GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Integrated Financial Management Information System (IFMIS)
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: This initiative enables quantitative decision-making that supports improved budget forecasting, formulation, planning, execution, and reporting based on approved methodologies.
Purpose and benefits: The outcome will be a deeper understanding of IT interdependencies and integrated processes and systems.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Integrated Data Retrieval System Modernization
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: IDRS currently operates on a mainframe with millions of lines of COBOL, Assembler, and C++. The AI tool automates code conversion to Java and migrates data to relational databases, enabling and accelerating modernization, reducing cost, risk, and technical debt.
Purpose and benefits: Decommission mainframe by 2028.  Reduce reliance on shrinking COBOL workforce availability and expertise.  Improve scalability and maintainability. Enable integration with modern IRS systems (UAPI, EDP).
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Treasury Readability
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Government contract descriptions are indecipherable to the average American. Treasury Acquisition Procedures Update No. 25-05 requires that contract descriptions in the Federal Procurement Data System provide a clear and concise plain language description of products or services acquired under a specific contract. Under the Federal Funding Accountability and Transparency Act (FFATA) et seq, the IRS must publish contract spending reports for viewing by policymakers and the public. The IRS has previously funded and published research on the use of models to validate the accuracy of structured contract spending data such as dates, dollar amounts, and addresses. Advances in large language models have made it feasible to automatically assess the quality of unstructured text descriptions of contract purchases. This coding provides automated suggestions from a Large Language Model that outlines failings and details suggestions on how contract descriptions can be improved. AI prompts are used to evaluate the quality of contract descriptions on an adjectival rating scale and provide narrative feedback on how to improve descriptions.
Purpose and benefits: Help Treasury/IRS improve the quality of contract description language and increase transparency with agency contract spending. Evaluate the quality of contract spending descriptions on an adjectival rating scale. Generate narrative feedback on how to improve specific contract descriptions using AI. Generate reporting quantifying trends in contract description quality (e.g. improvement or decline in data quality).
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Modernization Accelerator - Legacy Applications Chatbot & Code Conversion
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Modernization Accelerator
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Data Integration using Informatica Data Management Cloud
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Resume Scanner
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Risk category: High-impact
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Line-Item Consolidation for Form 1120
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Large Business & International (LB&I) partnerships have a variety of forms and statements that include text. Filings include semi-structured attachments to returns that consist of many short free text labels paired with dollar amounts. Short text labels that include information like descriptions, addresses, and alphanumeric type codes. Non-standard labels are difficult to structure and interpret unstructured narrative text, ranging from a sentence to entire paragraph-based documents, that taxpayers provide to explain, justify, or document a position. This is a research project. The goal of this project is to identify opportunities to derive value from the unstructured text. Assess potential value to examiners from consolidation across labeled pairs, utilize consolidated line items for outlier analysis, etc.
Purpose and benefits: Line-item consolidation is a crucial step in providing value to examiners through the grouping of numerous free-text responses into common, recognizable groups that allow for further analysis and exploration. Not only do these line-items become more accessible to examiners, but it facilitates a more robust benchmarking analysis of line-item amounts by creating larger groupings of common line-items. Success in the line-item consolidation stage was defined by how effectively each stage of the text consolidation pipeline could reduce the number of unique line-item descriptions while maintaining an appropriate level of specificity.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2024-09-27
US federal AI use case: Taxpayer 90
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Ticket Management Generative AI Pilot
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: As part of a Chief Technology Officer (CTO) sponsored objective to enable and utilize Generative Artificial Intelligence (Gen AI) capabilities the User and Network Services Information Technology (IT) Service Desk in cooperation with Enterprise Operations will be implementing a limited production challenge to support AI generated Incident Summarization; providing agents with concise summary of case notes and history, Resolution Notes generation; generating accurate resolution notes based on actions taken and solution achieved, and Knowledge Article Generation; generating complete knowledgebase articles base on incident or case records.
Purpose and benefits: We expect to see significant time savings when conducting warm handoffs between teams, while reducing the mean time to restore incidents. Additionally, the effort should ensure high-quality resolution notes are generated to assist in solving future incidents, while feeding existing knowledge review and publication processes potentially empowering self-service capabilities which will reduce time to closure activities.
