What happens when the guns and missile launchers of a modern battlefield far outnumber the human hands available to man them?
The U.S. Army is confronting that question head-on as it explores using artificial intelligence to staff artillery and air-defense units — not replacing soldiers outright but augmenting or even taking on duties traditionally performed by humans. The attraction is obvious: faster sensor fusion, quicker target identification, and persistent operations that would strain any crew. The obstacle is equally stark. Army leaders say the problem is that the technology is nowhere near what it needs to be.
The problem has several parts. The Army faces force-shaping challenges, including recruiting and retention shortfalls and the operational tempo of near-peer competition, that make full manning of some units difficult. At the same time, the threat environment — from long-range precision fires to swarms of unmanned aerial systems — demands reaction times and coordination well beyond human capacity in some scenarios. That mismatch is driving interest in AI-enabled systems that could assist with targeting, sensor management, electronic warfare awareness and decision support in air defense and indirect fires.
But experimenting with AI in combat functions is not the same as fielding mature systems. Across recent public reporting and discussions with defense leaders, the recurring refrain is caution: prototypes and pilots have produced promising demos; operationalizing them reliably under contested, degraded and adversarial conditions has proved elusive. The Army’s leaders and technologists repeatedly point to brittle algorithms, sparse or biased training data, opaque decision-making, and vulnerability to cyber and electronic attack as core limitations.
The technical challenges are practical and thorny. Effective artillery and air-defense operations require rapid, accurate fusion of multiple sensor streams — radar, electro-optical/infrared, signals intelligence — and the ability to discriminate friend from foe in cluttered environments. AI models need huge volumes of labeled, mission-representative data to learn those distinctions. That data is often classified, sparse for the most relevant scenarios, or simply nonexistent for novel threat types. Models trained in clean lab settings can fail when sensors degrade, networks jam, or adversaries deliberately feed false signals.
Robustness matters enormously. An adversary intent on defeating an AI-enabled system can exploit known weaknesses: spoofing, denial-of-service, adversarial perturbations to input sensors, or deceptive tactics that confuse automated pattern recognition. Ensuring resilience requires secure architectures, redundant sensors, adversarial testing, and the ability to degrade gracefully to human control — all engineering and doctrinal work that remains incomplete.
Human factors add another layer. Soldiers must trust any AI assistant enough to follow its guidance in life-or-death moments. That trust depends on transparent behavior, predictable failure modes, and the ability of crews to understand why an AI recommended a particular engagement. The explainability of many modern AI methods — especially deep learning — is limited. Training programs, new operating procedures, and careful human-machine teaming constructs are essential. Absent those, units may either over-rely on automation or reject useful tools out of fear.
The legal, ethical and policy frameworks are being shaped in parallel. The Department of Defense has established AI ethical principles and governance structures intended to ensure human judgment remains central to decisions involving the use of force. Implementing those principles in the heat of combat raises practical questions about command responsibility, rules of engagement and the latitude for autonomous action. Policymakers must balance operational need with legal constraints and public accountability.
Procurement and testing cycles present a further hurdle. The acquisition system that governs major Army buys is deliberately deliberate, designed to provide assurance and compliance. AI systems — especially those that change behavior through learning or require rapid software updates — strain conventional procurement models. Testing must move beyond laboratory acceptance tests to red-team, live-fire and joint-exercise validations that simulate degraded communications, contested spectrum, and real adversary tactics. That costs time and money.
Technologists stress that progress is real but incremental. Advances in edge computing, sensor miniaturization and model optimization have enabled more capable inference at the platform level. Simulation and synthetic data generation can accelerate training where real-world data are lacking. Still, practitioners caution that AI performance on curated benchmarks does not guarantee battlefield readiness. The field is still working toward standardized metrics for reliability, interpretability and failure rates that commanders can trust under pressure.
From the policy vantage, the calculus is twofold. Rushing immature systems into service could produce dangerous mistakes with strategic consequences; waiting too long risks operational inferiority if adversaries field effective AI-enabled counterfire and air-defense systems. Strategists watching near-peer rivals note how both Russia and China have invested heavily in integrating autonomy and machine assistance into their fires and air-defense concepts. That competitive dynamic raises the stakes for the U.S. but does not eliminate the need for prudence.
Soldiers on the ground — the users — voice practical concerns. They want tools that reduce workload without adding opaque layers of automation that can fail without warning. Their priorities include reliability, clear interfaces, simple modes for human override, and training that matches the realities of high-stress tactical environments. Any AI that increases cognitive load or requires constant patching will be resisted, regardless of its advertised capabilities.
What would responsible adoption look like? Several pragmatic steps are already echoed in defense research circles and exercises:
/ Incremental deployments that keep humans in the loop for targets-of-opportunity and critical engagement decisions
/ Rigorous, scenario-based testing including red-teaming, adversarial inputs and contested electromagnetic environments
/ Investment in secure, resilient edge compute and hardened sensor fusion architectures
/ Standardized metrics for reliability, explainability and failure modes that feed acquisition and operational decisions
/ Expanded synthetic and transfer-learning approaches to address data scarcity while increasing real-world validation
These steps imply patience and resources. They also imply an acceptance that AI will not be a silver bullet but a force multiplier when integrated thoughtfully into doctrine, training and sustainment. The alternative — pushing immature automation into the field because of personnel pressures — risks eroding trust, incurring avoidable losses and creating exploitable weaknesses for adversaries.
Ultimately, the Army’s effort is a microcosm of a broader tension facing militaries worldwide: the promise of faster, more precise operations enabled by AI versus the limits of systems that must perform under duress and deception. Getting it right requires not only better algorithms but better data, better testing, and better governance. It will also require clear-eyed honesty about where the technology still falls short.
If the goal is to ensure that artillery and air-defense systems remain effective in the coming decades, the Army must answer a basic question: can commanders accept the risk of delegating critical decisions to algorithms before those algorithms demonstrate battlefield-grade reliability? The stakes could not be higher.
Source: https://www.defenseone.com/technology/2025/10/army-wants-ai-help-man-artillery-and-air-defense-units/408778/
