GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: FRB AI Code Translation POC
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Code Assistance - Generating, debugging, and documenting code
Purpose and benefits: This POC will explore the remediation of legacy code by leveraging generative AI to accelerate migration to a more modern framework. The purpose of this POC is simply to evaluate the efficacy of gen AI for this purpose and any code generated would not be moved to production. AI-driven code translation accelerates the conversion of code from one language or framework to another, including updating legacy systems to modern platforms. The use of generative AI in technical debt remediation offers substantial operational efficiencies, cost savings, and strategic benefits, quantifiable through reduced labor hours, lower migration costs, and faster time-to-market for modernized applications. These advantages position teams to better leverage modern technologies and align with the strategic goals of agility and innovation.
Topic area: IT
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2024-09-26
US federal AI use case: CoCounsel tool used in the legal office
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Contextual Research (Exploring policies, regulations, trends, and strategic topics. Generating first drafts of documents, briefing, or communication materials using AI. (Content Generation & Refinement - Drafting, rewriting, and improving documents, emails, presentations, and plans). Knowledge Retrieval (Locating known information and presenting or analyzing the information)
Purpose and benefits: The legal office would like to use AI assistant, which will be used by a paralegal in the legal office.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
US federal AI use case: Knowledge Retrieval Chatbot POC
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Contextual Research (Exploring policies, regulations, trends, and strategic topics). Knowledge Retrieval (Locating known information and presenting or analyzing the information).
Purpose and benefits: The customer would like to test the use of a chatbot that could access all of their relocation policies (FTR, DSSR, FAM, JTR, ARC Relocation Guide, etc.) in an effort to aid in the amount of time they are searching for stuff in the regulations.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2024-11-22
US federal AI use case: Open source Python library and models for NLP
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Text analytics- process, analyze, and understand text
Purpose and benefits: DNP program needs to add a technology request to GEM for an open-source Python library and models from vendor.
Topic area: IT
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2024-11-26
US federal AI use case: AI-assisted Procurement POC
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Contextual Research (Exploring policies, regulations, trends, and strategic topics). Knowledge Retrieval (Locating known information and presenting or analyzing the information).
Purpose and benefits: Gathering data for a Prism project.  Expected outcomes is to verify the AI Tool is a useful aid in helping Procurement employees enhance identifying adding the correct FAR clauses needed for a procurement action.
Topic area: Procurement and Finance Mgmt
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2025-05-05
US federal AI use case: FORMS AI
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Data Analysis and Manipulation
Purpose and benefits: DDM-Debt is looking to utilize Machine Learning/GenAI solution to production in their FORMS system. The ML/GenAI solution automates the classification and routing of documents to the appropriate business units and roles.
Topic area: IT
Development stage: Deployed
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2025-02-11
US federal AI use case: Pilot a suite of Fiscal Service Gen AI solutions
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Data Analysis and Manipulation
Purpose and benefits: The Data Strategy Division is seeking to assess performance of AI alternatives to address business needs by doing comparative performance analysis with rigorous ROI testing using scientific testing frameworks (e.g., A/B, pre-post) to help direct decision-making around Artificial Intelligence (AI) adoption.
Topic area: IT
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2025-03-03
US federal AI use case: Application to AI services integration POC
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: AI Enablers (Enterprise solutions that enable AI to integrate into IT services infrastructures)
Purpose and benefits: Identify a method to connect COTS product to AI service. The proposed initiative aims to develop an enterprise API endpoint for AI services, enabling seamless integration of custom applications and Commercial-Off-The-Shelf (COTS) products.
