Printed from https://fiscalreceipts.com/program/0602180A/ — built September 26, 2026. Every figure is citation-backed; see the page online for per-number provenance.
Artificial Intelligence and Machine Learning Technologies
Budget figures
FY2026 award data is a partial year — USAspending reports awards on a rolling basis, and this corpus runs through 2026-09-04. why partial FY2026 data? →
Budget trajectory
| Fiscal year | Amount |
|---|---|
| FY24 | $23.7M |
| FY25 | $20.3M |
| FY26 | $13.7M |
All series figures: R-1 TOA · PB2026
Decade view
P-1/R-1 workbook TOA basis, shown compact in $B/$M (the workbook records USD thousands); each figure cites its own President's Budget edition
The vertical scale does not start at zero: the baseline sits just below this program’s smallest year, so a low point on this line is not a small amount. Read the shape for direction and the grid below for the figures.
● actuals (line) · ○ enacted · ◇ request — gaps are editions the program is absent from, never interpolated.
| Series | FY20 | FY21 | FY22 | FY23 | FY24 | FY25 | FY26 |
|---|---|---|---|---|---|---|---|
| Actuals | $0 | $0 | $14.5M | $15.5M | $23.7M | ||
| Enacted | – | $0 | $15.0M | $16.1M | $24.1M | $20.3M | |
| Request | – | – | $15.0M | $16.5M | $24.1M | $20.3M | $13.7M |
blank = series not published for this year; – = absent from that edition.
Asked vs spent: the PB2023 book requested $16.5M for FY2023; the PB2025 book reported $15.5M as actual total obligation authority — $973.0K below the request. 15.5 − 16.5 = -1.0 USD millions — the compact figures above are rounded for reading.
Program lineage
No predecessor/successor lineage was recorded for this program element — no FY-to-FY transfer into or out of this line was stated in the ingested J-books, and none was inferred from the program structure.
Description
Mission — Artificial Intelligence and Machine Learning Technologies
This Program Element (PE) investigates artificial intelligence (AI) and machine learning (ML) to support an AI-enabled Multi-Domain Operations (MDO) Force to mature target recognition/detection using multiple cooperative autonomous sensors (MCAS), leader decision-making, replication of tactical behaviors to enable autonomous capabilities for maneuver, predictive maintenance, and intelligence support for operations in support of long-range precision fires. The Army's Artificial Integration Center (AI2C) will provide strategic guidance and coordination of these applied research efforts in AI/ML across the Army Modernization enterprise. Work in this PE contributes to the Army Science and Technology (S&T) portfolio and is fully coordinated with efforts in PE 0601601A (Artificial Intelligence Basic Research) and PE 0603040A (Artificial Intelligence Advanced Technologies). The cited research is consistent with the Under Secretary of Defense for Research and Engineering S&T focus areas, the Army Modernization Strategy and the Chief Digital and Artificial Intelligence Office (CDAO).
Mission — AI Enhanced Intel Operations Technologies
This Project will design and develop technologies to augment human intelligence analysts with artificial intelligence (AI) and machine learning (ML)-enabled decision support, workflow automation, and recommendation tools to modernize how the Intelligence Warfighting Function supports Multi-Domain Operations and Joint All Domain Command and Control (JADC2). This Project will mature technologies that will enable intelligence organizations to conduct synchronized, proactive intelligence operations, therefore optimizing team performance. Work in this project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CL1 (AI Enhanced Intel Operations Advanced Technologies). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — ATR Using Multiple Cooperative Sensors App Tech
This Project will design and develop Artificial Intelligence (AI) and Machine Learning (ML) algorithms that leverage a team of air and ground sensors to autonomously navigate and collaborate through shared perception of the optical, thermal, and electromagnetic spectrums to find, identify, geo-locate, and track targets during reconnaissance missions. These technologies will produce prototype implementations of novel autonomy and detection algorithms to be run on teams of air and ground sensors, as well as an appropriate interface to task and observe feedback from autonomous sensors. Work in this Project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CL6 (ATR Using Multiple Cooperative Sensors Adv Technologies) The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Predictive Maintenance Applied Research
This Project designs and develops artificial intelligence (AI) and machine learning (ML) tools and capabilities to predict and analyze maintenance status for emerging and legacy aviation and ground platforms. Investigates techniques to extract data from maintenance databases and platform sensors and make inferences of missing data via virtual simulations. Will investigate maintenance concepts that employ AI data capture and integrate AI tools into enterprise resource planning for military aviation and ground vehicles. Will determine platforms of focus and prioritize by cost and value to Army missions. Each platform will be sequentially investigated at the appropriate component (i.e. engine health) and fleet level. Will determine appropriate technologies and capabilities needed to construct a robust Army-wide predicative maintenance platform that will accelerate the pace of innovation for this problem set. Will validate and inform requirements and technical architectures for modernization efforts of next generation aviation and ground systems both manned and unmanned. These technologies will produce concepts for a digitized maintenance environment that provides real-time decision-making support tools to maintainers and commanders by producing a warfighter optimized front end with an enterprise aggregated back end. Work in this project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CN6 (Predictive Maintenance Advanced Technology). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — AI Enabled Talent Management Applied Research