Topic area: IT
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-06-18
US federal AI use case: GenAI for Low Code Platform
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Leveraging AI product in the HCO-WPA Environment can significantly enhance the efficiency and accuracy of applications development while improving user outcomes. By automating repetitive tasks and providing intelligent suggestions, product simplifies app building, reducing development time and minimizing errors. For scenarios like delivering pay period updates, successors, and workforce demand, product ensures timely and accurate dissemination of information by generating automated workflows and insights. This not only limits human error but also accelerates the process, enabling users to receive updates quickly and consistently.
Purpose and benefits: The integration of AI into Workforce Planning and Succession Planning will provide significant benefits by enabling more accurate forecasting, data-driven insights, and proactive decision-making. AI can identify workforce trends, skill gaps, and attrition risks earlier, allowing leaders to align resources with future mission needs. By automating complex analysis and generating actionable recommendations, AI supports more efficient planning, ensures a stronger pipeline of ready successors, and enhances organizational resilience. The positive outcome is a more agile, informed, and future-ready workforce that can adapt quickly to evolving priorities and sustain mission success.
Topic area: HR
Development stage: Pre-deployment
Risk category: High-impact
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Statistical Information Services Customer Correspondence Analytics
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Statistics of Income (SOI) has collected years' worth of customer correspondence.  While having this information has been useful for historical documentation proposes, it is apparent that moving forward we need to evaluate the trends in requests, identify areas of demand, topics of interest, and areas for internal improvement.  Through the use of AI, we intend to summarize and evaluate the previous several years' worth of correspondence to better direct our limited resources moving forward.  The output will help us better realign our resources to more highly impactful tasks and assignments.  It will also help direct where improvements to current products need to be made and what those improvements ought to be to better inform the public.
Purpose and benefits: Improve customer service by having SOI information more easily understandable and available.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: 1040X AI Transcription
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: This project is meant to improve IRS capabilities to extract information from images and pdfs.
Purpose and benefits: In the f1040x case, this tool will help clear a backlog of scanned, f1040x returns. Generally, the tools will improve the efficiency of various stakeholders within the IRS so that taxpayers receive better service.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Accounts Management “Balance Due” Call Transcript Classification
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: This project aims to sort customer call transcripts into groups based on the issues customers need help with.
Purpose and benefits: The AI system will inform research and decision making to enhance the customer experience.
Topic area: Service Delivery
Development stage: Pilot
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2024-09-09
US federal AI use case: Identity Verification
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Improves identity document verification process for IRS users who complete the registration process. The expected benefit of this AI use case is to detect submissions of forged identity documents.
Purpose and benefits: Improvements to identity verification process to enable taxpayers to access IRS online services.
Topic area: Service Delivery
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2021-06-20
US federal AI use case: Robotic Process Automation for Form 941-X Extraction
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Procurement Data Transparency, Reporting, & Decision Tracking
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Platform provides an analytics hub for contract documents and contract spending metadata. Generative AI is used to produce a summary of the products and/or services acquired under each agency contract.
Purpose and benefits: Improve contracting spending category management and better ability to identify and analyze contracted products and services purchased by the agency.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-04-11
US federal AI use case: Legislative Analysis Tool
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The Legislative Analysis tool is a comprehensive tool for tracking, analyzing, and managing legislative and regulatory changes. It provides the following benefits: Real-time updates on legislative developments at federal, state, & local levels; Robust impact analysis features to assess how legislative changes affect your organization; A comprehensive database of legislative documents, making it easy to search & access relevant information; A user-friendly interface & collaboration features enhance team productivity & compliance management
Purpose and benefits: Improve efficiency
Topic area: IT
Development stage: Pilot
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-05-14
US federal AI use case: F1023 Hybrid NLP and ML Pipeline
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The model attempts to predict non-compliance among tax-exempt entities by leveraging embedding-based feature extraction feeding a downstream classifier.
Purpose and benefits: F1023 and F990 contain natural text fields around activity descriptions, mission or purpose statements making it difficult for Tax Exempt Government Entities (TEGE) to detect non-compliance among the population of Exempt Organizations (EOs). This use case provides improved ability to predict non-compliance.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Integrate Word Processor AI for Digitalization
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The intended purpose of the Digitalization Enablement Platform (DEP) and the integration of AI into processing taxpayer submitted documents is to minimize the transcription and storage of paper documents, streamline case management, and enhance the accuracy and efficiency with which taxpayer information is extracted, organized, and made accessible. The expected benefits include a massive reduction in physical storage requirements, cost savings, improved data security, a reduction in backlogged cases, and improved service.