Topic area: Service Delivery
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2025-07-08
US federal AI use case: AI Application and Agent Management POC
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: AI Enablers (Enterprise solutions that enable AI to integrate into IT services infrastructures)
Purpose and benefits: AI system is a comprehensive platform designed to help developers and IT administrators design, customize, and manage AI applications and agents. It provides a unified interface, SDK, and APIs to integrate data.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
US federal AI use case: Research / Discovery on GenAI Product for IT
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: AI Enablers (Enterprise solutions that enable AI to integrate into IT services infrastructures)
Purpose and benefits: Conducting initial research/discovery on AI product
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
US federal AI use case: Research / Discovery on GenAI Product for HR
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: AI Enablers (Enterprise solutions that enable AI to integrate into IT services infrastructures)
Purpose and benefits: Conducting initial research/discovery on AI product
Topic area: HR
Development stage: Pre-deployment
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
US federal AI use case: Local LLMs Evaluation
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: AI Enablers (Enterprise solutions that enable AI to integrate into IT services infrastructures)
Purpose and benefits: An open-source tool that allows you to run large language models (LLMs) locally on your own computer. Can be used to run and experiment with various LLM's locally and gain greater control over data .
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2025-08-05
US federal AI use case: GenAI Productivity Tool with Web Grounding Pilot
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: Contextual Research (Exploring policies, regulations, trends, and strategic topics). Knowledge Retrieval (Locating known information and presenting or analyzing the information).
Purpose and benefits: Conduct a web grounding-enabled pilot.  By default, web grounding is off in government environments; enabling will require an AI risk assessment. Without web grounding, AI product can only provide info through the summer of 2024.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2025-08-26
US federal AI use case: Establish localized Large Language Model (LLM)
Agency: Department of the Treasury - Bureau of the Fiscal Service (BFS)
Problem addressed: AI Enablers (Enterprise solutions that enable AI to integrate into IT services infrastructures)
Purpose and benefits: Consolidation of multiple research initiatives into an aligned approach to research an enterprise LLM that will be trained on approved internal documentation to provide accurate, consistent, and rapid responses to employee inquiries. This will improve service delivery, reduce case escalations, and free personnel to focus on complex, mission-critical work
Topic area: IT
Development stage: Pilot
Impact assessment: The tool, once deployed, is only for productivity and does not impact any of the defined high-impact areas
First used: 2025-09-10
US federal AI use case: Meeting Minutes Summary Tool
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: Manually creating meeting minutes and summaries is time-consuming and may result in inconsistent documentation of key discussion points and action items.
Purpose and benefits: Automated meeting summaries save staff time, ensure consistent documentation, and allow participants to focus on discussion rather than note-taking. Improves knowledge retention and follow-up on action items.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Customer Response
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: Manual processing and analysis is resource-intensive and may lack the speed and consistency needed for effective operations.
Purpose and benefits: Faster content creation, improved consistency, reduced staff time on routine writing tasks, and enhanced quality of generated content.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-02-01
US federal AI use case: Chatbots for internal research
Agency: Small Business Administration - Advocacy: Office of Advocacy
Problem addressed: OFO agents need quick access to relevant policy documents and guidance materials to efficiently respond to inquiries without manually searching through knowledge repositories.
Purpose and benefits: Faster response times for agents, more consistent guidance provided to clients, reduced time searching for information, and improved quality of service delivery.
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Office of Field Operations AI  -  Chat GTP, Bing CoPilot, or Google Gemini
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: Manual processing and analysis is resource-intensive and may lack the speed and consistency needed for effective operations.
Purpose and benefits: Faster content creation, improved consistency, reduced staff time on routine writing tasks, and enhanced quality of generated content.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Generative AI for SBA.gov product training
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Staff need quick access to relevant information and guidance to efficiently respond to inquiries and make decisions.
Purpose and benefits: Faster content creation, improved consistency, reduced staff time on routine writing tasks, and enhanced quality of generated content.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: AI for Small Business Development w/Copilot, Gemini or ChatGPT
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: SBA program managers need data-driven insights to identify at-risk 8(a) participants and predict growth trajectories to provide timely interventions and support.
Purpose and benefits: Predictive analytics enable proactive intervention for at-risk participants, optimize resource allocation for business development specialists, and improve success rates for 8(a) program participants.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: ChatGPT for OMS Marketing
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: Staff need quick access to relevant information and guidance to efficiently respond to inquiries and make decisions.