This Project designs, develops, and validates applied behavioral and social science research to enhance the Soldier Lifecycle (e.g., selection, assignment, training, and leader development) and human relations (e.g., unit cohesion). This Project will design and develop new personnel measures and methods that more fully assess potential and predict performance, behavior, attitudes, and resilience. These technologies also provide innovative and effective Force Integration methods to optimize individual and team performance to ensure the Army can meet mission requirements in uncertain and complex environments. This Project designs and develops new performance measures and metrics for individuals and units, designs innovative training methods, and conducts scientific assessments to inform Human Capital policy and programs. This Project will also investigate non-materiel solutions to help the Army adjust to changes in force size and structure, a variety of mission demands and contexts, challenges in human relations, and budgetary constraints. These technologies will produce tools that can measure and assess the skills of individual Soldiers and units' readiness to meet mission requirements. Work in this Project complements Program Element (PE) 0603007A (Manpower, Personnel and Training Advanced Technology) / Project 792 (Personnel Performance & Training). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Research in this Project supports the Army Science and Technology Ground Portfolio. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — AI-Enabled Command and Coordination Apl Research
This Project designs and develops solutions that enable Artificial Intelligence (AI)-Enabled Command and Coordination. Additionally, project investigates and matures technologies required to enable commanders and their staff to synchronize and converge all elements of available combat power to achieve multi-domain effects. Technology maturation includes the development and testing of algorithms, models, software, hardware, and interfaces required to support the command of Army forces, coordination of Army operations, execution of the operations process, and establishing necessary Command and Control (C2) systems. Work in this Project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project DA7 (AI-Enabled Command and Coordination Adv Tech). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Army AI Integration Center Apl Research (CA)
Congressional Interest Item funding provided for Army AI Integration Center Applied Research. The cited work is consistent with the Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy.
Mission — AI Development Environment Applied Research
This effort investigates cloud-native architectures, orchestration technologies, and collaboration techniques to support current and future Artificial Intelligence (AI) model development and machine learning operations (MLOps) tasks across a globally distributed workforce. Research will increase the effectiveness and efficiency of development platforms, decrease model development costs, optimize shared resources, and reduce the time required to integrate new AI capabilities into software products. This effort will provide the AI enabled Army of the future with low cost, rapid analytic and AI/ML solutions at the edge and enable accelerated algorithm development for faster delivery to the field. Less expensive AI/ML development by leveraging shared resources. These technologies will mature software components to improve the speed of development of AI models. Work in this project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project DE9 (AI Development Environment Advanced Technology). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Counter AI App Rsch
This Project designs and develops mechanisms for the implementation of trusted artificial intelligence and machine learning (AI/ML) for processing, detecting, identifying, and reacting to potentially adverse effects on AI/ML capabilities. It provides recommendations for countering adversarial AI/ML, improving algorithms, and ensuring resilience in complex and contested environments. Effective use of Counter-AI to secure response mechanisms for the identification and detection of adversarial AI/ML is critical to address threats in a rapidly evolving environment. These technologies will produce an AI solution that rapidly adjusts AI/ML algorithms to disregard and stop malicious attempts to corrupt Army AI/ML tools. The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — AI Enabled Contested Logistics Spt Tools App Tech
This Project designs and develops AI-enabled contested logistics tools for warfighters using all platforms (legacy and future) at all echelons, from the maintenance area to the lowest tactical level. This project investigates data from programs of record and determines additional data streams required to build a complete picture of logistics operations in a contested environment. Contested logistics data will investigate the required maintenance data, operations information, and personnel data to increase unit readiness and reduce decision making timelines and predict unit readiness based on historical operations. These technologies will design a suite of applications uniquely tailored to the end-user that will actively expand machine learning capabilities across the force with regards to the contested logistics domain. Work in this project complements Program Element (PE) 0603040A / Artificial Intelligence and Machine Learning Advanced Technologies / CN6 / Predictive Maintenance Advanced Technology. The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Artificial Intelligence and Machine Learning Technologies
This Program Element (PE) investigates artificial intelligence (AI) and machine learning (ML) to support an AI-enabled Multi-Domain Operations (MDO) Force to mature target recognition/detection using multiple cooperative autonomous sensors (MCAS), leader decision-making, replication of tactical behaviors to enable autonomous capabilities for maneuver, predictive maintenance, and intelligence support for operations in support of long-range precision fires. The Army's Artificial Integration Center (AI2C) will provide strategic guidance and coordination of these applied research efforts in AI/ML across the Army Modernization enterprise. Work in this PE contributes to the Army Science and Technology (S&T) portfolio and is fully coordinated with efforts in PE 0601601A (Artificial Intelligence Basic Research) and PE 0603040A (Artificial Intelligence Advanced Technologies). The cited research is consistent with the Under Secretary of Defense for Research and Engineering S&T focus areas, the Army Modernization Strategy and the Chief Digital and Artificial Intelligence Office (CDAO).