Purpose and benefits: Use of this system automates what formally required IRS staff to transcribe data form taxpayer submitted forms. This OCR automation, allows for the faster processing of taxpayer submissions and for efficiencies of IRS staff time.
Topic area: Service Delivery
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-08-12
US federal AI use case: AI Voiceover Generation for eLearning Development
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Voiceover generation for training courses. With return-to-office mandates in place we are unable to record narration voiceovers for training courses as we have historically done using live employees and microphone recording kits.
Purpose and benefits: Increased quality, a more diverse selection of voices, much more rapid voiceover generation and revision, increased development efficiency.
Topic area: HR
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2024-05-31
US federal AI use case: Enterprise Systems Testing Generative Pre-Trained Transformer (EST Knowledge Source and Documentation AI Tool)
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The EST GPT project is intended to solve the problem of inefficient and fragmented access to organizational knowledge by creating a centralized, AI-powered repository for documentation, manuals, and legacy data. Currently, employees face delays and inconsistencies when retrieving critical information, leading to wasted time, higher costs, and potential knowledge gaps, particularly during workforce transitions. By leveraging AI, EST GPT will enable quick, accurate, and user-friendly lookup of information, reducing wait times and improving access across the organization. This approach also mitigates the risk of knowledge loss by preserving institutional expertise in a structured, searchable format. As a result, the tool will not only enhance efficiency and decision-making but also deliver cost savings by minimizing duplication of effort and reducing reliance on individual knowledge holders. Ultimately, EST GPT addresses the challenge of knowledge management by ensuring information is always accessible, reliable, and ready to support data-driven operations.
Purpose and benefits: The expected benefits of EST GPT for the agency’s mission and the general public include significantly improved efficiency, cost savings, and enhanced access to critical knowledge. By streamlining navigation across the wide array of testing tools, the AI tool reduces reliance on Subject Matter Experts (SMEs), empowering teams to independently discover and apply insights, which in turn minimizes user wait times and bottlenecks. EST GPT accelerates onboarding by shortening the learning curve, ensuring new staff quickly become productive, and preserving institutional knowledge to prevent disruption during workforce transitions. Its ability to rapidly retrieve information from documentation ensures timely support, reduces delays in decision-making, and sustains project momentum. These improvements not only drive operational efficiency and reduce costs associated with duplicated effort and prolonged training but also strengthen the agency’s ability to deliver reliable, data-driven outcomes. Ultimately, EST GPT supports mission success by safeguarding knowledge and enabling faster, more informed public service delivery.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Using Text Summarization for Open-Ended Survey Responses
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Analyzing open-ended survey responses is very time intensive. This project seeks to reduce the time that analysis takes.
Purpose and benefits: Open-ended survey responses can capture more nuanced information than closed response questions. Using AI to summarize these responses will enable Small Business/Self-Employed (SB/SE) Research to better leverage open-ended responses to provide our customers with more nuanced information.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: A6020(b) Combined Annual Wage Reporting Automation
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: TP360 IRM Research Companion
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: AI is to assistant CSR as Knowledge Management Librarian for natural language querying to find solutions, improving IRM research thus minimizing time spent and redundant research.  Currently CSR do a basic search on SERP https://serp.enterprise.irs.gov/homepage.html and have to browse through the results to find tax related information in IRM while taxpayer is on phone.
Purpose and benefits: AI chatbots can deliver instant responses to routine inquiries, significantly reducing wait times and improving user satisfaction. Chatbots enhanced with RAG can pull responses from live documents or knowledge bases, improving grounding and accuracy
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Procurement Research
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Generative AI is new to the acquisition workforce. User research is needed to determine how to make AI-powered procurement systems easy-to-use, highly functional, and accurate for agency employees performing procurement tasks. Further, there is little research on which AI models are most performant for federal procurement tasks. An objective evaluation is needed to compare commercial AI models and possibly fine-tune or otherwise improve AI models for the federal procurement domain.