Purpose and benefits: Data-driven predictions, identification of patterns not visible through manual review, improved decision-making accuracy, and optimized resource allocation.
Topic area: Service Delivery
Development stage: Retired
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Gemini AI for Strategic Planning
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: Manual processing and analysis is resource-intensive and may lack the speed and consistency needed for effective operations.
Purpose and benefits: Increased operational efficiency, reduced manual processing time, improved consistency and accuracy, and enhanced mission delivery.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: GovCon Match
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Identifying trends and predicting outcomes requires analysis of complex data patterns that are difficult to assess manually.
Purpose and benefits: Data-driven predictions, identification of patterns not visible through manual review, improved decision-making accuracy, and optimized resource allocation.
Topic area: Service Delivery
Development stage: Retired
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Business Plan Creation
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: Manual processing and analysis is resource-intensive and may lack the speed and consistency needed for effective operations.
Purpose and benefits: Increased operational efficiency, reduced manual processing time, improved consistency and accuracy, and enhanced mission delivery.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Business Development
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: SBA program managers need data-driven insights to identify at-risk 8(a) participants and predict growth trajectories to provide timely interventions and support.
Purpose and benefits: Predictive analytics enable proactive intervention for at-risk participants, optimize resource allocation for business development specialists, and improve success rates for 8(a) program participants.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Employee Center AI Search
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Manual processing and analysis is resource-intensive and may lack the speed and consistency needed for effective operations.
Purpose and benefits: Data-driven predictions, identification of patterns not visible through manual review, improved decision-making accuracy, and optimized resource allocation.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Security Team Productivity Enhancement
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Manual processing and analysis is resource-intensive and may lack the speed and consistency needed for effective operations.
Purpose and benefits: Faster content creation, improved consistency, reduced staff time on routine writing tasks, and enhanced quality of generated content.
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: General AI Use Case across the SBA for web and desktop
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Staff need quick access to current, accurate information with verifiable sources for research, analysis, and decision-making. Traditional search requires manual verification and synthesis across multiple sources.
Purpose and benefits: Faster research and information gathering with built-in source citations, real-time information access, reduced time spent verifying sources, and improved quality of research outputs. Enables more informed decision-making with transparent sourcing.
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-02-06
US federal AI use case: Email, Document, and Composing AI Editor
Agency: Small Business Administration - OCPL: Office of Communications & Public Liaison
Problem addressed: Government communications require professional, clear, and error-free writing across diverse documents and correspondence. Manual proofreading is time-consuming and may miss subtle errors or tone inconsistencies.
Purpose and benefits: Improved quality and consistency of written communications, reduced time spent on editing and proofreading, enhanced professional tone in government correspondence, and increased confidence in written outputs. Supports staff at all writing skill levels.
Topic area: Service Delivery
Development stage: Pilot
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-15
US federal AI use case: Secure IL5 AI
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Government teams need secure AI capabilities that meet federal security requirements for analyzing sensitive data, generating content, and streamlining knowledge work without compromising data security or compliance.
Purpose and benefits: FedRAMP High authorized AI platform enables secure AI adoption across government agencies, enhances productivity through automated analysis and content generation, maintains data security and compliance, and provides flexible deployment options across classification levels.
Topic area: Service Delivery
Development stage: Retired
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2025-06-26
US federal AI use case: Employee Work Prioritization AI Agent
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Staff require versatile AI assistance for diverse tasks including content creation, coding, translation, research, and problem-solving. Single-purpose tools create inefficiencies and context-switching.
Purpose and benefits: Unified AI assistant handles multiple task types, improves productivity across content creation and coding activities, enables faster multilingual communication, provides accurate answers to complex questions, and supports creative problem-solving with contextual awareness.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Office of Advocacy AI System-Prompt Suite
Agency: Small Business Administration - Advocacy: Office of Advocacy
Problem addressed: Federal staff spend significant time on repetitive text-processing tasks such as rewriting, summarizing, drafting replies, checking grammar, and fact-checking. Traditional approaches require manual effort and lack consistency across analytic workflows. Unstructured AI outputs create ambiguity and are difficult to integrate into automated processes.
