Last year, Army Secretary Daniel Driscoll convened around two0 top executives from some of the nation’s leading technology and industrial companies to help the service determine how industry could best support the Army’s push to advance some of its most critical artificial intelligence initiatives.
The Army presented industry with a series of operational scenarios, asking companies how they would address the Army’s key challenges in these areas.
“Many of them really boil down to how do we become more data-driven and lessen the cognitive burden on operational units, especially in priority theaters, where limited bandwidth or denied environments will be an issue in a large-scale combat situation,” Deputy Under Secretary of the Army David Fitzgerald told Federal News Network.
“How do we overcome the tyranny of distance in the digital realm? That’s where most of the scenarios are focused — they were at a classified level, but that’s generally what we were examining: How do we speed up decision making, reduce cognitive burdens on our warfighters by introducing best-in-class, cutting-edge AI capabilities,” he added.
From that tabletop exercise at the Pentagon emerged the Army’s Rapid Implementation of Artificial Intelligence initiative, or ARIA, which is structured around three core efforts — building a “model armory” to deliver AI tools to soldiers at the tactical edge, embedding AI into the Army’s planning, programming, budgeting and execution (PPBE) process, and creating a digital twin of the service’s industrial base to enable a more efficient, AI-powered supply chain.
Around 20 companies took part in the exercise, and while most remain involved in some capacity, approximately 11 companies are deeply engaged in the initiative’s lines of effort.
“Some of them are focused on a single use case. Several of them are cross-cutting. But even the ones that didn’t have a specific role on one of the use case teams, many of them are contributing through enterprise advisory — they’re helping self-organize and coordinate the industry partners,” Fitzgerald said.
Automating the PPBE Process
Team Grey, led by Col. Patrick Workman, aims to identify which elements of the PPBE process are most ready for AI automation now, while also laying the foundation for more advanced capabilities in the future. One of the biggest hurdles, Fitzgerald said, is the Army’s data landscape — many business systems remain isolated, outdated and dependent on manual data entry. As a result, the service had to invest significant time cleaning up its data before it could begin leveraging AI.
“We needed to move those databases around, consolidate some business systems into a cloud-based environment that AI could actually access. And that produced a kind of secondary benefit of retiring a number of legacy systems, which has already saved us several million dollars, and we’re retiring 33 additional systems, merging another 12, and that consolidation is going to save nearly $100 million in fiscal 2026 and roughly another $70 million in 2027. So this is an effort that’s somewhat self-funding in many ways,” Fitzgerald said.
Beyond fixing its underlying data, the Army is also trying to address one of the most challenging aspects of the PPBE process — while service leaders can see how funds are distributed, they often lack insight into why those decisions were made or what impact those choices might have across the force.
Team Grey is working to change that by building tools that allow senior leaders to quickly run “what if” scenarios — for example, how changes in end strength could affect munitions procurement capability. Currently, that kind of analysis is done manually and can take weeks.
“One example of how this has already been applied is we were able to examine the Army activities and resourcing at one of our contingency locations in Africa to help inform basing decisions. And we did a comparison where we used the AI tool, and it was able to pull that information within a few minutes. In contrast, that took three resource managers and about two weeks to gather the same information, and the results were very similar. The AI was probably a bit more accurate in the final analysis. I think that was a real selling point for the enterprise,” Fitzgerald said.
‘Model Armory’
Meanwhile, Team Black focuses on delivering AI capabilities directly to soldiers at the tactical edge through a “model armory” — a shared repository of tools that troops can access on demand and customize for specific missions.
“You can think of it as an arms room where you go and check out the weapon that you need,” Fitzgerald explained.
“For instance, a user could request a computer vision model that’s searching for specific friendly or enemy vehicle signatures in a particular operating area with current weather conditions to achieve better results. And then that model continues to be hosted in that armory so other units can leverage the best-in-class option and then adapt it for their specific operational needs,” he added.
WebAI, one of the participating companies, for example, helps provide the infrastructure needed to evaluate the performance of AI models and deliver them to users in the field.
“One of the biggest challenges is that the cloud over the last 30 years has become the repository for everything. When you don’t have access to the cloud, all of the orchestration services for cloud capabilities work really well in the cloud, but they don’t scale effectively to the edge. So the ability for us to provide cloud-like services to support things like model orchestration, agentic workflows in denied environments, peer to peer, requires building out an entirely different network that allows for a different topology through which to share and build applications, share and distribute or receive data. The backend infrastructure for this is actually quite complex, and it doesn’t really exist widely in the military today,” Fitzgerald said.
“For both the government and commercial sectors,” noted Jason Rathje, president of public sector at webAI.
“Technologies that perform efficiently in cloud environments often struggle when deployed at the edge. This requires purpose-built edge solutions. The advantage is that once these challenges are solved, the resulting architecture can effectively scale back into cloud environments as well,” he explained.
Reengineering supply chain operations
Yellowstone Team under Colonel Matt Alexander’s leadership is applying artificial intelligence to overhaul the Army’s supply chain while modernizing its outdated industrial infrastructure. The initiative begins with creating digital replicas of operations at Anniston Army Depot. Many military depots continue to use manual, antiquated systems that restrict access to component information and maintenance requirements.
The project focuses on structuring this data to establish instantaneous supply-demand monitoring, helping the Army enhance transparency, eliminate delays, and accelerate procurement decisions regarding manufacturing versus purchasing.
Each team operates on different timelines, with implementation proceeding in stages, all working toward complete operational readiness within twelve months.
“Looking ahead, we envision integration and expansion. These three parallel initiatives will eventually merge into a unified, comprehensive effort,” Fitzgerald remarked.
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