Purpose and benefits: America’s AI Action Plan, dated July 2025, calls AI procurement toolbox managed by the General Services Administration (GSA), in coordination with OMB, that facilitates uniformity across the Federal enterprise to the greatest extent practicable. This system would allow any Federal agency to easily choose among multiple models. Agencies should also have ample flexibility to customize models to their own ends, as well as to see a catalog of other agency AI uses. Treasury is well-positioned to make a significant contribution and advance the state of the art in AI models used for procurement tasks.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Research effort may examine AI bias (e.g. large vs. small business) in drafting procurement documents as market research / proposal evaluation. Research effort and not an operational use case where AI will make or influence government decisions.
US federal AI use case: AI-Augmented Software Modernization and Insight
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The IRS maintains a large portfolio of complex Java/Spring Boot applications that accumulate technical debt, dependency conflicts, and inconsistent coding practices. Manually identifying outdated libraries, researching upgrade paths, and resolving build failures consumes significant engineering time and slows modernization efforts. AI is intended to reduce this burden by automating research, detecting vulnerabilities, and proposing secure, standards-compliant code improvements.
Purpose and benefits: For the IRS mission: Faster modernization of taxpayer-facing systems, improved system reliability, and reduced risk from outdated or insecure software components. For the public: More resilient digital services, reduced downtime, and faster delivery of secure taxpayer tools and features. For IRS engineers: Less time spent on repetitive research, freeing focus for high-value mission logic and innovation.
Topic area: IT
Development stage: Pilot
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-08-20
US federal AI use case: Resume Ranking Engine
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Career alignment problem by bridging the gap between employees and job opportunities. Employees struggle to understand how well their resume aligns with specific job roles. AI will help to analyze the resume and job descriptions automatically. Rank roles by fit score so employees can know where to focus. Highlight gaps in skills or experience with actionable suggestions. Provide section by section scoring so the employee will know exactly what needs improvement.
Purpose and benefits: Increased efficiency, higher accuracy, transparency, equity and career growth.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: AI-Powered Enterprise Standard Portfolio (ESP) for End-to-End Technology Lifecycle
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The core problem the AI is intended to solve lies in the fragmented and highly manual nature of how commercial off-the-shelf technologies are managed throughout their life cycle. Today, users submit requests for new software or hardware, but determining whether the request duplicates existing tools, whether the technology is still supported, or whether it fits enterprise standards often depends on tribal knowledge, scattered documentation, or siloed teams. This leads to long cycle times, inconsistent risk evaluations, and limited visibility into how many licenses or subscriptions are actually in use. Organizations struggle to manage active and inactive instances across both on-premise and cloud-based environments, making it difficult to optimize costs or ensure that end-of-life and upgrade paths are handled consistently. The lack of seamless hand-offs across cataloging, approval, procurement, deployment, and monitoring workflows contributes further to inefficiency and risk.
Purpose and benefits: The expected benefits of introducing AI into this process are significant. By providing automated triage and context-aware answers to common questions about request procedures and timelines, AI can shorten decision cycles and improve user satisfaction. Automated checks against enterprise standards and authoritative catalogs ensure duplication and compliance issues are caught early, while lifecycle and vulnerability monitoring reduces the chance of security exposures. Cost efficiency improves as AI correlates license usage data with actual activity, identifying inactive instances and recommending right-sizing actions. Standardized analyses and automatically generated approval packets improve auditability, while consistent tracking of end-of-life timelines and upgrade requirements reduces the likelihood of unexpected outages. Ultimately, the mission outcome is better: users gain timely access to the technologies they need, while the organization maintains governance, security, and fiscal responsibility.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Form 4546 IDR Question-Answering Automation
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Use of Large Language Models (LLMs) to automatically assess whether taxpayers have addressed the questions raised by Large Business and International (LB&I) Revenue Agents in Information Document Requests (IDRs) responses.
Purpose and benefits: This will assist the Revenue Agent by processing the received taxpayer correspondence to speed up disposition.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Vendor Risk Analytics
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Research on vendor risk analytic methods using supervised learning to assess contractor responsibility and whether a prospective vendor would perform successfully. AI can predict potential vendor inability to perform successfully. This capability has the potential to prevent costly, mission-impacting contracting issues. Seminal research and model training in this area was conducted by IRS and Navy personnel who published this paper (https://calhoun.nps.edu/handle/10945/62901). Subsequent, independent federally funded and academic research studies have also found that AI has the potential to improve contractor source selection decision making.
