COMMENTARY
T
he concept-development and acquisition communities have long treated artificial intelligence and machine learning (AI/ML) as speculative future technologies for next generation military systems, but the Army can no longer ignore the problems of procuring and supplying AI/ML models in
current military systems. As military transformation expert, Peter W. Singer, noted in his April 2024 article, “Te AI Revolution is Already Here,” that “Te battlefield appli- cations of AI are quickly expanding from swarming drones to information warfare and beyond.” Tough the U.S. Army has already started awarding contracts for AI, most of these contracts are for large monolithic platforms and unified systems such as Palantir Foundry, reported on by Lindsay Clark in her article “Palantir wins U.S. Army contract for battlefield AI,” in March 2024. Tese systems do serve an essential purpose for the enterprise as a whole, but they also leave a critical gap in the acquisition of individual AI/ML models for narrowly scoped systems. Te war in eastern Europe is revealing that the pace of adaptation in contemporary large-scale conflicts will require the rapid development and procurement of modular AI-enabled systems, such as first-person view drones and tactical dashboards, as described in Mick Ryan’s February 2024 arti- cle “Russia’s Adaptation Advantage.”
Based on our experience developing and deploying Griffin Analytics, a maintenance management and predictive logistics application currently employed by Army Aviation, we advocate two forward thinking research and development processes that together will enable affordable and adaptable procurement of AI/ML systems. First, the Soldier-led Agile software development being pioneered by the Army Artificial Intelligence Integra- tion Center (AI2C) and the Army Software Factory produces Army owned code that can integrate AI/ML models, while avoiding the vendor lock of monolithic systems. Second, the model-quality simulation framework being developed by cadets and faculty at the United States Military Academy Department of Mathematical Sciences enables AI/ML performance metrics to be translated into operational terms and establish specific bench- marks for individual models. Together, these processes would allow the procurement of AI/ML models to be more like ordering parts or components that improve existing Army processes, than the purchase of major end items.
ARMY ARTIFICIAL INTELLIGENCE INTEGRATION CENTER As a direct report to Army Futures Command (AFC), AI2C plays a pivotal role in inte- grating AI/ML technologies into Army operations, driving innovation and adaptability. AI2C executes the Soldier-led Agile software development process, which is crucial for several reasons:
• Army-Owned Code: By developing software in-house, AI2C ensures the Army retains ownership and control over its codebase. Tis reduces reliance on external vendors and mitigates the risk of vendor lock-in, which can limit the military’s flexibility in adapting to new technologies or changing operational needs.
• Modular AI/ML Systems:Te Agile development approach enables the creation of modular AI/ML solutions that can be integrated into various military systems. Tis flexibility allows the Army to deploy AI/ML models that are narrowly scoped to specific tasks, such as predictive analytics or decision support, facili- tating rapid response to evolving challenges.
• Agile Response: Te Soldier-led Agile development model promotes iterative and incremental improvements, allowing AI2C to refine its solutions based
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