The new frontline: Winning the information war at the tactical edge

Military leaders overseeing operations in the Indo-Pacific face a daunting logistical puzzle. With forces dispersed across a vast theater that includes potential flashpoints like Taiwan in the South China Sea, ensuring that every base, ship, and unit has the right personnel, equipment, and supplies is a monumental task. That requires enormous intelligence at the tactical edge—and increasingly, the use of artificial intelligence (AI) to speed up decision-making.

Traditionally, that meant collecting and sending data back to command facilities in Hawaii or the continental U.S. for analysis and response. But in fast-changing operational environments, that approach is quickly becoming outmoded and unreliable.

This scenario highlights both the challenge commanders face and the strategic shift underway across the military. The decisive advantage no longer rests solely on the movement of troops and materiel—but on the ability to move and process information faster, more securely, and with greater operational relevance than adversaries.

Achieving that kind of information advantage means being able to deliver real-time insights to warfighters in the field—especially in environments where communications are disconnected, disrupted, intermittent, or limited (DDIL). This isn’t just a technical upgrade; it’s a strategic imperative.

Underlying this shift is the growing expectation that actionable intelligence will reach those on the front lines faster than it reaches our adversaries. That expectation is driven in no small part by the commercial experience most consumers have become accustomed to – e.g., the ability to track deliveries en route and notifications when they arrive.  

Conflict planning and logistics in contested DDIL environments are obviously more complicated, which is all the more reason why the advantage lies with those who have an information advantage. That requires assessing, processing, and disseminating vast amounts of data quickly at the edge.

Gaining the data edge

“In many regards, data is the five-five-six round of the next war,” said John Sahlin, vice president for defense cyber solutions at General Dynamics Information Technology (GDIT), referring to the standardized rifle cartridge used by NATO forces. “It has become the lynchpin to enhance the decision-making process for advantage.”

That advantage depends on more than just collecting data. It requires turning it into usable intelligence faster than adversaries can react.

“The core problem is latency,” explained Matt Ashton, partner customer engineer at Google Public Sector. “Until recently, the immense volume of data from sensors, drones, and logistical trackers required the processing power and AI available primarily in distant cloud computing centers.”

“Our DOD customers struggle with the current status quo at the edge because they can’t run true AI,” said Ashton. “So data has to get sent back to the mother ship to crunch the data and get a resolution. The massive differentiator now is our ability to provide AI at the edge.”

According to both industry experts, the solution lies in a combination of powerful, ruggedized edge computing platforms and AI models specifically engineered for defense use that can operate independently, even when completely disconnected from high-capacity networks.

Google, for example, provides this capability through its Google Distributed Cloud (GDC), a platform designed to bring data center capabilities to the field.

“GDC was built to run so it never has to ‘call home.’ It can sit on the Moon or a ship. It doesn’t have to get updates,” Ashton said. “It’s a family of solutions that includes a global network, but also features an air-gapped GDC box that connects to the Wide Area Network and other on-prem servers not on the internet.”

This allows commanders on submarines, at remote bases, or in forward-deployed positions to run AI and analytics locally and process vast sensor data streams in-theater without waiting on external links.

Why mission-specific AI models matter

However, raw computing power is only part of the equation. Commercial AI models often lack a nuanced understanding of military operations. This is where operationally relevant AI models developed by GDIT that translate raw data into relevant, actionable intelligence are crucial.

Sahlin compared the role of mission-specific AI models to a speedometer in a car. “What it measures is the revolutions per minute of the axle. What it reports is how fast you’re going in miles per hour,” he explained. “That’s the kind of insight that only comes from real-world familiarity with military operations.”

“A clear grasp of operational objectives is key to developing models that are tuned to real-world demands of each mission,” said Sahlin. “So that may mean multiple mini-models to translate data into relevant insights.”

Sahlin also explained why applications built on an open data architecture model are crucial to adaptability at the edge.

“The real value of an open data architecture, particularly in the defense industry, is that it’s a very decentralized platform. Logistics is a classic example of commercial, local, last-mile delivery providers working with many sources. In the military, you won’t have a single source or model. This is where open architecture is critical.”

Security remains foundational to all of this. Sahlin noted that while the military can benefit from commercial innovation, it still needs to ensure higher levels of security than commercial operators. So it’s also essential that the military’s AI development partners have a deep understanding of the Defense Department’s zero trust security practices and requirements, which apply to the broader base of defense suppliers in the DOD’s supply chain.

“GDIT’s value lies in its longstanding experience supporting defense missions,” Sahlin said. “We work with clients to gather the right data, build tailored models, and deliver intelligence to the edge, even in DDIL conditions where units may be disconnected or intentionally silent.”

Looking ahead

By combining a platform like GDC with mission-specific AI models from GDIT, military logistics teams can move from reactive support to proactive planning, anticipating needs, reallocating resources, and outmaneuvering adversaries.

As operational demands grow more complex and communications become more contested, defense leaders say gaining an information advantage at the edge isn’t just important, it’s essential for mission success.

Learn more about how GDIT and Google Distributed Cloud can help your organization deliver at the edge more proactively.

This article was sponsored by GDIT and Google Cloud.