GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: FAS Vision Agentforce
Agency: General Services Administration
Problem addressed: The AI, specifically the "FAS Vision Agentforce" system, is intended to solve several key customer service challenges by automating and improving operations. Its primary purpose is to efficiently handle customer inquiries, especially routine ones like order status requests, and to automate discrepancy case creation for issues with orders received. By doing so, it aims to alleviate the burden on human customer service representatives, reduce manual processes, and provide quicker, more consistent support to agency customers.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: ServiceNow Generic Ticket Classification
Agency: General Services Administration
Problem addressed: Used to automatically route generic tickets to the correct group.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: ServiceNow Virtual Agent (Curie)
Agency: General Services Administration
Problem addressed: This use case is designed for use within GSA to allow employees to research IT issues presenting to their workstation or find a quick answer to an IT procedure.
Topic area: Service Delivery
Development stage: Deployed
US federal AI use case: Marketplace: AI Supported Bookings & Space Stacking
Agency: General Services Administration
Problem addressed: The use case is intended to solve the problem of inefficient real estate resource management within federal agencies. It aims to optimize workspace utilization by providing smart recommendations for seating arrangements and space allocation, thereby reducing friction in the reservation process, minimizing unused space, and enabling data-informed decisions for space consolidation and cost reduction.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: OCFO Chatbot
Agency: General Services Administration
Problem addressed: The AI is being developed to address the challenge of providing efficient and timely financial inquiry support by automating responses to common user questions. This aims to reduce the workload on financial personnel and improve the accessibility of accurate financial guidance, thereby streamlining the process and eliminating the need for users to wait for human intervention for routine inquiries.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: GovCXAnalyzer
Agency: General Services Administration
Problem addressed: The AI is intended to enhance the analysis of customer experience (CX) data across government services. It aims to process structured and unstructured customer feedback to identify key themes, sentiments, and pain points, thereby improving government services.
Topic area: Service Delivery
Development stage: Deployed
US federal AI use case: Strategic Atlas
Agency: General Services Administration
Problem addressed: The AI is intended to solve the problem of difficulty in monitoring strategy execution and related opportunities at GSA, specifically because execution often depends on agency partners whose priorities, progress, and results are hard to track.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
US federal AI use case: RPA Cloud Connection
Agency: General Services Administration
Problem addressed: The AI is intended to modernize the Process Optimization and Automation (PO&A) architecture by integrating the RPA Test environment with a cloud-based AI environment. This aims to automate the extraction and processing of data from unstructured documents such as invoices, receipts, forms, and emails, which typically consume significant manual effort and time. The goal is to streamline document-based workflows and improve overall efficiency in operations.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Office of Civil Rights (OCR) Case Crawler
Agency: General Services Administration
Problem addressed: The AI is intended to solve the problem of manually searching through extensive PDF documents of past EEOC OFO case decisions and OCR final agency decisions. This manual process is time-consuming and inefficient, and the AI aims to automate and streamline the search for desired terms or case names within these documents.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: ChatOGC
Agency: General Services Administration
Problem addressed: The AI system aims to solve the problem of significant inefficiencies faced by attorneys within the Office of General Counsel (OGC) when searching for internal legal opinions and relevant GSA orders. These critical resources are currently scattered across various platforms, making them difficult and time-consuming to locate, leading to attorneys spending considerable time searching for precedents or contacting colleagues.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: No-Code Text and Sentiment Analysis with XM Discover
Agency: General Services Administration
Problem addressed: The AI in this use case is intended to solve the problem of efficiently conducting text and sentiment analysis on qualitative feedback in a no-code environment. This allows GSA to gain a more nuanced view of customer sentiment and connect text feedback to key survey metrics and operational data for more thorough insights, moving beyond simple text summarization. Ultimately, the AI helps GSA to improve its ability to make data-driven decisions regarding customer needs.
Topic area: Service Delivery
Development stage: Deployed
US federal AI use case: Solicitation Review Tool (SRT)
Agency: General Services Administration
Problem addressed: The AI is intended to solve the problem of efficiently identifying non-compliant Information and Communications Technology (ICT) solicitations within SAM.gov data. It aims to automate the initial screening process to flag solicitations that may lack necessary compliance language, which would otherwise require extensive manual review by agencies.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