Purpose and benefits: Significantly increases frequency and quality of analytic work by reducing friction in text processing, improving consistency through structured JSON outputs, enabling automation at scale, making outputs audit-ready and transparent, and supporting reuse across government agencies. Published prompts advance Executive Order 14179 and OMB M-25-21 goals for responsible AI adoption and cross-government innovation.
Topic area: IT
Development stage: Pilot
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2025-12-10
US federal AI use case: Audit Green Book Classification
Agency: Small Business Administration - OCFO: Office of the Chief Financial Officer
Problem addressed: Manual classification and tagging of audits and recommendations according to OMB Green Book taxonomy is labor-intensive and prone to inconsistency.
Purpose and benefits: Consistent classification improves audit tracking, enables better trend analysis, streamlines reporting to OMB and OIG, and reduces staff time spent on categorization.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: SBA.gov External Customer Chatbot
Agency: Small Business Administration - OCPL: Office of Communications & Public Liaison
Problem addressed: Customer service teams are overwhelmed with routine inquiries that slow response times and prevent agents from focusing on complex, high-value customer issues. Manual case classification and routing is time-consuming and inconsistent. Customers expect 24/7 support and instant, personalized responses based on their account history.
Purpose and benefits: Automates 30-40% of routine customer service interactions, reducing agent workload and response times by up to 50%. Provides 24/7 instant customer support with personalized responses grounded in CRM data. Enables intelligent case classification and routing to appropriate agents. Allows human agents to focus on complex and emotional customer issues. Improves customer satisfaction through faster resolutions and consistent service quality. Scales customer service capacity without linear growth in headcount.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-10
US federal AI use case: Predictive AIOps
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: IT operations teams are overwhelmed with massive volumes of alerts and events from diverse monitoring tools, creating alert fatigue and slowing incident response. Manual correlation of events, log analysis, and root cause identification is time-consuming and error-prone. Reactive problem-solving leads to prolonged outages, high Mean Time to Resolution (MTTR), and degraded service availability. Traditional static thresholds fail to adapt to dynamic IT environments.
Purpose and benefits: Reduces alert noise by up to 90% through intelligent event correlation and anomaly detection. Decreases Mean Time to Resolution (MTTR) by 30-50% via automated root cause analysis and remediation workflows. Enables proactive incident prevention through predictive analytics that identify potential issues before they cause outages. Provides self-healing capabilities with automated remediation reducing manual intervention. Improves service availability and operational efficiency. Shifts IT operations from reactive firefighting to proactive problem prevention. Centralizes insights and actions on single platform, breaking down data silos.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2025-12-12
US federal AI use case: Data Analysis and Visualization AI
Agency: Small Business Administration - OCFO: Office of the Chief Financial Officer
Problem addressed: Data analysts spend significant time on repetitive tasks like data cleaning, semantic modeling, calculation creation, and generating data narratives. Non-technical business users struggle to explore data and create visualizations without extensive training. Translating complex datasets into actionable insights is time-consuming. Creating comprehensive documentation for data assets requires manual effort. Identifying trends, anomalies, and meaningful patterns in large datasets is labor-intensive.
Purpose and benefits: Accelerates data analysis with natural language queries that instantly generate visualizations and calculations. Reduces time spent on data preparation through AI-assisted cleaning and mapping automation. Simplifies complex analysis by converting natural language prompts into accurate calculations and visualizations. Democratizes data access for non-technical users through conversational data exploration. Automates data documentation generation for data sources, workbooks, and tables. Provides proactive context-aware insights by identifying patterns and anomalies. Generates natural language data stories that explain visualizations. Enhances decision-making speed and quality with AI-powered insights. Built on Einstein Trust Layer ensuring security, governance, and data privacy.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-11
US federal AI use case: Developer Code Assistant AI
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Software developers spend significant time on repetitive coding tasks, writing boilerplate code, creating documentation, generating unit tests, and conducting code reviews. Legacy code modernization is complex and time-consuming. Understanding large codebases requires extensive manual analysis. Developers need context-aware assistance that understands company-specific code patterns and AWS best practices.
