Rapid-fire AI Capabilities and Limitations: Exploring the Realm of Artificial Intelligence
The United States Army is exploring the integration of Artificial Intelligence (AI) into its D3A (Decide, Detect, Deliver, Assess) targeting methodology as a means to streamline the targeting process while maintaining moral and legal responsibilities.
The integration of AI is designed to optimize the targeting workflow, accelerating data processing and decision-making. This allows the Army to operate at "machine speed" while managing complex multidomain operations. AI enhances critical targeting functions, such as intelligence processing, decision support, and autonomous operations, providing scaling advantages and improving the speed and efficiency of target identification and engagement.
However, accountability and ethical considerations are paramount. Human commanders remain the final decision-makers, ensuring human-on-the-loop oversight to validate rules of engagement, proportionality, and military necessity. This balance ensures AI acts as a tool to augment—not replace—human judgment in lethal targeting decisions, sustaining moral and operational control within the Army's integrated warfighting functions.
Emerging programs such as the Israeli AI-enabled system "the Gospel" and the US Department of Defense's Project Maven reflect a growing desire to accelerate targeting cycles. Incorporating AI into D3A is about more than just speed; it's about optimizing sensor-to-shooter kill chains, reducing cognitive burden, and improving commanders' decision-making in contested environments.
The Army is advancing its operational concept under multidomain operations and pursuing a transformation initiative. AI in targeting presents a moral dilemma, requiring it to be employed as a tool, not as a substitute for the warfighter's judgment. AI technologies have proven utility in defense applications, including intelligence, surveillance, and reconnaissance processing, decision support, and autonomous systems operations.
Achieving the targeting effect destroy beyond physical damage requires a comprehensive understanding of time components and the target's inability to fulfill its primary function for the remainder of a mission. In the decide phase, tools such as game theory models, decision trees, and logistic regression algorithms can support enemy course-of-action development, attack asset prioritization, and effects determination.
During detect, AI excels at target recognition via pattern association and anomaly detection, leveraging sensor fusion and deep learning. In the assess phase, battle damage estimation benefits from clustering models and explainable AI tools that support image interpretation and effects validation. For deliver, optimization algorithms and prescriptive analytics can refine weapon-target pairing and target engagement timings.
However, a central question the Army has yet to answer is: Can AI enable the D3A cycle to achieve faster, more reliable, and more effective targeting—while preserving accountability through human oversight? A modular and doctrinally grounded approach is required to adapt AI to the D3A methodology, mapping AI capabilities to discrete phases of the targeting cycle and identifying value-added contributions to each step.
Jesse R. Crifasi, a retired US Army chief warrant officer 4, former division artillery targeting and division field artillery intelligence officer for the 82nd Airborne Division, a senior advisor in the defense industry specializing in joint fires and targeting, and a PhD student in public policy and national security at Liberty University, has authored multiple doctrinal and technical assessments on digital fires and artificial intelligence integration in targeting operations. Crifasi emphasizes the importance of human commanders remaining the final arbiters of lethal force, preserving the principle of human-on-the-loop decision-making. The goal is to embed AI where it adds the most value while ensuring humans remain central at key decision points.
Current large language models (LLMs), such as Meta's LLaMA, lack understanding of doctrinal terminology and contextual nuance. The Army continues to rely on D3A as the doctrinal cornerstone of fires and effects integration at the brigade and division levels. AI-enabled targeting doctrine must codify decision points where human intervention is not just preferred—but required.
Sgt. Rebecca Watkins is the image credit for the article. The views expressed are Crifasi's own and do not reflect the official position of the United States Military Academy, Department of the Army, or Department of Defense. Time is the most compelling performance metric for evaluating AI effectiveness in the targeting process. AI offers scaling advantages, particularly in data processing and decision acceleration.
However, as the Army moves forward with integrating AI into its targeting methodology, it must ensure that this technology is used responsibly, maintaining the principle of human-on-the-loop decision-making and preserving accountability and ethical considerations.
- The Army's exploration of Artificial Intelligence (AI) involves integrating it into the D3A targeting methodology for a more streamlined process while maintaining moral and legal responsibilities.
- AI is designed to optimize the targeting workflow, accelerating data processing and decision-making, allowing the Army to operate at "machine speed" in complex multidomain operations.
- AI enhances critical targeting functions, improving the speed and efficiency of target identification and engagement, and providing scaling advantages.
- Human commanders remain the final decision-makers in lethal targeting decisions, ensuring human-on-the-loop oversight to validate rules of engagement, proportionality, and military necessity.
- AI technologies have proven utility in defense applications, including intelligence, surveillance, and reconnaissance processing, decision support, and autonomous systems operations.
- Maintaining accountability through human oversight is a central question as the Army integrates AI into the D3A cycle to achieve faster, more reliable, and more effective targeting.
- As the Army moves forward with AI integration, it must ensure responsible use, maintaining the principle of human-on-the-loop decision-making and preserving accountability and ethical considerations.
