The Pulse
Navy Puts Applied AI First on Five-Area Technology List
The U.S. Navy places applied AI at the top of a five-part technology priority list shared with investors and defense companies. The plan covers sensor fusion, autonomous behavior, cyber operations, quantum systems, networking, spectrum oper
AI.info Team ·
The U.S. Navy is putting applied artificial intelligence at the front of a five-part technology priority list intended to guide defense companies and investors toward capabilities the service expects to buy over the next several years.
Justin Fanelli, the Department of the Navy’s chief technology officer, shared the updated priorities in an interview published by TechCrunch on September 19, 2026. The list is not a funding commitment or a ranking of individual programs, but Fanelli said it is designed to give commercial companies a clearer signal about where their products could fit inside the Navy.
Applied AI Leads the Navy’s Five Technology Areas
The first category covers applied AI, including machine learning and increasingly agentic software that turns raw data into operational decisions. The Navy’s examples include sensor fusion, targeting support, autonomous behavior, and cyber operations built primarily through software rather than new hardware.
Four other categories follow: quantum information science; advanced networking; electromagnetic spectrum operations; and digital engineering and interoperability. The list reflects a focus on technologies that can operate across ships, remote locations, coalition networks, and contested communications environments.
Digital engineering and interoperability form the connective layer. The category includes open application programming interfaces, model-based systems engineering, and zero-trust architecture intended to reduce the custom integration work required whenever the Navy adds a new system.
Fanelli’s Investor Signal Is Also a Procurement Strategy
Fanelli told TechCrunch that the Navy’s demand signal has grown since it first published longer-term technology priorities. Investors told him the earlier list changed how they viewed the Navy’s buying plans, leading the service to issue an updated version after consultation with several venture investors.
“The cost of that, or the responsibility, is for us, if we’re not going to do it ourselves, to cast a cleaner signal,”
Justin Fanelli, chief technology officer, Department of the Navy
The approach is aimed partly at the financing gap between early research and the stage at which the Navy typically makes larger purchases. Fanelli said the service mostly buys from companies around Series D through Series F, while earlier Navy-backed research historically helped companies move through the seed-to-Series-B period.
Under the newer approach, commercial investors are expected to finance more of that development. The Navy can then buy mature products or work alongside private capital rather than funding every capability from the beginning.
Commercial Systems Are Already Replacing Delayed Programs
Fanelli cited several recent purchases to illustrate how the strategy works in practice. The Navy awarded a $562 million contract in September for the MQ-25 Stingray, an autonomous refueling aircraft designed to extend the range of carrier-based fighter jets.
The service is also buying edge-computing hardware from Armada for deployment aboard ships and at remote sites. Gecko Robotics is handling inspection work that previously required manual and hazardous processes, while Domino Data Lab is running the Navy’s machine-learning pipeline, according to Fanelli.
In another example, the Navy replaced a delayed shipboard camera program with commercial cameras and software from Applied Intuition. Fanelli said the change cut about four years from the schedule and allowed the system to reach more ships than originally expected.
Those examples show the distinction between a technology priority and a traditional weapons program. The Navy is not only seeking new platforms; it is looking for software, data infrastructure, and commercially developed systems that can improve existing operations without waiting through a full hardware development cycle.
The List Builds on Earlier Navy AI Directives
The updated priorities follow a formal Department of the Navy technology framework released in June 2025. That document listed AI and autonomy among five top-level priority areas and identified applied machine learning, natural-language processing, model verification, AI risk governance, autonomous mission platforms, edge AI infrastructure, and AI development pipelines as subareas.
The service has also adopted a broader AI strategy. In July 2026, Acting Secretary of the Navy Hung Cao approved the Department of the Navy’s Strategy to Weaponize Data and Artificial Intelligence, which calls for a data-ready force and faster operational decision-making. The Navy’s chief information office described the strategy as a roadmap for an “AI-first” fleet.
Separate research priorities show where the technology could be applied. Navy budget documents describe work on machine learning for shipyard maintenance scheduling, AI-assisted extraction of naval procedures from natural-language data, predictive maintenance for autonomous platforms, and onboard processing for mine-countermeasure missions.
Priorities Do Not Guarantee Contracts
The Navy’s five-area list carries an explicit limitation: it does not promise funding, select a particular vendor, or guarantee that every technology will survive the procurement process. The document also says priorities can change in response to near-term and long-term operational needs.
Fanelli said budget structure remains one of the main reasons promising technologies fail after entering the Navy. A new system must replace something the service already pays for, or the Navy may struggle to sustain it through long planning cycles.
That condition gives the applied-AI priority a practical test. Algorithms and agentic software may attract investment, but they will need to show that they improve decisions, reduce dangerous manual work, replace existing costs, or connect systems that currently operate apart. The Navy’s list makes those adoption targets more explicit than a general call for artificial intelligence.