Purpose and benefits: Accelerates software development by up to 57% through intelligent code completion and generation. Reduces time spent on documentation, unit testing, and code reviews through autonomous agentic capabilities. Transforms legacy applications automatically (e.g., .NET upgrades, Java version migrations). Improves code quality with AWS-specific best practices and security recommendations. Enables faster onboarding to complex codebases. Customizes to private repositories for company-specific relevance. Supports inline chat for contextual help. Provides CLI natural language translation for command-line efficiency.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Developer Code Assistant AI
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Developers spend substantial time writing repetitive boilerplate code, looking up syntax and APIs, translating requirements into code, and debugging errors. Understanding unfamiliar codebases is time-consuming. Manual code reviews are labor-intensive. Command-line operations require remembering complex syntax. Context-switching between documentation and coding disrupts flow.
Purpose and benefits: Increases developer productivity by 55-94% according to customer studies (Grupo Boticário case). Accelerates coding with real-time intelligent suggestions that understand context. Reduces time searching for documentation and syntax. Enables autonomous coding through GitHub issue assignment to AI agents. Provides instant code explanations and error detection. Improves code quality through AI-powered refactoring suggestions. Streamlines terminal workflows with natural language CLI commands. Maintains flow state by reducing context-switching. Supports multiple programming languages and frameworks.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: AI Incident Summarization
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Security analysts spend 15–30 minutes manually reading hundreds of work notes, logs, and audit trails to understand the status and history of each security incident, slowing response and shift handoffs.
Purpose and benefits: One-click incident summarization reduces analyst documentation time by 15–30 minutes per incident, improves shift handoff quality, and enables faster incident response through structured summaries.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2025-12-12
US federal AI use case: AI Resolution Notes Generation
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Formal closing notes for security incidents take analysts 15–30 minutes to manually draft at incident closure, diverting time from active threat response and creating documentation backlogs.
Purpose and benefits: Automated drafting of formal resolution notes allows analysts to review and approve rather than write from scratch, accelerating incident closure and freeing analyst time for active defense.
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2025-12-12
US federal AI use case: AI Vulnerability Ticket Deduplication
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Multiple security scanners (Tenable, Qualys, Rapid7) report the same vulnerability on the same host, generating duplicate tickets that create 'ticket bloat' and waste remediation team effort on redundant work.
Purpose and benefits: Reduced vulnerability ticket volume, faster remediation prioritization, and elimination of redundant vulnerability response effort across multiple scanning tools.
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2025-12-12
US federal AI use case: AI Vulnerability Remediation Assistance
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Security teams require environment-specific remediation guidance to determine the optimal patch or configuration change for each identified vulnerability, a process that is currently manual and time-intensive.
Purpose and benefits: Faster and more accurate remediation recommendations tailored to SBA’s specific environment configurations, reducing mean time to remediate and improving patch accuracy.
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2025-09-16
US federal AI use case: Internal OFO Chatbot Recommender
Agency: Small Business Administration - OFO: Office of Field Operations
Problem addressed: OFO agents need quick access to relevant policy documents and guidance materials to efficiently respond to inquiries without manually searching through knowledge repositories.
Purpose and benefits: Faster response times for agents, more consistent guidance provided to clients, reduced time searching for information, and improved quality of service delivery.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Multi-Agent Orchestration
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: SBA maintains millions of lines of legacy ColdFusion and COBOL code supporting mission-critical lending systems (CAFS) with minimal test coverage and significant technical debt. Manual code refactoring, test case generation, and project management across modernization initiatives are resource-intensive and error-prone, creating risk during cloud migration.
