Army leaders say the modern battlefield is so saturated with sensors and networked weapons generating more data than soldiers can realistically process on their own that artificial intelligence is needed to meaningfully sort it all.
For years, the Army’s focus was on fielding more sensors for battlefield information and awareness, but now the service is also having to think about information overload and managing the massive amounts of data coming in.
During a recent US Army and NATO exercise in Europe, troops used a homegrown AI system to consume and sort data. The value wasn’t strictly that the AI could do it faster but rather that it could remember context and patterns that humans couldn’t.
The case from the Dynamic Front exercise is another example of how the US military is increasingly implementing AI and automation into everything from enemy attack simulations to paperwork.
“The modern battlefield, what we’re already seeing across the globe, it is swimming in sensors, and we are drowning in data,” Col. Jeff Pickler, the Army 2nd Multi-Domain Task Force commander, said at a media roundtable on Dynamic Front.
There aren’t enough people to decipher all the available information, he said. “They will never be able to fully process all of that.”
US Army photo by Kevin Sterling Payne
The software aimed at addressing that problem remains in beta testing. In the next iteration of Dynamic Front — which will merge with another exercise, Arcane Front, to pair technology experimentation with theater-level combat rehearsals — Army leaders say they intend to test the AI at a larger scale.
“If we’re looking at a target set in the European theater where we think we’re going to need to process upwards of 1,500 targets a day, that’s beyond the human scope,” Pickler said. “The answer to the equation there is in AI and automations.”
During a potential large-scale conflict in Europe, AI could assist in locating and assessing those targets.
The system can do this quickly, but the speed isn’t the main benefit. AI can remember patterns that humans might forget or not even notice. Pickler gave an example of AI realizing that unrelated shipping reports, a local power outage, and a fertilizer delivery together might suggest missile fueling activity.
“So the difference isn’t seconds versus minutes — it’s minutes instead of months. Not because the machine scans quickly, but because it keeps context across sources that humans can’t hold in memory,” Pickler said after the roundtable.
“It doesn’t replace analysts by reading faster,” he said, “it replaces the weeks analysts spend reconnecting information spread across thousands of reports.”
US Army photo by Capt. Regina Koesters
In a conflict scenario, that could mean analysts reach a clearer picture of the battlefield faster. Correlations between data gathered from different sensors could surface more quickly. If an adversary were fueling, arming, or moving weapons in ways that were not immediately obvious, AI could help flag those links.
Humans, though, would still decide how to respond.
Soldiers have seen success with iterating on the current AI model, the Army said. It’s been retooled during testing, and humans remain in the loop, reviewing outputs at multiple stages.
The goal is to continue increasing the overlap the model would have with human-produced information. In a targeting example, a milestone would be if AI achieved 90 to 95% agreement with humans on 100 target sets.
The Army’s push for AI and automation is also driving the development of its Next Generation Command and Control software, a priority initiative.
The technology being developed by vendor teams including Anduril, Palantir, and Lockheed Martin uses AI and machine learning to provide commanders and soldiers with real-time data on ammunition levels, maintenance needs, intelligence feeds, targeting, and simulated enemy attacks.
But AI is also changing other aspects of how the Army works. Autonomous features in drones, weapons, and targeting might be at the forefront, but behind the scenes, personnel are using new tools, redesigned workflows, and data integration for recruiting, maintenance, and inventors. These are manual tasks that the service believes can be improved with AI.