US federal AI use case: Solicitation Review Tool - Section 508
Agency: General Services Administration
Problem addressed: The AI is intended to solve the problem of ensuring that federal solicitations for Information and Communication Technology (ICT) include adequate Section 508 compliance requirements to support accessibility in federal IT. It aims to automate the detection of ICT-related solicitations and then check for Section 508 compliance, improving the accessibility of government IT acquisitions.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Software Supply Chain Security Research
Agency: General Services Administration
Problem addressed: The AI is intended to solve the problem of time-consuming manual research into security vulnerabilities for third-party software as part of the Software Security Review process. Currently, a manual process handles a limited number of issues per day, and a backlog of over 200 issues exists, with each issue potentially taking hours of human effort for research.
Topic area: Cybersecurity
Development stage: Pre-deployment
US federal AI use case: Expedited Transfer of Program of Requirements into Test Fit Layouts and Concept Design Options
Agency: General Services Administration
Problem addressed: The AI is intended to solve the time-consuming process of generating initial test fit layouts and concept design options from program requirements, a task that traditionally takes weeks to months. By leveraging AI, the system aims to significantly expedite this process, allowing for preliminary designs to be created within 24 to 72 hours. This addresses the need for faster turnaround times in initial design conversations and concept visualization for potential clients.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: National Computerized Maintenance Management System (NCMMS) AI Chatbot
Agency: General Services Administration
Problem addressed: The AI is intended to solve the problem of providing user support within National Computerized Maintenance Management System (NCMMS) (IBM Maximo) for facility managers, contractors, and management teams, specifically by offering contextual support for the 20,000 work orders submitted daily.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: PBS AI Chatbot Domain Enhancements
Agency: General Services Administration
Problem addressed: The AI chatbot is intended to solve the problem of siloed information and time-consuming research across various Public Buildings Service (PBS) domains, including Portfolio Management, Facilities Management, Project Delivery, Acquisition, Client Strategy, Architecture & Engineering, and Strategy & Performance. By consolidating disparate data sources, policy documents, and internal guidance into a conversational, citation-rich assistant, the AI aims to streamline access to critical information and provide on-demand, data-driven insights for PBS personnel.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Leasing Desk Guide Bot
Agency: General Services Administration
Problem addressed: The AI, specifically the "Leasing Desk Guide Bot," is intended to solve the problem of GSA stakeholders needing to efficiently access accurate, consistent, and context-relevant information from the Leasing Desk Guide to perform their duties. By leveraging a RAG-based internal chatbot, the system aims to streamline the information retrieval process, reducing the time and effort traditionally required to find specific details within a large corpus of knowledge, thereby enhancing operational efficiency for GSA personnel.
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Portfolio Analysis LLM
Agency: General Services Administration
Problem addressed: The AI is intended to solve the problem of enabling strategic decision-making around the optimization of the PBS real estate portfolio. This involves aggregating multiple data sources to answer key strategic questions, such as how to increase occupancy levels or find opportunities to decrease the cost per occupant within the portfolio.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
US federal AI use case: Pricing Policy Chatbot
Agency: General Services Administration
Problem addressed: The AI, specifically the Pricing Policy Chatbot, is intended to solve the problem of efficiently providing factually accurate, context-relevant, and consistent information related to Pricing Policy to internal stakeholders. By training a RAG-based internal chatbot on a dedicated corpus of knowledge, the system aims to streamline the process of information retrieval and ensure that personnel have immediate access to the necessary guidance for their duties, thereby reducing potential inconsistencies and improving operational efficiency.
Topic area: Admin Functions
Development stage: Pilot
US federal AI use case: Tier 1 Use Cases
Agency: General Services Administration
Problem addressed: These use case addresses challenges such as lack of familiarity with AI tools, inefficient low-stakes tasks, uninformed AI tool acquisition, and unaddressed ethical implications by providing a framework for exploring, evaluating, and understanding AI capabilities with non-sensitive information.
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Design Your Facility
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Discover Financial Business Intelligence System (FBIS) Report Analysis (Sub-CAN Line Items)
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can spend plans be more real-time and less manual to create and keep up to date?