Mission — AI Enhanced Intel Operations Technologies
This Project will design and develop technologies to augment human intelligence analysts with artificial intelligence (AI) and machine learning (ML)-enabled decision support, workflow automation, and recommendation tools to modernize how the Intelligence Warfighting Function supports Multi-Domain Operations and Joint All Domain Command and Control (JADC2). This Project will mature technologies that will enable intelligence organizations to conduct synchronized, proactive intelligence operations, therefore optimizing team performance. Work in this project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CL1 (AI Enhanced Intel Operations Advanced Technologies). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — ATR Using Multiple Cooperative Sensors App Tech
This Project will design and develop Artificial Intelligence (AI) and Machine Learning (ML) algorithms that leverage a team of air and ground sensors to autonomously navigate and collaborate through shared perception of the optical, thermal, and electromagnetic spectrums to find, identify, geo-locate, and track targets during reconnaissance missions. These technologies will produce prototype implementations of novel autonomy and detection algorithms to be run on teams of air and ground sensors, as well as an appropriate interface to task and observe feedback from autonomous sensors. Work in this Project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CL6 (ATR Using Multiple Cooperative Sensors Adv Technologies) The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Predictive Maintenance Applied Research
This Project designs and develops artificial intelligence (AI) and machine learning (ML) tools and capabilities to predict and analyze maintenance status for emerging and legacy aviation and ground platforms. Investigates techniques to extract data from maintenance databases and platform sensors and make inferences of missing data via virtual simulations. Will investigate maintenance concepts that employ AI data capture and integrate AI tools into enterprise resource planning for military aviation and ground vehicles. Will determine platforms of focus and prioritize by cost and value to Army missions. Each platform will be sequentially investigated at the appropriate component (i.e. engine health) and fleet level. Will determine appropriate technologies and capabilities needed to construct a robust Army-wide predicative maintenance platform that will accelerate the pace of innovation for this problem set. Will validate and inform requirements and technical architectures for modernization efforts of next generation aviation and ground systems both manned and unmanned. These technologies will produce concepts for a digitized maintenance environment that provides real-time decision-making support tools to maintainers and commanders by producing a warfighter optimized front end with an enterprise aggregated back end. Work in this project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project CN6 (Predictive Maintenance Advanced Technology). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — AI Enabled Talent Management Applied Research
This Project designs, develops, and validates applied behavioral and social science research to enhance the Soldier Lifecycle (e.g., selection, assignment, training, and leader development) and human relations (e.g., unit cohesion). This Project will design and develop new personnel measures and methods that more fully assess potential and predict performance, behavior, attitudes, and resilience. These technologies also provide innovative and effective Force Integration methods to optimize individual and team performance to ensure the Army can meet mission requirements in uncertain and complex environments. This Project designs and develops new performance measures and metrics for individuals and units, designs innovative training methods, and conducts scientific assessments to inform Human Capital policy and programs. This Project will also investigate non-materiel solutions to help the Army adjust to changes in force size and structure, a variety of mission demands and contexts, challenges in human relations, and budgetary constraints. These technologies will produce tools that can measure and assess the skills of individual Soldiers and units' readiness to meet mission requirements. Work in this Project complements Program Element (PE) 0603007A (Manpower, Personnel and Training Advanced Technology) / Project 792 (Personnel Performance & Training). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Research in this Project supports the Army Science and Technology Ground Portfolio. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — AI-Enabled Command and Coordination Apl Research
This Project designs and develops solutions that enable Artificial Intelligence (AI)-Enabled Command and Coordination. Additionally, project investigates and matures technologies required to enable commanders and their staff to synchronize and converge all elements of available combat power to achieve multi-domain effects. Technology maturation includes the development and testing of algorithms, models, software, hardware, and interfaces required to support the command of Army forces, coordination of Army operations, execution of the operations process, and establishing necessary Command and Control (C2) systems. Work in this Project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project DA7 (AI-Enabled Command and Coordination Adv Tech). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Army AI Integration Center Apl Research (CA)
Congressional Interest Item funding provided for Army AI Integration Center Applied Research. The cited work is consistent with the Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy.
Mission — AI Development Environment Applied Research
This effort investigates cloud-native architectures, orchestration technologies, and collaboration techniques to support current and future Artificial Intelligence (AI) model development and machine learning operations (MLOps) tasks across a globally distributed workforce. Research will increase the effectiveness and efficiency of development platforms, decrease model development costs, optimize shared resources, and reduce the time required to integrate new AI capabilities into software products. This effort will provide the AI enabled Army of the future with low cost, rapid analytic and AI/ML solutions at the edge and enable accelerated algorithm development for faster delivery to the field. Less expensive AI/ML development by leveraging shared resources. These technologies will mature software components to improve the speed of development of AI models. Work in this project complements Program Element (PE) 0603040A (Artificial Intelligence and Machine Learning Advanced Technologies) / Project DE9 (AI Development Environment Advanced Technology). The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — Counter AI App Rsch
This Project designs and develops mechanisms for the implementation of trusted artificial intelligence and machine learning (AI/ML) for processing, detecting, identifying, and reacting to potentially adverse effects on AI/ML capabilities. It provides recommendations for countering adversarial AI/ML, improving algorithms, and ensuring resilience in complex and contested environments. Effective use of Counter-AI to secure response mechanisms for the identification and detection of adversarial AI/ML is critical to address threats in a rapidly evolving environment. These technologies will produce an AI solution that rapidly adjusts AI/ML algorithms to disregard and stop malicious attempts to corrupt Army AI/ML tools. The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Mission — AI Enabled Contested Logistics Spt Tools App Tech
This Project designs and develops AI-enabled contested logistics tools for warfighters using all platforms (legacy and future) at all echelons, from the maintenance area to the lowest tactical level. This project investigates data from programs of record and determines additional data streams required to build a complete picture of logistics operations in a contested environment. Contested logistics data will investigate the required maintenance data, operations information, and personnel data to increase unit readiness and reduce decision making timelines and predict unit readiness based on historical operations. These technologies will design a suite of applications uniquely tailored to the end-user that will actively expand machine learning capabilities across the force with regards to the contested logistics domain. Work in this project complements Program Element (PE) 0603040A / Artificial Intelligence and Machine Learning Advanced Technologies / CN6 / Predictive Maintenance Advanced Technology. The cited work is consistent with Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Artificial Intelligence Integration Center (AI2C).
Justification
Accomplishments & Planned Programs (38)
AI-Enabled Intelligence Decision Support
This effort will investigate the augmentation of Military Intelligence and Operations (Intel/Ops) with artificial intelligence capabilities to leverage Mission, Enemy, Terrain and Weather, Troops, Time Available, and Civilian Considerations (METT-TC) information available to Commanders in support of Intelligence Preparation of the Battlefield (IPB) and the Military Decision Making Process (MDMP). The effort will mature techniques to visualize and animate threat models to support automated AI-enabled enemy courses of action analysis.
Foundation for AI Intelligence Support to Operations (ARCANE SERIES)
Design and develop an AI infrastructure/pipeline for training, integrating, and sustaining AI across multiple AI domains to inform requirements for enterprise production systems and edge systems for the Army Military Intelligence and Operations (Intel/Ops) community.
Rare Object Generation and Detection
This effort will design and develop AI and machine learning (ML) technology to generate and detect objects that are rarely detected and have limited training data sets (rare object generation and detection). Rare object generation and detection is a key ML challenge due to limited amounts of available training data that make it difficult to build high performing AI models to address these challenges.
AI-Enabled Intelligence Fusion for Targeting
AI Enabled Intelligence Fusion for Targeting will investigate the fusion of different type of intelligence data (multi-INT fusion) and validate AI algorithms that can fuse data from various military intelligence systems to support sensor to shooter automation for the strategic, operational, and tactical levels. This effort will design and develop AI capabilities for support of Long Range Precision Fires, Mission Command, and Maneuver Commanders by leveraging Intelligence Community enterprise investments in sensing, data transport, and Machine Learning / AI frameworks.
AI-Enabled Social Media Exploitation
Artificial Intelligence (AI) Enabled Social Media Exploitation will enhance the social cybersecurity posture for the U.S. Army by developing, maturing, and experimenting with AI-enabled tools for exploiting social media information and other pertinent publicly available information (PAI). This effort investigates how the combination of network science with AI/ML techniques such as natural language processing and low shot learning and enables identification and characterization of adversaries and collection opportunities via cyber-mediated vectors. These capabilities support improved battlefield awareness by allowing operational units to discover and track online, adversarial influence campaigns, in multiple languages across multiple platforms.
Collaborative Target Detection and Tracking
This effort will design and develop the AI / ML technologies to automatically detect and track targets using electro-optical, thermal, and electromagnetic sensors and constrained computing hardware onboard the air and ground vehicles and share threat perception across the unmanned team.
Autonomous and Collaborative Mobility
This effort will design and develop mobility algorithms using AI and ML techniques that allow autonomous ground and air vehicles to passively perceive the terrain and self-navigate without active and detectable sensing. Design and develop collaborative teaming techniques for autonomous air and ground vehicles to work together on reconnaissance missions.
Intuitive Mission Command Interfaces
Design and develop the capability for warfighters to quickly and intuitively convey reconnaissance guidance, confirm or deny detected targets, and take recommended action through common mission command tools, including Tactical Assault Kit (TAK) and Integrated Visual Augmentation System (IVAS).
Predictive Maintenance
This Project designs and develops artificial intelligence (AI) and machine learning (ML) tools and capabilities to predict and analyze maintenance status for emerging and legacy aviation and ground platforms. Investigates techniques to extract data from maintenance databases and platform sensors and make inferences to address missing data. Will investigate maintenance concepts that employ AI data capture and integrate AI tools into enterprise resource planning for military aviation and ground vehicles. Will determine platforms of focus and prioritize by cost and value to Army missions. Each platform will be sequentially investigated at the appropriate component (i.e. engine health) and fleet level. Will determine appropriate technologies and capabilities needed to construct a robust Army-wide predicative maintenance platform that will accelerate the pace of innovation for this problem set. Will validate and inform requirements and technical architectures for modernization efforts of next generation aviation and ground systems both manned and unmanned.
Artificial Intelligence (AI)-Enabled Skill Identification for Job Matching and Team Building
This effort will develop AI techniques to create an analytical suite that can measure skills required by job postings and skills possessed by soldiers and officers. This will permit the Army to "put the right person in the right job" and determine how to combine individuals to optimize team performance.
AI-Enhanced Planning for Optimal Operations
This effort designs and develops AI-enabled components for associating people, processes, networks, and command posts in support of command and control. Develops and trains models that analyze, understand, and optimize AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities. This effort will provide tool for Commanders and staffs at Echelons Above Brigade to explore hypothetical situations in support of the operations process and Army planning to achieve decision dominance.
AI Command and Coordination Environment
This effort designs and develops AI-enabled systems that link people, processes, networks, and command posts in support of command and coordination. Develops and trains models that analyze, understand, and optimize AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities.
AI-Enabled Common Operating Picture and Battle Tracking
This effort develops and matures AI-enabled tools that allow commanders and staff to prepare for, execute, and assess Army operations to enable decision dominance. Matures and investigate human-machine interfaces that take input of commanders' intent and plans and provides computer-based battle tracking to identify risk to mission and force and AI-optimized direction to Army forces and unified action partners.
Distributed Artificial Intelligence
Designs and develops a distributed AI architecture that will be able to autonomously search for and discover heterogeneous data sources; optimizes AI processing across dynamic and opportunistic resources; and fuses AI capabilities between the enterprise, the edge, and AI-infused sensors and systems embedded on-platform.
AI Foundations for Command and Coordination
Develops, trains, and fine tunes novel foundational models in computer vision, natural language processing/ understanding, and temporal/event series analysis that analyze, understand, and optimize enhance AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities.
Soldier Assistant Language Technologies
This effort will investigate and mature application of cutting-edge language technologies onto warfighter systems in order to increase network effectiveness and resilience, reduce personnel requirements, and increase Solder situational awareness. Exploitation of semantic understanding, machine translation, natural language processing, automated speech recognition and other emerging language-based technologies and techniques enable decisions at machine speed, expanding the scope of useful da. Tools such as large language and multi-modal models, artificial intelligence agents, and audio denoisers will streamline staff processes and empower forces at all echelons to operate and make sense of information in ways not previously possible.
Artificial Intelligence Environment Applied Research
This effort investigates cloud and cloud-native architectures, orchestration technologies, and collaboration techniques to support current and future AI model development and machine learning operations (MLOps) tasks across a globally distributed workforce. Research will increase efficiency of development platforms, decrease model development costs, and reduce the time required to integrate new AI capabilities into software products.
Counter AI ML Model Applied Research
This Effort will research capabilities to identify, detect, prevent, protect, and react to exploitation of AI/ML model vulnerabilities. The exploitation of AI/ML models can adversely affect the performance of the underlying systems. This Effort will provide tools to verify and validate techniques capable of detecting the potential presence of malicious adversarial inputs and/or inaccurate AI/ML model performance.
Federated Predictive Logistics Applied Research
This effort investigates the required predictive logistics analytics by validating the collection and input of structured, quality data from the warfighter and networked sensors; developing validated and verified algorithms; and by developing machine learning models for use by warfighters, to identify and quantify risk, effectively allocate and prioritize resources, and assess future courses of action in support of logistics and sustainment operations in a contested environment.
AI-Enabled Intelligence Decision Support
This effort will investigate the augmentation of Military Intelligence and Operations (Intel/Ops) with artificial intelligence capabilities to leverage Mission, Enemy, Terrain and Weather, Troops, Time Available, and Civilian Considerations (METT-TC) information available to Commanders in support of Intelligence Preparation of the Battlefield (IPB) and the Military Decision Making Process (MDMP). The effort will mature techniques to visualize and animate threat models to support automated AI-enabled enemy courses of action analysis.