Purpose and benefits: Multi-agent AI orchestration will accelerate legacy code modernization by automating code refactoring recommendations, generating comprehensive test suites for financial calculation modules, and streamlining project management workflows. Expected benefits include reduced manual effort for QA testing, improved code quality and test coverage for CAFS applications, faster time-to-migration for AWS EVS initiatives, and better coordination across modernization workstreams.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: AI-Powered Search and Recovery
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: During cloud migration (AWS EVS) and disaster recovery operations, locating specific data across backup snapshots, identifying clean recovery points, and performing forensic analysis of backup data requires significant manual effort and specialized knowledge of backup infrastructure.
Purpose and benefits: AI-powered conversational search across enterprise backup data enables faster data discovery during recovery operations, reduces time-to-recovery during cyber incidents, and supports forensic investigation by allowing natural language queries against unstructured backup data. SaaS deployment model reduces operational overhead for backup infrastructure management during AWS EVS migration.
Topic area: Cybersecurity
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: ODRR Comm - Talking Point Generation
Agency: Small Business Administration - ODRR: Office of Disaster Recovery And Resilience
Problem addressed: Creating tailored talking points for public and internal discussions requires manual compilation of statistics and program data across multiple sources.
Purpose and benefits: Faster preparation for public engagements, data-backed messaging, consistent communication across the agency, and more effective stakeholder engagement.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: 7(a) Purchases ML Model
Agency: Small Business Administration - OCA: Office of Capital Access
Problem addressed: Manual review of all 7(a) loan purchase applications is resource-intensive, but OCA needs to maintain quality control and minimize PIIA error rates.
Purpose and benefits: Reduced manual review burden while maintaining or improving quality, faster processing times for legitimate applications, and optimized use of limited review staff resources.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Amazon Q in Connect
Agency: Small Business Administration - OCIO: Office of the Chief Information Officer
Problem addressed: Staff need quick access to relevant information and guidance to efficiently respond to inquiries and make decisions.
Purpose and benefits: Faster content creation, improved consistency, reduced staff time on routine writing tasks, and enhanced quality of generated content.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
First used: 2026-01-01
US federal AI use case: Comment Letter Review and Analysis
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Development stage: Retired
US federal AI use case: EDGAR Filing Certification Review Solution
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Development stage: Retired
US federal AI use case: Evaluating Cybersecurity Alerts
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Problem addressed: Explore different mechanisms leveraging AI to inform and expedite cybersecurity processes.
Purpose and benefits: Enhances identification of anomalous cybersecurity alerts for review. Increases the effectiveness and efficiency of limited cybersecurity resources.
Topic area: Cybersecurity
Development stage: Pilot
First used: 2025-08-01
US federal AI use case: EDGAR Filing Audit Report Solution
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Development stage: Retired
US federal AI use case: EDGAR Technical Support Public Chatbot
Agency: Securities and Exchange Commission - EDGAR Business Office (EBO)
Development stage: Retired
US federal AI use case: EDGAR Filing Signature Review Solution
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Development stage: Retired
US federal AI use case: EDGAR Filing Risk Disclosure Review Solution
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Development stage: Retired
US federal AI use case: Testing Developer Workflows
Agency: Securities and Exchange Commission - Strategic Hub for Innocation and Financial Technology (FinHub)
Development stage: Retired
US federal AI use case: Name Matching
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: Name Matching - Identifies certain similarities in names across data sources. This allows staff to more easily compare information across data sources.
Purpose and benefits: Identifies certain similarities in names across data sources. This allows staff to more easily compare information across data sources.
Topic area: Other
Development stage: Deployed
First used: 2018-02-01
US federal AI use case: Forms Filed
Agency: Securities and Exchange Commission - Division of Investment Management (IM)
Development stage: Retired
US federal AI use case: Parsing of Plain Language Descriptions to Machine-Readable Notations
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: Automates conversion of plain-language text into machine-readable standardized notations. This permits ease of analysis
Purpose and benefits: Automates conversion of plain-language text into machine-readable standardized notations.  This permits ease of analysis.
Topic area: Other
Development stage: Deployed
First used: 2022-02-01
US federal AI use case: Identification of Potentially Manipulative Trading Activity in Registrant Data
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: Identifies potentially manipulative trading activity for further review by an examination team.