The data used to create and monitor spend plans are spread across multiple out-of-the-box reports available in FBIS. Combing through them is time-intensive, making it more difficult to keep spend plans up to date in real-time. This task is especially challenging due to the unstandardized nature of sub-CAN (Congressional Appropriation Number) line item descriptions.
Purpose and benefits: More efficient spend plan creation and monitoring.

This AI-enabled tool provides a resource for ACF budget managers to identify related line items across disparate reports in FBIS.
Topic area: Procurement and Finance Mgmt
Development stage: Pilot
First used: 2026-04-25
US federal AI use case: Grant Spend Health Analysis
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Structuring and Validating Completeness of Case Data Information
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can referrals from the Department of Homeland Security (DHS) be more quickly reviewed for critical pieces of information about their parents and the reason for separation?

Since November 2024, when a minor is separated from their parent or legal guardian, the U.S. Customs and Border Protection (CBP) within DHS is required to send certain pieces of information about the parent/legal guardian in accordance with the Ms L vs ICE settlement. CBP send this information in a block of free text and sometimes do not include the required information. Historically, ACF's Office of Refugee Resettlement (ORR)’s tracking of required information has been done manually and inconsistently.
Purpose and benefits: More easily searchable and accurate data on family separations

Quicker validation of CBP compliance in essential data sharing for separated families, enabling faster follow-up as needed to receive any missing data points
Topic area: Benefits Processing
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The use of AI is narrowly focused on extracting key data points from scanned notices. The outputs do not serve as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the 6 cases outlined in M-25-21, page 19.
First used: 2026-12-24
US federal AI use case: Structuring Notice of Concern Data
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can the Office of Refugee Resettlement (ORR) clear its backlog of notices of concern (NOC) and minimize backlog in the future?

Notice of Concern (NOC) forms contain critical information regarding safety of children who have left ORR's care. Some forms are received as scans, with the information not in machine-readable format. ORR receives hundreds of NOCs a day. Due to personnel shortage in the Prevention of Child Abuse and Neglect Team (PCAN) team responsible for reviewing and acting on NOCs, as of October 2024 there was a backlog of over 30,000 NOCs.
Purpose and benefits: More effective and efficient review of NOCs

With AI-enabled structuring of data in NOCs received in scanned formats, ORR can reduce the large backlog that has accumulated.
Topic area: Benefits Processing
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The use of AI is narrowly focused on extracting key data points from unstructured narratives and validating data completeness. The outputs do not serve as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the 6 cases outlined in M-25-21, page 19.
First used: 2026-12-24
US federal AI use case: Unaccompanied Children Program Policy & Procedure Research Tool
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can the Office of Refugee Resettlement (ORR) research the laws, standards, policies, and procedures applicable to monitoring visits more quickly while maintaining thoroughness?

The Office of Refugee Resettlement (ORR) conducts monitoring visits at least monthly to ensure that care providers meet minimum standards for the care and timely release of unaccompanied children, and that they abide by all Federal and State laws and regulations, licensing and accreditation standards, ORR policies and procedures, and child welfare standards. If ORR monitoring finds a care provider to be out of compliance with requirements, ORR issues corrective action findings and requires the care provider to resolve the issue within a specified time frame. Compliance determination involves research into the various laws, standards, policies, and procedures.
Purpose and benefits: Faster issuance of well-informed corrective action findings

The goal of the  UC Program Policy & Research Tool is to speed up this process as children's health and well-being may be impacted before a corrective action finding is issued and the issue is resolved. The UC Program Policy & Procedure Research Tool speeds up research of relevant laws, standards, policies, and procedures content curated and approved by ORR's policy team. This research is one part of the process that informs ORR's monitoring team's decisions on whether corrective actions are needed and if so, what corrective actions.
Topic area: Service Delivery
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: Unaccompanied Children Process Model Digital Twins
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Analyzing Public Comments on Proposed Rule
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Outreach List Segmentation
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Ask HR Policy
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF better support Executive Officers and Administrative Officers find relevant Human Resources (HR) policy?

HHS has over 70 HR policies that ACF Executive Officers/Administrative Officers must navigate when trying to find an answer to a question.
Purpose and benefits: Faster fact-finding on HHS's HR policies 

Rather than clicking through multiple policies to try to identify the ones with relevant information to their question, Executive Officers/Administrative Officers can ask a question in natural language.
Topic area: HR
Development stage: Deployed
First used: 2026-05-24
US federal AI use case: Child Welfare Information Automated Inquiry System

(Note: previously named "Child Welfare Information Gateway OneReach Application")
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can Child Welfare Information Hotline callers get the right information faster without increasing staffing?

The Children's Bureau runs the Children Welfare Information Gateway, a connection to trusted resources on the child welfare continuum. The Information Gateway has a hotline for answering questions or requesting information: https://www.childwelfare.gov/stay-connected/contact/. Callers to the hotline range from those having more routine questions (such as asking for the contact information for their state's child welfare agency) to reporting more complex, nuanced situations.
Purpose and benefits: Approximately a quarter of inquires to the Child Welfare Information Hotline are assisted by AI, freeing up time for staff to focus on more complex, nuanced cases. In the first 4 years, this amounts to ~2,500 inquiries assisted by AI.
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-03-20
US federal AI use case: Collective Bargaining Compass
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can the HHS-NTEU collective bargaining agreement be more easily referenced?

The HHS NTEU Collective Bargaining Agreement and associated rules are over 400 pages long, covering numerous topics related to employer-labor relations.
Purpose and benefits: Faster fact-finding on the HHS-NTEU collective bargaining agreement.

Rather than searching for relevant passages through keyword matching, people can ask their questions in natural language.
Topic area: HR
Development stage: Deployed
First used: 2026-02-24
US federal AI use case: Discover User Assistant
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF Discover users better understand how to use the platform?

ACF Discover is a relatively new platform designed to streamline various analyses and data maintenance responsibilities of ACF Executive Officers and other ACF administrative and management staff. When launched, ACF Discover users were trained with instructional documentation. However, some users still find it difficult to understand the different software modules and how to use them.
Purpose and benefits: Easier navigation of the ACF Discover Staff Management platform

Rather than searching for relevant passages through keyword matching, people can ask their questions in natural language.
Topic area: IT
Development stage: Deployed
First used: 2026-01-24
US federal AI use case: Policy Knowledge Base Data Migration
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Qualitative Analysis
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can thematic coding and trend analysis across qualitative data be done more efficiently?

ACF staff often conduct surveys and interviews, which generate qualitative data that needs to be analyzed for themes and trends. The standard approach involves multiple human passes of labeling the data for analysis, which is very time-intensive.
Purpose and benefits: Faster initial labeling of qualitative data that human reviewers are then able to correct and iterate from
Topic area: Other
Development stage: Deployed
First used: 2026-03-23
US federal AI use case: Intakes Referral Parser
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Training and Technical Assistance (TTA) GenAI Chatbot
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Funding Opportunity Redundancy Analysis
Agency: Department of Health and Human Services - HHS/ACF
Development stage: Retired
US federal AI use case: Unaccompanied Child Sponsor Identity Verification
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF strengthen sponsor identity verification to reduce fraudulent sponsor applications?

Throughout the process of sponsor vetting, there are various touchpoints where the identity of the sponsor is critical to ensuring a child will be placed with a safe guardian.  Knowing that the person (sponsor, household adult) is who they claim to be and that the person presenting at different points of the sponsor application process is consistently the person who was vetted is essential to ensure the welfare of a child.
Purpose and benefits: Increased certainty that the adults applying to sponsor/care for a child release from ORR care are who they claim to be, so that they may be properly vetted in providing a safe environment for children post-release.
Topic area: Benefits Processing
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Head Start Correspondence Categorizer
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can regional management and program specialists with large grant workloads keep track of all the correspondence being received by staff in their regional offices?

The Office of Head Start's (OHS) program specialists receive correspondence through the Head Start Enterprise System (HSES) for a variety of topics. Many of the requests, questions, and reports are tracked to completion in another system that has more robust alerting and workflow management capabilities. OHS is automating the data transfer between these two systems, introducing a data processing step that helps categorize correspondence.
Purpose and benefits: More efficient tracking of correspondence. 

The AI categorization of correspondence can help managers and program specialists more quickly identify correspondence that require more immediate action.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Builder Buddy
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF staff more readily develop their own tailored virtual assistants on our enterprise genAI platform?

ACF's enterprise generative AI platform gives users the tools to create their own tailored virtual "agents" to assist with more specialized tasks specific to a user's work. Users do not need to code to build these virtual assistants but do have to provide adequate context and instructions. Builder Buddy lets users draft a virtual assistant through describing their needs and providing context in a natural conversational manner as an alternative to a form-based builder interface.
Purpose and benefits: More ACF staff feel empowered and equipped to configure their own tailored virtual assistants, increasing the usefulness of LLMs beyond basic chat

Tailored virtual assistants allow ACF staff to leverage LLMs in repeated workflows, reducing administrative burden and allowing staff to focus on higher-order analysis.
Topic area: IT
Development stage: Deployed
First used: 2026-05-25
US federal AI use case: Document Review for Alignment with Executive Orders: Position Descriptions
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF identify position descriptions that may need to be adjusted for alignment with recent executive orders?

