal AI use case: Compliance Risk Monitoring
Agency: Federal Deposit Insurance Corporation - Division of Depositor and Consumer Protection
Problem addressed: Examiners must analyze a large amount of data to identify regulatory areas with relatively higher risk of violations on a data driven objective basis. This process is time consuming and does not currently utilize ML, which can reduce time intensity and improve effectiveness of risk detection.
Purpose and benefits: FDIC will more effectively and efficiently utilize its examination resources on financial institutions and regulatory areas where consumer harm risk is highest.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Pre-Examination Planning Monitoring
Agency: Federal Deposit Insurance Corporation - Division of Depositor and Consumer Protection
Problem addressed: The pre-exam planning process is time intensive and FDIC has limited examiner resources to review pre exam planning materials and make examination scoping decisions
Purpose and benefits: This project mitigates time constraints and limited examiner resources by enhancing efficiency of pre-exam planning by identifying PEP questions that are not indicative / misaligned with risk, thereby reducing examiner burden and yielding time savings for pre exam planning
Topic area: Other
Development stage: Deployed
First used: 2023-01-01
US federal AI use case: HMDA (Home Mortgage Disclosure Act) Outlier Screen
Agency: Federal Deposit Insurance Corporation - Division of Depositor and Consumer Protection
Problem addressed: Scoping fair lending examinations can be time intensive.
Purpose and benefits: The HMDA (Home Mortgage Disclosure Act) program pre-screens off-site mortgage data to allow better utilization of examiner time on the highest risk areas and provides a data driven, objective basis to determining risk identification.
Topic area: Other
Development stage: Deployed
First used: 2019-01-01
US federal AI use case: Generative Artificial Intelligence (AI) for Legal Research
Agency: Federal Deposit Insurance Corporation - Division of Administration
Problem addressed: Reduce the time it takes to find legal research information, so approved FDIC employees can answer the legal sensitive questions faster and easier.
Purpose and benefits: Thomson Reuters plans to deliver generative AI capabilities in Westlaw. This use case will allow users with the ability to find the answers they need faster and easier. This directly supports our mission by providing users with enhanced research capabilities through generative AI.  

This directly supports our mission by providing users with enhanced research capabilities through generative AI.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Plain Language Policy Assistant
Agency: Federal Deposit Insurance Corporation - Chief Information Officer Organization
Development stage: Retired
US federal AI use case: Automatically-Scored Writing Assessment (AWA)
Agency: Federal Deposit Insurance Corporation - Division of Administration
Problem addressed: Reduce the time it takes to review the writing assessment and cost of essay scoring for the purpose of making more timely employment decisions.
Purpose and benefits: The Automatically-Scored Writing Assessment (AWA) is intended to significantly increase speed and reduce cost of essay scoring for the purpose of employment decisions.
Topic area: HR
Development stage: Deployed
Risk category: High-impact
First used: 2008-01-01
US federal AI use case: FDIC Predictive Budget Analysis
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: Take in budget data for reporting and prediction. Analyze and explain differences in budget and actuals data. The AI solution is intended to decrease time to produce results via automation and reduce risk of human error. It helps solve the problem of timeliness of predictive efforts.
Purpose and benefits: Reduces time and work to produce the report; increases response time to react and review data; allows deeper dives. This helps protect the DIF.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment