Artificial Intelligence and Machine Learning Advanced Technologies

FY2026 to FY2027 change Funding decrease reflects the strategic reallocation of resources to support evolving priorities and objectives.

FY2026 plans — current year Will directly integrate Android Tactical Assault Kit (ATAK)-adjacent software onto program of record (POR) support systems, most prominently Nett Warrior; optimize AI algorithms/models for autonomy, automated threat recognition, target geolocation, and auto adjust fire for effective use by POR ecosystems; mature drone autonomy and tasking options to enable Soldier control at a minimum cognitive burden; demonstrate the creation and support of advanced expendable, attributional drone designs to execute targeted reconnaissance and engagement tasks in field conditions; extend the infrastructure necessary to support effective AI model retraining, movement, and monitoring leveraging both edge and…

FY2025 accomplishments Provides modular sensor and computer hardware and integrates them onto two transitions platforms. Matures and demonstrates the functionality of low-level vehicle control and drive commands for the Small Multi-purpose Equipment Transport (SMET) and Remote Control Vechicle (RCV) using the Robotics Technology Kernel (RTK) By-wire-Kit (B-kit). Matures the existing, non-controlled, autonomy stack developed under the previous phases of this project to Robot Operating System (ROS) version 2.0 to ease the transition of autonomy modules to the Army's latest version of its controlled autonomy stack called Robotics Technology Kernel (RTK) that uses ROS 2.0 or selected module. Install and evaluate RTK…

FY2026 to FY2027 change Funding decrease reflects the strategic reallocation of resources to support evolving priorities and objectives.

FY2026 plans — current year Will optimize predictive maintenance application capabilities in a disconnected, denied, intermittent and/or with limited bandwidth (DDIL) environment through integration during deployments and training exercises; continue to utilize maintainer support device architectures; demonstrate applications that demonstrate a proof of concept for a design based on the network available for warfighters to access.

FY2025 accomplishments The project will mature and demonstrate the edge/cloud compute capability to experiment and develop progressive web applications that are able to operate in a Denied, Degraded, Intermittent, and Limited (bandwith) (DDIL) environment. These applications will provide functionality for the tactical unit collocated with the node and any other units connected to that node and will federate with the enterprise when connection is restored. This leverages work in support of the tactical data fabric and the Lower Echelon Analytics Platform Tactical (LTAC).

FY2026 to FY2027 change Funding decrease reflects the strategic reallocation of resources to support evolving priorities and objectives.

FY2026 plans — current year Will mature and demonstrate AI-enabled common operating picture through enhancements that surface machine learning/artificial intelligence (ML/AI) insights from the Sustainment, Intelligence, Fires, Protection, Movement and Maneuver, and Information Advantage warfighting functions.

FY2025 accomplishments Develop AI-enabled common operating picture that surfaces ML/AI insights from the Sustainment, Intelligence, Fires, Protection, Movement and Maneuver, and Information Advantage warfighting functions.

FY2026 to FY2027 change Funding decrease reflects the strategic reallocation of resources to support evolving priorities and objectives.

FY2026 plans — current year Will mature and demonstrate advanced algorithms for use by wider force and Operational Data Science Teams (ODSTs); build and support enhanced artificial intelligence enabled mission command information applications for the command post; conduct experiments to validate lower echelon analytic platform tactical data fabric.

FY2025 accomplishments Will mature and demonstrate advanced algorithms for use by wider force and Operational Data Science Teams (ODSTs) to build and support emerging artificial intelligence enabled mission command information applications for the command post. Validates emerging lower echelon analytic platform tactical data fabric.

FY2026 to FY2027 change Funding decrease reflects the strategic reallocation of resources to support evolving priorities and objectives.

FY2026 plans — current year Will continue maturation of foundational AI models and algorithms that integrate into simulation framework and create course of actions at the theater echelons; previously developed game theory and multi-agent reinforcement learning will be optimized to validate algorithm function, design, learning strategies, and utility functions so that AI systems enable model training.