ACF needed to conduct an audit across position descriptions in accordance with HHS Secretarial Directives related to recent executive orders such as  Executive Order 14151 "Ending Radical and Wasteful Government DEI Programs and Preferencing" and Executive Order 14168 "Defending Women From Gender Ideology Extremism and Restoring Biological Truth to the Federal Government".
Purpose and benefits: Increased efficiency of review, with reduced administrative burden on staff

Staff were able to focus time more effectively by using AI to support the flagging of potential affected position descriptions.
Topic area: HR
Development stage: Deployed
First used: 2026-03-25
US federal AI use case: Document Review for Alignment with Executive Orders: Grant Materials
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF identify grants that may need to be reviewed for alignment with recent executive orders?

ACF needed to conduct an audit across existing grants and new grant applications in accordance with HHS Secretarial Directives related to recent executive orders such as  Executive Order 14151 "Ending Radical and Wasteful Government DEI Programs and Preferencing" and Executive Order 14168 "Defending Women From Gender Ideology Extremism and Restoring Biological Truth to the Federal Government".
Purpose and benefits: Increased efficiency of review, with reduced administrative burden on staff

To increase the efficiency of the executive order alignment review, ACF is leveraging an AI-based process that reviews application submission files and generates initial flags and priorities for discussion, which are then routed to ACF Program Office staff for final review, justification, and recommendation.
Topic area: Other
Development stage: Deployed
First used: 2026-03-25
US federal AI use case: Grant management support: structuring information in applications
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF staff more efficiently review grant applications that have non-standardized formats?

Grant applications for ACF-funded programs can come in different formats, even when a standard set of questions or template is provided. ACF staff evaluating applications look for explanation and details to assess against pre-established evaluation criteria. Going back-and-forth across an application to find the relevant explanation can be time intensive, especially for applications that include multiple documents spanning 50+ pages.
Purpose and benefits: Increased efficiency of review so that more time can be spent on grant application analysis and evaluation
Topic area: Other
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: Acquisition support: co-drafting acquisition packages
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF acquisition teams more efficiently draft acquisition packages? 

In addition to tailored performance work statements (PWS), acquisition packages include multiple documents that often require information based on the PWS. There are also instances where a recompete is issued that largely follows a previous contract, with some updates to volumes. Different contract awarding agencies have different formats. ACF's acquisition teams therefore commonly need to repackage information.
Purpose and benefits: Increased efficiency in preparing acquisition packages so that more time is spent on the substance of scoping contracts and less time on rote drafting
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2026-12-24
US federal AI use case: Acquisition support: assisting reviews and co-drafting technical evaluation documents
Agency: Department of Health and Human Services - HHS/ACF
Problem addressed: How can ACF teams more efficiently review contract proposals and summarize technical evaluation discussions?

In response to Requests for Information and Requests for Proposals, ACF teams receive responses from interested vendors. In the review process, ACF staff need to provide summarized comments for each response on potential suitability for delivering the work. These summaries are based on individual and group review against pre-established criteria. When there is a high volume of vendor responses, review teams have many summaries to write.
Purpose and benefits: Increased efficiency in drafting technical evaluation documents, so that more time is spent on review and analysis and less time is spent on "blank screen syndrome"
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: Sub Can Line Finder
Agency: Department of Health and Human Services - HHS/ACF/OA
Problem addressed: The LLM helps compile an aggregated report of costs by intaking a specific CAN, category, sub CAN line item description, and a projection cost from the user (either by a CSV or manual entry) and returning line items from the open-closed report and requisition purchase order report related to the sub CAN line item description such as supplier name, document number, document type, and total obligations for that sub CAN line item.
Purpose and benefits: The Sub CAN Line finder LLM helps find related obligations, line items from various reports and returns information crucial to a budget officer when creating a spend plan for their office. The LLM aids ACF Discover track various line items and pulls the information into one place: the Spend Plan module where ACF can track yearly budgets and monitor budget health.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-10-24
US federal AI use case: USPSTF Public Forms Data AI Integration
Agency: Department of Health and Human Services - HHS/AHRQ/CEPI
Development stage: Retired
US federal AI use case: AI DevOps - Improving Development and CI/CD Operations
Agency: Department of Health and Human Services - HHS/AHRQ/CFACT
Problem addressed: Introducing AI to DevOps can identify and reduce errors, shorten release cycles, and empower development teams with data-driven insights, resulting in faster continuous integration and shorter development lifecycles.
Purpose and benefits: Integrating AI into DevOps pipeline can boost efficiency, enhance code quality, and accelerate development cycle. AI code review uses artificial intelligence algorithms to analyze source code for potential issues. Initial integration of AI code review can assist in detecting bugs, security vulnerabilities, performance bottlenecks, and deviations from coding standards.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AHRQ Search
Agency: Department of Health and Human Services - HHS/AHRQ/CQuIPS
Problem addressed: Organization wide search that includes Relevancy Tailoring, Auto-generation Synonyms, Automated Suggestions,  Suggested Related Content, Auto Tagging, and "Did you mean?" to allow visitors to find specific content.

