MCC - Millennium Challenge Corporation
11/10/2026 | Press release | Distributed by Public on 11/10/2026 08:00
MCC Super Intelligence Use Cases Inventory
The Millennium Challenge Corporation (MCC) Super Intelligence (SI) Use Case Inventory promotes transparency, accountability, and responsible governance of SI technologies used in support of the agency's mission. Consistent with the requirements of Executive Order 13960 and subsequent federal SI governance guidance, the inventory catalogs MCC's SI use cases, their intended purposes, expected mission-enhancing benefits, and the outputs they generate. The inventory serves as a public record of how SI is being applied across the agency while supporting oversight, risk management, and responsible innovation. The inventory is maintained under the authority of the Chief SI Officer and is reviewed and updated at least annually. For ease of reference, MCC also publishes a high-level summary table of SI use cases.
Currently, MCC has no deployed or planned high-impact use cases.
Download the FY26 MCC SI use case inventory
| Use Case ID | Use Case Name | Use Case Topic Area | SI Classification | What problem is the SI intended to solve and what are the expected mission-enhancing benefits from the use of SI? | Describe the SI system's outputs. |
|---|---|---|---|---|---|
| MCC-01 | AI-Assisted Satellite Imagery Analysis for Program Evaluation and Monitoring | International Affairs | Computer Vision | Traditional evaluation and monitoring approaches can require extensive field data collection and may provide limited ability to observe changes across large or geographically dispersed agricultural areas. The use case applies machine learning to satellite imagery and related data to identify and monitor land-use and agricultural conditions and generate evaluation-relevant findings about MCC-supported investments and interventions. | Machine-learning-derived classifications, estimates, maps, and other geospatial indicators describing agricultural and land-surface conditions. Outputs may include crop-type classifications, estimates of agricultural productivity or yield, and changes in observed conditions over time. Results are used as evaluation and monitoring evidence and are subject to interpretation and validation by MCC and NASA researchers and other subject-matter experts. |
| MCC-02 | Document writing and analysis | Administrative Functions | Generative SI | Personnel routinely create, translate, review, summarize, and refine documents that support mission planning, decision-making, communications, and operations. AI assistance can enhance these activities by helping users draft and edit content, generate summaries, identify key insights, improve clarity and consistency, and analyze information contained within documents. These capabilities can accelerate document development, improve the quality and consistency of work products, and enable personnel to focus more time on mission-critical activities and decision-making. | The system produces natural-language outputs such as drafted emails and memos, document summaries, rewritten text for improved clarity or tone, outlines and bullet lists, simple comparative analyses of user-provided content, and interactive explanations of policies and reference materials. It can also generate and explain code snippets, Python scripts, Power Query expressions, DAX formulas, spreadsheet formulas, and other technical recommendations to support data transformation, visualization, analysis, troubleshooting, and internal automation. AI-generated content, code, formulas, and analytical outputs are reviewed, tested, and validated by staff before use in official products or operational workflows. |
| MCC-03 | General productivity | Administrative Functions | Generative SI | Personnel routinely participate in meetings, coordinate activities, manage follow-up actions, communicate across teams, and work in multilingual environments. AI assistance can enhance productivity by generating meeting summaries and action items, supporting real-time translation and language accessibility, facilitating task tracking and follow-up, and helping teams maintain awareness of key decisions and outcomes. These capabilities improve collaboration, promote more inclusive participation, reduce administrative burden, and enable personnel to focus more time on mission-critical activities. | Generate consistent summaries and action items for internal meetings, reducing time staff spend on manual note taking and follow up drafting; improve clarity about decisions, owners, and next steps, which supports more reliable execution of mission work; and improve continuity across recurring meetings by providing a written record that participants can quickly review. Live closed captioning for multi-lingual meetings. |
| MCC-04 | Search and information retreival | Administrative Functions | Generative SI | Effective mission execution depends on personnel being able to quickly locate and use relevant, authoritative agency information. AI assistance can improve search and information retrieval by helping users find and surface resources across approved agency repositories, systems, and knowledge bases using natural-language questions. It can identify relevant policies, guidance, procedures, reference materials, and other institutional knowledge; summarize key information; and connect users to source content for further review. These capabilities improve access to agency resources, reduce time spent navigating multiple information sources, strengthen knowledge sharing, and support timely, well-informed mission decisions. | Outputs produced are natural-language responses to user questions and return of relevant information from approved agency resources, knowledge repositories, policies, procedures, guidance documents, and reference materials. Outputs may include summarized answers, links or references to authoritative source documents, synthesized information from multiple approved sources, recommended resources for further review, and conversational guidance that helps users locate and understand agency information. |
| MCC-05 | Apply NLP to de-identify, transcribe, translate, and code qualitative data for MCC evaluations | International Affairs | Natural Language Processing (NLP) | Evaluation activities generate large volumes of interview and other qualitative data in multiple languages that require substantial manual effort to prepare for analysis. This effort is intended to streamline the processing of evaluation data by de-identifying, transcribing, translating, and coding qualitative information, allowing evaluators to analyze larger amounts of information more efficiently while maintaining appropriate human review. | De-identified transcripts, translated transcripts or text, and coded qualitative data derived from evaluation interviews and other source material. Outputs are used to support human-led evaluation and analysis and are subject to review and validation by evaluation personnel. |
| MCC-06 | AI-assisted legal research | International Affairs | Generative SI | OGC staff must conduct time-intensive legal and policy research across multiple sources to gather authoritative information, perform due diligence, establish factual context, and validate information. These efforts can be resource-intensive and delay the delivery of timely, well-supported legal analysis and advice. | Improves research efficiency, accelerates access to relevant legal and policy information, supports fact-checking and due diligence, and helps staff synthesize information to inform professional analysis and advice by answering human language questions or prompts. |
Send questions to [email protected].
MCC - Millennium Challenge Corporation published this content on November 10, 2026, and is solely responsible for the information contained herein. Distributed via Public Technologies (PUBT), unedited and unaltered, on November 10, 2026 at 14:00 UTC. If you believe the information included in the content is inaccurate or outdated and requires editing or removal, please contact us at [email protected]