Purpose and benefits: AI assesses the likelihood of successful contractor performance and identifies vendors at heightened risk of poor performance or non-compliance.
Topic area: Procurement and Finance Mgmt
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: AI makes recommendations only and any contracting decisions would go through multiple layers of review by agency officials.
First used: 2019-04-30
US federal AI use case: AI for Schema Development - Online Adaptive Forms
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The Generative AI Tool will assist our project team in developing schemas on all non-tax, tax-related or tax forms they are converting to mobile/online adaptive versions. The use of these generative AI tools will reduce time spent on this task, freeing up our authors to focus on the form authoring process.
Purpose and benefits: The Online Adaptive Forms team is facing significant resource constraints due to budget cuts and limited authoring staff. To meet the IRS’s need to onboard the ~100 non-tax forms in the backlog for online adaptive forms, the project team is leveraging Generative AI to streamline and accelerate the form digitalization process. The opportunities for this use case include removing the need for employees to manually create schemas for our forms, reducing the number of hours spent manually creating schemas and authoring forms, and assisting form authors by generating a first draft version of the mobile adaptive form.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Using AI to analyze Taxpayer Burden Survey open-ended responses
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Large amounts of qualitative data need to be coded to convert to quantitative data for analysis.
Purpose and benefits: Reduced level of effort and objective summarization of qualitative data; Complete data from surveys
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: AI Assisted Business Process Mapping
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: This project utilizes Large Language Model (LLM) to produce a library of IRS business process maps and optionally integrate them into knowledge graphs based on Internal Revenue Manual (IRM), the IRS processing codes (Document 6209) and other pertinent documents. In addition, a custom tool will assist users with prompt engineering to drill down on a specific business area with additional desktop guides and standard operating procedures (SOPs).
Purpose and benefits: This is for the IRS internal use to drive efficiency by mapping processes and helping identifying bottlenecks along the way. The library of the business process map will be a good companion to IRM, Document 6209 and valuable resources to the IRS employees.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: GenAI Productivity Tool
Agency: Department of the Treasury - Technology Common Services Center (TCSC)
Problem addressed: General research and smart assistant functions
Purpose and benefits: Improves organizational efficiency by automating communication, documentation and research tasks.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: The AI system's outcome does not affect civil rights, safety, or access to critical services, therefore, it doesn't meet the criteria for a high-impact AI use case.  This system is provided to bureaus and offices across treasury that may chose to implement high impact use capabilities, however, as the system provider we are unable to indicate this classification.
First used: 2025-05-07
US federal AI use case: GenAI Procurement Tool
Agency: Department of the Treasury - Technology Common Services Center (TCSC)
Problem addressed: Streamlines government procurement and contract management by automating compliance checks, bid analysis, and acquisition workflows. It directly supports federal acquisition compliance by validating documentation against procurement rules (such as FAR and DFARS), ensuring audit-readiness, and maintaining transparent, traceable records of contract decisions—minimizing risks of non-compliance in acquisition processes.
Purpose and benefits: Improves Procurement Efficiency & Accountability — supports Treasury’s goal of ensuring responsible use of federal funds and maintaining integrity in acquisition and financial operations. Simplifies procurement and contract management for Treasury acquisition teams by automating bid review, data analysis, and documentation. It helps staff make faster, data-driven decisions while increasing transparency and reducing administrative burden.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: This AI use case is not considered high-impact for a U.S. government agency. The AI system’s functionality supports acquisition planning, bid analysis, and workflow efficiency improvements. Its outcomes do not directly affect civil rights, individual safety, or access to critical government services. The system operates as a decision-support tool that aids contracting officers and analysts in administrative and analytical tasks, rather than making autonomous determinations impacting individuals or public welfare. Therefore, it does not meet the criteria for a high-impact AI use case.