This AI use case enhances our agency's efficiency and user experience by optimizing search results, auto-completing queries, suggesting relevant searches and content tags, as well as proposing spelling corrections.
Purpose and benefits: This AI use case aims to optimize search results by adjusting their ranking, ensuring that the most pertinent information is displayed at the top. It also enhances search effectiveness by adding synonyms to queries behind the scenes. It further improves user experience by auto-completing queries as they are being typed, and showing related searches that might offer additional valuable insights. The system also proposes content tags automatically, leveraging machine learning to assess existing content tagging patterns. Additionally, it suggests spelling corrections and reformats search queries based on data from Google Analytics.
Topic area: IT
Development stage: Deployed
First used: 2026-09-19
US federal AI use case: Chatbot
Agency: Department of Health and Human Services - HHS/AHRQ/CQuIPS
Development stage: Retired
US federal AI use case: NLQuery- As Data or Pulse
Agency: Department of Health and Human Services - HHS/AHRQ/CQuIPS
Development stage: Retired
US federal AI use case: Quality and Safety Review System AI-enabled automated abstraction
Agency: Department of Health and Human Services - HHS/AHRQ/CQuIPS
Development stage: Retired
US federal AI use case: Enhancing Diversity in Peer Review - Pilot
Agency: Department of Health and Human Services - HHS/AHRQ/OEREP
Development stage: Retired
US federal AI use case: PRISM Ally
Agency: Department of Health and Human Services - HHS/ASFR/OA
Problem addressed: Assists users with federal regulatory and agency policy questions related to acquisition
Purpose and benefits: A.I. will help HHS deliver faster, higher-quality public services and measurably improve mission outcomes by cutting cycle times and backlogs, boosting accuracy, and increasing first-contact resolution and customer satisfaction. It will drive cost avoidance and productivity through automation and reuse of shared data/models and code, lowering unit costs per transaction while protecting taxpayer dollars. Built-in accessibility, interpretability, and human-in-the-loop safeguards strengthen equity, fairness, and public trust, with transparent citations, monitoring, and appeal mechanisms. The workforce benefits from targeted upskilling and copilots that reduce manual research and documentation, improving time-to-competency and decision quality. Data quality and interoperability improve via standardized metadata, provenance, and sharing—enabling secure, portable, and interoperable solutions that reduce vendor lock-in and long-term risk. Success will be tracked with concrete metrics such as cycle-time reduction, error-rate and rework decreases, customer experience score gains, cost-per-action savings, accessibility conformance, reuse/adoption counts, training completions, and compliance/incident rates.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2026-06-25
US federal AI use case: GrantSolutions Text Analyzer Tool
Agency: Department of Health and Human Services - HHS/ASFR/OG
Problem addressed: Program staff authoring NOFOs need capabilities to simplify language and ensure NOFOs remain compliant with the Plain Writing Act of 2010.
Purpose and benefits: Automates repetitive workflows, reduces manual effort, delivers actionable insights, and accelerates accurate, transparent decisions across the grant lifecycle.
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-07-23
US federal AI use case: GrantSolutions AI Writing Assistant
Agency: Department of Health and Human Services - HHS/ASFR/OG
Problem addressed: Program staff authoring NOFOs need capabilities to simplify language and ensure NOFOs remain compliant with the Plain Writing Act of 2010.
Purpose and benefits: Automates repetitive workflows, reduces manual effort, delivers actionable insights, and accelerates accurate, transparent decisions across the grant lifecycle.
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-05-25
US federal AI use case: GrantSolutions Recipient Risk Tool
Agency: Department of Health and Human Services - HHS/ASFR/OG
Problem addressed: Grant managers need an efficient method to conduct risk assessments before issuing financial assistance awards to prevent fraud, waste, and abuse.
Purpose and benefits: Automates repetitive workflows, reduces manual effort, delivers actionable insights, and accelerates accurate, transparent decisions across the grant lifecycle.
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-03-19
US federal AI use case: GrantSolutions Non?Competing Continuation Approval Tool
Agency: Department of Health and Human Services - HHS/ASFR/OG
Problem addressed: Grant managers need an efficient workflow to identify and analyze differences in non-competing continuation budgets and narratives from year to year.