Foundation for AI Intelligence Support to Operations (ARCANE SERIES)
Design and develop an AI infrastructure/pipeline for training, integrating, and sustaining AI across multiple AI domains to inform requirements for enterprise production systems and edge systems for the Army Military Intelligence and Operations (Intel/Ops) community.
Rare Object Generation and Detection
This effort will design and develop AI and machine learning (ML) technology to generate and detect objects that are rarely detected and have limited training data sets (rare object generation and detection). Rare object generation and detection is a key ML challenge due to limited amounts of available training data that make it difficult to build high performing AI models to address these challenges.
AI-Enabled Intelligence Fusion for Targeting
AI Enabled Intelligence Fusion for Targeting will investigate the fusion of different type of intelligence data (multi-INT fusion) and validate AI algorithms that can fuse data from various military intelligence systems to support sensor to shooter automation for the strategic, operational, and tactical levels. This effort will design and develop AI capabilities for support of Long Range Precision Fires, Mission Command, and Maneuver Commanders by leveraging Intelligence Community enterprise investments in sensing, data transport, and Machine Learning / AI frameworks.
AI-Enabled Social Media Exploitation
Artificial Intelligence (AI) Enabled Social Media Exploitation will enhance the social cybersecurity posture for the U.S. Army by developing, maturing, and experimenting with AI-enabled tools for exploiting social media information and other pertinent publicly available information (PAI). This effort investigates how the combination of network science with AI/ML techniques such as natural language processing and low shot learning and enables identification and characterization of adversaries and collection opportunities via cyber-mediated vectors. These capabilities support improved battlefield awareness by allowing operational units to discover and track online, adversarial influence campaigns, in multiple languages across multiple platforms.
Collaborative Target Detection and Tracking
This effort will design and develop the AI / ML technologies to automatically detect and track targets using electro-optical, thermal, and electromagnetic sensors and constrained computing hardware onboard the air and ground vehicles and share threat perception across the unmanned team.
Autonomous and Collaborative Mobility
This effort will design and develop mobility algorithms using AI and ML techniques that allow autonomous ground and air vehicles to passively perceive the terrain and self-navigate without active and detectable sensing. Design and develop collaborative teaming techniques for autonomous air and ground vehicles to work together on reconnaissance missions.
Intuitive Mission Command Interfaces
Design and develop the capability for warfighters to quickly and intuitively convey reconnaissance guidance, confirm or deny detected targets, and take recommended action through common mission command tools, including Tactical Assault Kit (TAK) and Integrated Visual Augmentation System (IVAS).
Predictive Maintenance
This Project designs and develops artificial intelligence (AI) and machine learning (ML) tools and capabilities to predict and analyze maintenance status for emerging and legacy aviation and ground platforms. Investigates techniques to extract data from maintenance databases and platform sensors and make inferences to address missing data. Will investigate maintenance concepts that employ AI data capture and integrate AI tools into enterprise resource planning for military aviation and ground vehicles. Will determine platforms of focus and prioritize by cost and value to Army missions. Each platform will be sequentially investigated at the appropriate component (i.e. engine health) and fleet level. Will determine appropriate technologies and capabilities needed to construct a robust Army-wide predicative maintenance platform that will accelerate the pace of innovation for this problem set. Will validate and inform requirements and technical architectures for modernization efforts of next generation aviation and ground systems both manned and unmanned.
Artificial Intelligence (AI)-Enabled Skill Identification for Job Matching and Team Building
This effort will develop AI techniques to create an analytical suite that can measure skills required by job postings and skills possessed by soldiers and officers. This will permit the Army to "put the right person in the right job" and determine how to combine individuals to optimize team performance.
AI-Enhanced Planning for Optimal Operations
This effort designs and develops AI-enabled components for associating people, processes, networks, and command posts in support of command and control. Develops and trains models that analyze, understand, and optimize AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities. This effort will provide tool for Commanders and staffs at Echelons Above Brigade to explore hypothetical situations in support of the operations process and Army planning to achieve decision dominance.
AI Command and Coordination Environment
This effort designs and develops AI-enabled systems that link people, processes, networks, and command posts in support of command and coordination. Develops and trains models that analyze, understand, and optimize AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities.
AI-Enabled Common Operating Picture and Battle Tracking
This effort develops and matures AI-enabled tools that allow commanders and staff to prepare for, execute, and assess Army operations to enable decision dominance. Matures and investigate human-machine interfaces that take input of commanders' intent and plans and provides computer-based battle tracking to identify risk to mission and force and AI-optimized direction to Army forces and unified action partners.