Purpose and benefits: Identifies potentially manipulative trading activity for further review by an examination team.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Testing of Various AI Algorithms and Models
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Development stage: Retired
US federal AI use case: Using Natural Language Processing Techniques to Search and Analyze Certain Filings
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: Helps search and analyze a corpus of unstructured filings. Staff may consider the results of these efforts in their examination efforts.
Purpose and benefits: Helps search and analyze a corpus of unstructured filings. Staff may consider the results of these efforts in their examination efforts.
Topic area: Other
Development stage: Deployed
First used: 2020-04-01
US federal AI use case: Using Machine Learning/Artificial Intelligence Techniques to Predict Entities With Certain Risk Characteristics
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: Analyze data to predict entities with certain risk characteristics. Staff may consider the results of these efforts in their examination efforts.
Purpose and benefits: Analyze data to predict entities with certain risk characteristics. Staff may consider the results of these efforts in their examination efforts.
Topic area: Other
Development stage: Deployed
First used: 2015-04-01
US federal AI use case: Developed Parsing Logic for Filings
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS), Division of Economic and Risk Assessment (DERA), Office of the Chief Data Officer (OCDO)
Problem addressed: Help staff more efficiently search and analyze disclosure text contained in various filings.
Purpose and benefits: Help staff more efficiently search and analyze disclosure text contained in various filings.
Topic area: Other
Development stage: Deployed
First used: 2024-07-01
US federal AI use case: Identification of Potentially Manipulative Activity in Certain Accounts
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: Identifies accounts with certain activity of interest for further review by an examination team
Purpose and benefits: Identifies accounts with certain activity of interest for further review by an examination team.
Topic area: Other
Development stage: Deployed
First used: 2024-05-01
US federal AI use case: Using AI to Help with Coding-Related Tasks/Questions
Agency: Securities and Exchange Commission - Division of Enforcement (ENF); Division of Economic and Risk Analysis (DERA)
Development stage: Retired
US federal AI use case: Tailored Shareholder Report (TSR)
Agency: Securities and Exchange Commission - Division of Investment Management (IM), Disclosure Review and Accounting Office (DRAO)
Development stage: Retired
US federal AI use case: Optical Character Recognition (OCR) for Records Analysis
Agency: Securities and Exchange Commission - Division of Enforcement (ENF)
Problem addressed: Extract structured data from unstructured digital image files of checks.
Purpose and benefits: Assists ENF staff in extracting relevant information from digital image files. This will accelerate associated ENF investigations.
Topic area: Other
Development stage: Deployed
First used: 2022-05-01
US federal AI use case: Single Event Insider Trading Analysis
Agency: Securities and Exchange Commission - Division of Enforcement (ENF)
Problem addressed: Assist ENF staff in identifying accounts with trading activity in advance of material equity price moves that warrant further investigation by the ENF staff.
Purpose and benefits: Assist ENF staff in identifying accounts with trading activity in advance of material equity price moves that warrant further investigation by the ENF staff.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2018-04-01
US federal AI use case: Utilize Westlaw Quick Check to cross check SEC documents.
Agency: Securities and Exchange Commission - Division of Enforcement (ENF)
Problem addressed: Utilize existing Westlaw product to cross check SEC documents.
Purpose and benefits: Review SEC documents for additional case information.
Topic area: Other
Development stage: Deployed
First used: 2025-01-01
US federal AI use case: Augmenting cyber threat alerts on the ACES platform with ACES's specific information.
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Problem addressed: The current Cyberthreat alerts on the ACES platform are difficult to understand and we often see false positives relative to new use cases.
Purpose and benefits: The current Cyberthreat alerts on the ACES platform are difficult to understand and we often see false positives relative to new use cases. 

We would like to run the alerts through a RAG-enabled AI model to:
• Add SEC specific guidance and context to the emails based on a curated knowledge base.
• Provide more guidance on how to respond to the alert.
• Make the notification email easier to read and understand.
Topic area: Cybersecurity
Development stage: Pilot