Purpose and benefits: Automates repetitive workflows, reduces manual effort, delivers actionable insights, and accelerates accurate, transparent decisions across the grant lifecycle.
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-12-21
US federal AI use case: GrantSolutions Helpdesk Agent
Agency: Department of Health and Human Services - HHS/ASFR/OG
Problem addressed: GrantSolutions users need an efficient method to get answers to their login and access-related questions.
Purpose and benefits: Automates repetitive workflows, reduces manual effort, delivers actionable insights, and accelerates accurate, transparent decisions across the grant lifecycle.
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: GrantSolutions Non?Competing Continuation Review Tool
Agency: Department of Health and Human Services - HHS/ASFR/OG
Problem addressed: Grant managers need an efficient workflow to identify and analyze differences in non-competing continuation budgets and narratives from year to year and ensure narratives promote federal priorities and mission.
Purpose and benefits: Automates repetitive workflows, reduces manual effort, delivers actionable insights, and accelerates accurate, transparent decisions across the grant lifecycle.
Topic area: Service Delivery
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: Senior Leadership Briefing Generation
Agency: Department of Health and Human Services - HHS/ASPR/CP
Problem addressed: The manual process of parsing and understanding documents, and summarizing their content
Purpose and benefits: Saves the SLB team time manually typing out a briefing
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: AIP Cyber Incident Ingestion
Agency: Department of Health and Human Services - HHS/ASPR/CP
Problem addressed: The manual process of parsing out cyber incidents and entering them into the system
Purpose and benefits: Allows the Cyber team to quickly ingest cyber incident data
Topic area: Cybersecurity
Development stage: Pilot
First used: 2026-11-25
US federal AI use case: ASPR TRACIE - Web search results improvement (prototyping stage)
Agency: Department of Health and Human Services - HHS/ASPR/CP
Problem addressed: Web search results improvement - ASPR TRACIE
Purpose and benefits: Public users searching on the website can expect to see more relevant and improved search results.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: emPOWER AI
Agency: Department of Health and Human Services - HHS/ASPR/ODAIA
Problem addressed: Public health authorities, first responders and others across the emergency management spectrum indicated that they had to be able to more rapidly access HHS emPOWER Map publicly available data, particularly in unstable internet conditions, during disasters. emPOWER AI, an  Amazon Alexa Skill, was created to allow anyone with a smartphone, particularly emergency and first responders, to be able to request the data from the HHS emPOWER Map and receive it within seconds from the field to headquarters.
Purpose and benefits: Public health authorities, first responders and others across the emergency management spectrum indicated that they had to be able to more rapidly access HHS emPOWER Map publicly available data, particularly in unstable internet conditions, during disasters. emPOWER AI, an  Amazon Alexa Skill, was created to allow anyone with a smartphone, particularly emergency and first responders, to be able to request the data from the HHS emPOWER Map and receive it within seconds from the field to headquarters. For example, a local first responder in the field at a location of a disaster could rapidly identify the total number of Medicare beneficiaries that live independently in a given zip code, and may be adversely impacted by a rapidly progressing flood or wildfire emergency and use this information to inform decision-making on evacuation assistance resources and teams.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-12-19
US federal AI use case: Certification and Testing/Program Administration – AI-enabled Internal Processes
Agency: Department of Health and Human Services - HHS/ASTP
Problem addressed: ONC’s certification and related program operations rely on many manual, text-heavy, and repetitive tasks (e.g., analyzing surveillance reports, validating certification test results, generating public meeting materials, preparing communications, managing Jira tickets, summarizing standards/IG guidance, and drafting acquisition documents). These activities are time-consuming, error-prone, and difficult to scale as program workload increases. The AI use case is intended to automate or semi-automate routine document drafting, data summarization, basic analysis, and information retrieval across these functions so staff can focus on higher-value review, oversight, and decision-making.