Distributed Artificial Intelligence
Designs and develops a distributed AI architecture that will be able to autonomously search for and discover heterogeneous data sources; optimizes AI processing across dynamic and opportunistic resources; and fuses AI capabilities between the enterprise, the edge, and AI-infused sensors and systems embedded on-platform.
AI Foundations for Command and Coordination
Develops, trains, and fine tunes novel foundational models in computer vision, natural language processing/ understanding, and temporal/event series analysis that analyze, understand, and optimize enhance AI-operations across Army Battle Command Systems and data fabrics. Establishes access to fused multitudinous data sources in support of AI-based analytics capabilities.
Soldier Assistant Language Technologies
This effort will investigate and mature application of cutting-edge language technologies onto warfighter systems in order to increase network effectiveness and resilience, reduce personnel requirements, and increase Solder situational awareness. Exploitation of semantic understanding, machine translation, natural language processing, automated speech recognition and other emerging language-based technologies and techniques enable decisions at machine speed, expanding the scope of useful da. Tools such as large language and multi-modal models, artificial intelligence agents, and audio denoisers will streamline staff processes and empower forces at all echelons to operate and make sense of information in ways not previously possible.
Artificial Intelligence Environment Applied Research
This effort investigates cloud and cloud-native architectures, orchestration technologies, and collaboration techniques to support current and future AI model development and machine learning operations (MLOps) tasks across a globally distributed workforce. Research will increase efficiency of development platforms, decrease model development costs, and reduce the time required to integrate new AI capabilities into software products.
Counter AI ML Model Applied Research
This Effort will research capabilities to identify, detect, prevent, protect, and react to exploitation of AI/ML model vulnerabilities. The exploitation of AI/ML models can adversely affect the performance of the underlying systems. This Effort will provide tools to verify and validate techniques capable of detecting the potential presence of malicious adversarial inputs and/or inaccurate AI/ML model performance.
Federated Predictive Logistics Applied Research
This effort investigates the required predictive logistics analytics by validating the collection and input of structured, quality data from the warfighter and networked sensors; developing validated and verified algorithms; and by developing machine learning models for use by warfighters, to identify and quantify risk, effectively allocate and prioritize resources, and assess future courses of action in support of logistics and sustainment operations in a contested environment.
Budget line items(workbook-cited)
P-1/R-1 workbook Total Obligation Authority basis (USD thousands) · PB2026.
Exhibit R-1
| Account | Org | Type | Amount |
|---|---|---|---|
| Research, Development, Test and Evaluation, Army | A | FY24 Actuals | $23.7M |
| Research, Development, Test and Evaluation, Army | A | FY25 Enacted | $20.3M |
| Research, Development, Test and Evaluation, Army | A | FY25 Total | $20.3M |
| Research, Development, Test and Evaluation, Army | A | FY26 Disc. Request | $13.7M |
| Research, Development, Test and Evaluation, Army | A | FY26 Total | $13.7M |
Budget Details(R-2/P-40 facts)
J-book detail basis (R-2/P-40, USD millions) · PB2026 — a different accounting basis from the P-1/R-1 workbook TOA above; where the two disagree, the reconciliation strip under Budget figures shows both.
Wider than this screen — swipe the table sideways for the remaining fiscal-year columns.
| Project | FY24 Actuals | FY25 Total | FY26 Base | FY26 Request |
|---|---|---|---|---|
| Program Element | $23.7M | $20.3M | $13.7M | $13.7M |
| CL2: AI Enhanced Intel Operations Technologies | $2.45M | $2.97M | $2.82M | $2.82M |
| CL7: ATR Using Multiple Cooperative Sensors App Tech | $7.94M | $5.70M | $2.63M | $2.63M |
| CN7: Predictive Maintenance Applied Research | $5.81M | $6.07M | $1.26M | $1.26M |
| DA5: AI Enabled Talent Management Applied Research | — | $307.0K | $312.0K | $312.0K |
| DA6: AI-Enabled Command and Coordination Apl Research | $3.15M | $3.52M | $4.98M | $4.98M |
| DM7: Counter AI App Rsch | — | — | $1.50M | $1.50M |
| DM8: AI Enabled Contested Logistics Spt Tools App Tech | — | — | $249.0K | $249.0K |
| DE8: AI Development Environment Applied Research | $1.35M | $1.75M | — | — |
| DB9: Army AI Integration Center Apl Research (CA) | $3.00M | — | — | — |
Follow the dollar
No follow-the-dollar view — this program's awards haven't been crosswalked at high confidence (flows cover 312 of 1,938 programs). why coverage is partial? →
Awards
No awards are linked to this program element at high or medium confidence — the budget→award crosswalk only asserts links it can defend, and this line has none yet.
Lobbying mentions
Showing 25 of 43 from the Senate LDA disclosure database.