FY2025 accomplishments Will mature and demonstrate game theory and multi-agent reinforcement learning and other foundational AI models and algorithms to integrate with an available simulation framework to create COAs at the theater echelons. Will optimize scenario criteria needed for the algorithm to function, design, and develop learning strategies and utility functions, and integrate the AI system into an available simulation suite to enable model training.

FY2026 to FY2027 change Funding decrease reflects realignment to consolidate data driven decision tools and related activities into one project.

FY2026 plans — current year Will demonstrate, and mature appropriate application(s) or system(s) leveraging emerging language-based AI technologies for mission command of operational forces.

FY2026 to FY2027 change Funding decrease reflects the completion of this effort.

FY2026 plans — current year Will further improve the ability to connect to additional external data sources, refine the ability to manage collaborative development of AI/ML solutions, and improve the visibility of telemetry data collected via the MLOps pipeline; develop additional capabilities to expand the scope of technologies supported for AI model development, operationalization, and testing.

FY2025 accomplishments Will mature and demonstrate scalable Machine Learning Operations (MLOps) at echelon. Improve and optimize data interfaces for multi-cloud data lake repositories and data mesh technologies. Demonstrate advanced tools for Artificial Intelligence (AI) test, evaluation, verification and validation, and the security of AI models.

FY2026 to FY2027 change Funding decrease reflects realignment to Program Element (PE) 0603463A (Network C3I Advanced Technology) / Project AO1 (UNT - Every Receiver is a Sensor Advanced Tech).

FY2026 plans — current year Will mature and demonstrate a system of applications that utilize AI technologies to identify targets of interest and develop algorithms that use multiple data sources to predict representation for novel object classes from a small number of novel class samples; optimize the fusion of visual, language, signal, and event-based information and semantic relationships to learn new objects and relationships and validate knowledge transfer from base classes to novel classes to reduce the time it takes to train AI algorithms.

FY2025 accomplishments AI Enabled Intelligence Fusion for Targeting will continue to provide a system of applications to identify targets of interest. This effort will further mature and optimize algorithms to predict representation of novel object classes from a small number of novel class samples, improving the AI algorithm learning capability and reducing the need for manual data input. Will continue to develop the use of visual, language, signal, and event-based information and semantic relationships to learn additional new objects and relationships and validate knowledge transfer from base classes to novel classes in order to reduce the time it takes to train AI algorithms. Will demonstrate the ability of…

FY2026 to FY2027 change Funding decrease reflects realignment to Program Element (PE) 0603463A (Network C3I Advanced Technology) / Project AO1 (UNT - Every Receiver is a Sensor Advanced Tech).

FY2026 plans — current year Will continue to mature data frameworks and data pipelines for fusion of intelligence data from multiple military intelligence systems; develop and optimize data frameworks and pipelines with infrastructure components that can implement machine learning algorithms across multiple AI domains.

FY2025 accomplishments In order to inform requirements for Project Linchpin, will continue to mature data frameworks and data pipelines for fusion of intelligence data from multiple military intelligence systems. Will continue to develop and optimize data frameworks and pipelines with infrastructure components that can implement machine learning algorithms across multiple AI domains.

FY2026 to FY2027 change Funding decrease reflects the strategic reallocation of resources to support evolving priorities and objectives.

FY2026 plans — current year Will provide the full spectrum of information necessary to integrate predictive logistics to include data streams for operations, personnel, and maintenance. Predictive modeling will focus on maturing predictive modeling techniques that increase decision making capabilities for the warfighter.

FY2026 to FY2027 change Funding decrease reflects the strategic reallocation of resources to support evolving priorities and objectives.

FY2026 plans — current year Will mature the architecture and implementation of data flows from the tactical edge to the enterprise; deliver predictive models from the enterprise back to the tactical edge based on modeling and decision requirements from warfighters.