Purpose and benefits: Expected benefits include: (1) improved efficiency of internal certification and program operations (e.g., faster preparation of surveillance analyses, CHPL artifacts, and meeting materials); (2) reduced risk of omissions and inconsistencies in internal documents through standardized AI-assisted drafting and terminology checks; (3) quicker access to relevant information from CHPL data, Jira tickets, standards implementation guides, and financial spreadsheets; and (4) more timely, clear, and consistent public-facing communications and policy/support documents. Indirectly, these improvements support ONC’s mission to advance safe, interoperable health IT by improving the quality and timeliness of its certification, oversight, and communication activities.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: The NEF DSI/AI project addresses local validation of AI-based clinical decision support (CDS)/decision support interventions (DSI) in provider settings.
Agency: Department of Health and Human Services - HHS/ASTP
Problem addressed: Assess quality of AI-based clinical decision support (CDS) tools
Purpose and benefits: [LAVA, or "Local AI Evaluator",  does not itself use AI.] LAVA would assist clinicians in assessing the accuracy, and therefore usefulness, of AI diagnosis tools. AI diagnosis tools are developed to apply to a national population, rather than to smaller, local populations, such as those served by small providers with one or few physical locations. These smaller, local patient populations may have different demographics than that on which the AI-based tool was trained, so the LAVA tool can help illuminate these differences and how the AI tool may apply to the local population. This can help providers learn how to best use their AI diagnosis tools.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: HaMLET: Harnessing Machine Learning to Eliminate Tuberculosis
Agency: Department of Health and Human Services - HHS/CDC
Development stage: Retired
US federal AI use case: Adverse Childhood Experiences (ACEs) Literature Review Dashboard
Agency: Department of Health and Human Services - HHS/CDC
Development stage: Retired
US federal AI use case: Automated Analysis of Injury Control Research Center (ICRC) Annual Progress Reports (APRs) using Large Language Models
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The AI is designed to streamline the review process of Annual Progress Reports (APRs) submitted by Injury Control Research Centers (ICRCs), improve efficiency, and support the evaluation of the performance and progress of ICRC-funded activities.
Purpose and benefits: The AI will help quickly and efficiently identify key challenges and insights from ICRC APRs, enabling more effective decision-making in the review process. By automating the extraction and analysis of critical information, the AI allows the ICRC team to focus on higher-level evaluation and strategic planning. This will reduce the time and resources needed for manual review, improve the consistency and accuracy of assessments, and facilitate faster responses to ICRC needs. Ultimately, this will support ICRCs in overcoming challenges and achieving their research and injury control goals, benefiting the public health system as a whole.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-08-23
US federal AI use case: Detecting Stimulant and Opioid Misuse and Illicit Use
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To detect and analyze non-therapeutic (illicit or misuse) stimulant and opioid use from free-text clinical notes in EHRs, which is not possible using standard medical codes.
Purpose and benefits: The AI models enable the extraction of novel insights from EHRs regarding non-therapeutic drug use, improving the statistical analysis of health data for the National Hospital Care Survey (NHCS). This supports more accurate public health statistics and may influence analysis of other datasets with EHR clinical notes.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-03-24
US federal AI use case: DHP Virtual Assistant
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To assist HIV researchers by retrieving relevant information related to HIV and HIV research, improving productivity and efficiency in literature review.
Purpose and benefits: Expected to increase productivity among HIV researchers by streamlining information retrieval for HIV research.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-11-24
US federal AI use case: Fuzzy matching tool
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To improve clearance and review management of CDC publications by identifying similar records between eClearance submissions and CDC-authored publications, ensuring compliance with NIHMS and CDC public access policies, and supporting science prioritization and impact analyses.
Purpose and benefits: The tool is expected to streamline the clearance process, ensure compliance with public access policies, and assist in identifying and prioritizing CDC-authored publications. This will save staff time and improve the efficiency and accuracy of publication management.
Topic area: Admin Functions
Development stage: Pilot
First used: 2026-03-23