H.R.1, One Big Beautiful Bill Act 2025 FY27 Defense Appropriations FY27 Transportation Appropriations (THUD): FAA…
H.R.1, One Big Beautiful Bill Act 2025 FY27 Defense Appropriations FY27 Transportation Appropriations (THUD): FAA…
House (no bill number) and Senate (no bill number) Fiscal Year 2027 Department of Defense Appropriations Bill…
House (HR 9495) and Senate (no bill number) Fiscal Year 2027 Department of Defense Appropriations Bill -- issues and…
General outreach on science, technology, engineering, and math programs (STEM). Issues related to artificial…
General outreach on science, technology, engineering, and math programs (STEM). Issues related to artificial…
General digital transformation, cybersecurity and technology issues, artificial intelligence Multi-Cloud technology…
- Artificial Intelligence - Quantum - Weather Act
- Artificial Intelligence - Quantum - Cyber
- Artificial Intelligence - Quantum - Cyber
H.R.1, One Big Beautiful Bill Act 2025 FY26 Defense Appropriations (S.2572/H.R.4016) FY26 Transportation…
H.R.1, One Big Beautiful Bill Act 2025 FY26 Defense Appropriations (S.2572/H.R.4016) FY26 Transportation…
H.R.1, One Big Beautiful Bill Act 2025 FY26 Defense Appropriations FY26 Transportation Appropriations (THUD) FY26…
FY24 and FY25 Defense Appropriations FY24 Transportation Appropriations (THUD) FY24 Interior and Environment…
House (HR 4016) and Senate (no bill number) Fiscal Year 2026 Department of Defense Appropriations Bill -- issues and…
Licensing, competition, trade, and government procurement. Health IT procurement; health reform and related IT issues.…
House (HR 8774) and Senate (S 4921) Fiscal Year 2025 Department of Defense Appropriations Bill -- issues and funding…
Licensing, competition, trade, and government procurement. Health IT procurement; health reform and related IT issues.…
House (HR 4016) and Senate (S 2572) Fiscal Year 2026 Department of Defense Appropriations Bill -- issues and funding…
House (HR 4016) and Senate (S 2572) Fiscal Year 2026 Department of Defense Appropriations Bill -- issues and funding…
General outreach on science, technology, engineering, and math programs (STEM). Issues related to artificial…
General outreach on science, technology, engineering, and math programs (STEM). Issues related to artificial…
General outreach on science, technology, engineering, and math programs (STEM). Issues related to artificial…
Issues related to Artificial Intelligence
Issues related to Artificial Intelligence
Oversight
Department-level designation (not specific to this program)
GAO lists 5 high-risk areas for DOD as a whole. That designation covers the department, not Artificial Intelligence and Machine Learning Technologies. No program-specific GAO finding for this line is in the ingested data. See the DOD oversight record.
Program dossier
No research dossier for this program — dossiers cover 50 of 1,938 programs, the largest fully J-book-detailed lines by FY2026 requested dollars. why no dossier here? →
Primary sources
Open any budget figure for its exact receipt. Verified line items lead with the highlighted government PDF; original spreadsheets download with their budget edition and exhibit in the filename.
Budget totals · TOA sources
FY2026 Department of Defense Budget: RDT&E Programs (R-1) · comptroller.war.gov
Exhibit R-1 · J21
Complete workbook · unchanged saved copy. Saves as PB2026_DoD_R-1_Research-Development-Test-Evaluation.xlsx.
FY2022 Department of Defense Budget: RDT&E Programs (R-1) · comptroller.war.gov
Exhibit R-1 · J19
Complete workbook · unchanged saved copy. Saves as PB2022_DoD_R-1_Research-Development-Test-Evaluation.xlsx.
FY2023 Department of Defense Budget: RDT&E Programs (R-1) · comptroller.war.gov
Exhibit R-1 · J20
Complete workbook · unchanged saved copy. Saves as PB2023_DoD_R-1_Research-Development-Test-Evaluation.xlsx.
FY2024 Department of Defense Budget: RDT&E Programs (R-1) · comptroller.war.gov
Exhibit R-1 · J20
Complete workbook · unchanged saved copy. Saves as PB2024_DoD_R-1_Research-Development-Test-Evaluation.xlsx.
FY2025 Department of Defense Budget: RDT&E Programs (R-1) · comptroller.war.gov
Exhibit R-1 · J19
Complete workbook · unchanged saved copy. Saves as PB2025_DoD_R-1_Research-Development-Test-Evaluation.xlsx.
Detailed budget justification
The related TOA spreadsheets above use a different accounting basis from R-2/P-40 detail. Their amounts are not assumed to match these detailed sources.
RDTE - Vol 1 - Budget Activity 2 · www.asafm.army.mil
PDF page 333
RDTE - Vol 1 - Budget Activity 1 · www.asafm.army.mil