First used: 2026-01-15
US federal AI use case: ChatOFR AI Chatbot
Agency: Department of the Treasury - Office of Financial Research (OFR)
Problem addressed: In-house AI platform providing OFR staff with access to multiple large language models for question-answering guidance, text generation and summary, and code development assistance
Purpose and benefits: Improves operational efficiency by providing staff with quick access to internal guidance and draft text generation capabilities reducing time spent searching for information and creating initial document drafts
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Internal tool for OFR staff only air-gapped system with no external impact on public services or rights
First used: 2025-01-24
US federal AI use case: Programmatic Access to LLMs via API
Agency: Department of the Treasury - Office of Financial Research (OFR)
Problem addressed: Secure endpoints enabling researchers to submit prompts and receive model output directly within analytical workflows
Purpose and benefits: Streamlines research processes by integrating AI capabilities directly into analytical workflows reducing manual data processing and improving research efficiency
Topic area: Science
Development stage: Deployed
Impact assessment: Secure endpoints for internal researchers only no direct public impact or rights-affecting decisions
First used: 2025-09-11
US federal AI use case: NCCBR Data Validation Pilot
Agency: Department of the Treasury - Office of Financial Research (OFR)
Problem addressed: AI modules applied to our collection pipelines to flag ingestion errors and assess data quality before analyst review
Purpose and benefits: Improves data quality and reduces manual effort in identifying data ingestion errors enabling analysts to focus on higher-value tasks
Topic area: Science
Development stage: Pilot
Impact assessment: Data quality validation tool that flags errors for human review no automated decisions affecting public services or individual rights
First used: 2025-06-01
US federal AI use case: Automated Document Processing
Agency: Department of the Treasury - Office of Financial Research (OFR)
Problem addressed: Converting legacy PDFs and chart images into structured datasets for faster research ingestion
Purpose and benefits: Accelerates research by automating the conversion of unstructured documents into analyzable data formats reducing manual data entry and improving data accessibility
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Document conversion tool for internal research use no public-facing decisions or rights-impacting outcomes
US federal AI use case: Advanced Anomaly & Changepoint Detection
Agency: Department of the Treasury - Office of Financial Research (OFR)
Problem addressed: Scaling our topic-attention analytics to signal market shifts in real time
Purpose and benefits: Enhances market surveillance capabilities by providing real-time detection of significant market changes enabling more timely analysis and response to financial market developments
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Analytical tool for market monitoring provides signals for human analysts to review no automated decision-making affecting individuals
US federal AI use case: Procurement Intelligence
Agency: Department of the Treasury - Office of Financial Research (OFR)
Problem addressed: AI-powered market monitoring to identify competitive suppliers single-source risks and cost-optimization opportunities
Purpose and benefits: Improves procurement efficiency and reduces costs by identifying optimal suppliers mitigating single-source risks and highlighting cost-saving opportunities
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: Internal procurement analysis tool for market monitoring and supplier identification no direct impact on external parties
US federal AI use case: Operational Support Bots
Agency: Department of the Treasury - Office of Financial Research (OFR)
Problem addressed: Chatbots for IT service-desk tasks ticket triage password resets and resource provisioning to free analysts for higher-value work
Purpose and benefits: Reduces IT support workload by automating routine service desk tasks improving response times for common requests and allowing IT staff to focus on complex issues
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Internal IT support tool for OFR staff no external user impact or rights-affecting decisions
US federal AI use case: Repair Spend
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: The AI is to review financial documents and then classify each expense as money spent on "facility repairs" or "not facility repairs".
Purpose and benefits: The expected benefits include reduction of manual hours identifying the types of transactions.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2020-06-09
US federal AI use case: ARS Project Mapping
Agency: Department of Agriculture - Research, Education and Economics
Development stage: Retired
US federal AI use case: NAL Automated Indexing
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: Library content grows rapidly every year as knowledge expands, but it isn't possible for a small team of librarians to organize it manually, efficiently, and accurately at scale.
Purpose and benefits: This system automatically assigns word tags to agricultural research articles from a controlled list of terms provided by the National Agricultural Library Thesaurus (NALT). The tags can be used to look up and retrieve articles. Using these tags benefits users by making it easier to find the content they are looking for.
Topic area: Science
Development stage: Deployed
First used: 2012-06-01
US federal AI use case: Predictive Modeling of Invasive Pest Species
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Imported agricultural products may contain pests. The purpose of this model is to assist in determining if imported agricultural products from other countries to have pests.
Purpose and benefits: Benefits include more reliable discovery and quarantine of pests, preventing pest invasion and making trade safer.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Democratizing Data
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: Identifies which researchers are using National Agriculture Statistics Service (NASS) data, for what purpose, and when they used it.
Purpose and benefits: Provides program evaluation, budget justification, and customer service.
