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Undersea Artificial Intelligence / Machine Learning (AI/ML)

Category: RDT&E • Line Item: 0304797N • FY26 Budget Request: $29.3M

Overview

Budget Account
1319N - Research, Development, Test and Evaluation, Navy
Budget Activity
04 - Advanced Component Development and Prototypes
Previous Year
Description

The Undersea Artificial Intelligence/Machine Learning (AI/ML) Program (PE 0304797N) is a newly established initiative within the Department of the Navy, designed to advance undersea warfare capabilities through the application of artificial intelligence and machine learning technologies. This program is aligned with the requirements of the Office of the Under Secretary of Defense for Intelligence and Security (OUSD(I&S)) for the Military Intelligence Program (MIP). Its primary goal is to leverage AI/ML to automate and enhance data processing in undersea environments, with a particular focus on improving the Navy's ability to gather, analyze, and interpret undersea acoustic data for surveillance and operational decision-making.

The program's objectives are centered on research, development, testing, and evaluation (RDT&E) of AI/ML tools that support undersea warfighting missions. By automating repetitive data analysis tasks and reducing human error, the program aims to increase operational efficiency and productivity. The initiative also seeks to transform the detection and identification of undersea noise, which is critical for the Navy's undersea surveillance systems. This transformation is expected to provide a significant technological edge in undersea situational awareness and threat detection.

A key component of this program is the development and integration of AI/ML capabilities. Funding supports a full spectrum of activities, including research, software development, capability maturation, and software testing. The project also covers operational and maintenance efforts, with resources dedicated to civilian, military, and contractor personnel required for successful execution.

Within this effort, resources are allocated for several critical activities. These include AI/ML research and development focused on the creation and refinement of algorithms and data processing techniques tailored to the undersea domain. Additional support is provided for AI/ML capability development, encompassing the integration and operationalization of these technologies into Navy systems. Developmental Test & Evaluation (DT&E) ensures that new capabilities meet performance and reliability standards before fielding.

The program also includes a management services component, supporting project oversight, coordination, and administrative functions necessary to maintain alignment with broader Navy and Department of Defense objectives. The establishment of a dedicated MIP Program Element/Line Item (PE/LI) is intended to structure an effective resourcing strategy, prevent duplicative efforts across the Navy, and ensure accurate accounting of all undersea AI/ML investments.

Budget adjustments for FY 2026 reflect a net decrease from the previous submission, primarily due to rate adjustments and efficiency initiatives in line with Executive Orders 14222 and 14210, which focus on government cost efficiency and workforce optimization. Despite these adjustments, funding increased from FY 2025 to FY 2026 to further support AI/ML data processing development and software testing efforts.

Budget Trend

Undersea Artificial Intelligence / Machine Learning (AI/ML) Research Development, Test & Evaluation Programs (0304797N) budget history and request


Interactive stacked bar chart for exploring the Undersea Artificial Intelligence / Machine Learning (AI/ML) budget
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2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Enacted Requested
$0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $28,563,000 $29,308,000
The DoD did not provide line item forecasts in its FY26 budget request, see the prior year budget for any forecasted years
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FY2026 Defense Budget Detail

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FY2026 Budget Released: 06/30/25