Topic area: Service Delivery
Development stage: Deployed
First used: 2021-07-30
US federal AI use case: LCAT - Land Change Analysis Technique
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: Generating landcover from imagery
Purpose and benefits: Benefits include reduced waste, fraud, and abuse; informed decision making; land use planning and crop reporting; habitat management
Topic area: Science
Development stage: Pilot
First used: 2018-10-01
US federal AI use case: Retailer Receipt Analysis
Agency: Department of Agriculture - Food, Nutrition, and Consumer Services
Development stage: Retired
US federal AI use case: Ecosystem Management Decision Support System (EMDS)
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: This system provides decision support for environmental analysis and planning by using AI-powered tools in ArcGIS and QGIS.
Purpose and benefits: Use of this system empowers stakeholders to make more informed and effective decisions about natural resource management.
Topic area: Energy and Environment
Development stage: Deployed
First used: 1997-02-01
US federal AI use case: Cross-Laminated Timber (CLT) Knowledge Database
Agency: Department of Agriculture - Natural Resources and Environment
Development stage: Retired
US federal AI use case: Raster Tools
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Integrating geospatial analysis with AI in parallel.
Purpose and benefits: This system will make machine learning techniques available for geospatial applications. Benefits include standardization of methods, improved work quality, and increased user productivity.
Topic area: Science
Development stage: Pilot
First used: 2021-08-01
US federal AI use case: TreeMap and FuelMap (all versions)
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Produce tree-level model of the forests of the US, and corresponding model of fuel loading.
Purpose and benefits: TreeMap and FuelMap enable modeling of fuel treatments (amount of wood removed, reduction in fire risk), smoke emissions, and biomass estimation
Topic area: Science
Development stage: Deployed
First used: 2016-10-12
US federal AI use case: Landscape Change Monitoring System (LCMS)
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: LCMS produces annual vegetation cover change, land cover, land use, and tree canopy cover data on National Forest System lands and more broadly across the conterminous United States, Alaska, Puerto Rico, U.S. Virgin Islands, and Hawaii from 1985 through present.
Purpose and benefits: Supporting agency priority initiatives such as quick and efficient monitoring of fire and fuels management across the landscape, documenting vegetation changes due to wildland and prescribed fire, tracking post-fire recovery and restoration work, maximizing the value of existing investments, reducing technical barriers, and improving mission outcomes.
Topic area: Science
Development stage: Deployed
First used: 2016-01-15
US federal AI use case: Forest Health Detection Monitoring
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: This project monitors forest health by detecting tree damage through changes in light patterns collected by satellites.
Purpose and benefits: This detection method helps the Forest Health Protection program monitor forested areas that can't be checked on the ground or with aerial surveys.
Topic area: Science
Development stage: Pilot
First used: 2021-06-29
US federal AI use case: Cropland Data Layer
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: Generating agricultural statistics
Purpose and benefits: This project produces supplemental estimates of crop acreage for internal use and releases geospatial data products to the user community.
Topic area: Science
Development stage: Deployed
First used: 2008-01-01
US federal AI use case: List Frame Deadwood Identification
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: This model helps identify farms that may be out of business on the National Agricultural Statistics Service list. Parts of the model were used to create clear rules to identify these farms.
Purpose and benefits: The resulting list is more accurate and allows for smaller sample sizes, reducing the burden on respondents.
Topic area: Other
Development stage: Deployed
First used: 2016-02-03
US federal AI use case: Climate Change Classification NLP
Agency: Department of Agriculture - Research, Education and Economics
Development stage: Retired
US federal AI use case: Video Surveillance System
Agency: Department of Agriculture - Office of Safety, Security, and Protection
Problem addressed: The purpose of this system is to conduct facial recognition video surveillance to provide enhanced security.
Purpose and benefits: Benefits include reduced labor hours for technicians and enhanced surveillance capability.
Topic area: Admin Functions
Development stage: Deployed
Risk category: High-impact
First used: 2020-02-14
US federal AI use case: Aquisition Approval Request Compliance Tool
Agency: Department of Agriculture - Office of the Chief Information Officer
Problem addressed: This project was developed to help identify likely Information Technology (IT) purchases that do not have an associated Acquisition Approval Request.
Purpose and benefits: The benefits are reducing unauthorized Information Technology (IT) purchases and increasing compliance with IT procurement procedures and approvals.
Topic area: IT
Development stage: Pre-deployment