Accelerating to Machine Pace: Understanding the Power and Constraints of Artificial Intelligence
The United States Army is revolutionising its targeting processes by integrating artificial intelligence (AI) into its D3A methodology, a systematic approach that consists of Decide, Detect, Deliver, and Assess. This move aims to enhance decision-making speed, accuracy, and operational synchronisation in the battlefield.
Integrating AI into D3A
In the Decide phase, AI assists commanders by rapidly analysing terrain, weather, enemy disposition, and both historical and live intelligence data to generate and refine courses of action (COAs). This AI-enabled planning integrates deception and synchronises operations vertically and horizontally across command echelons within seconds, a task that would take humans significantly longer.
The Detect phase utilises AI-powered surveillance and reconnaissance tools to continuously monitor the battlespace. For instance, AI systems like the Scylla platform have demonstrated high accuracy in identifying threats by analysing sensor data in real time, allowing quicker detection of hostile targets.
During the Deliver phase, AI assists strike platforms by providing precise targeting information, optimising timing, and integrating manoeuvres into coordinated plans that include nonlethal fires, traffic control, and command post movements to enhance effectiveness and minimise risk.
In the Assess phase, AI conducts rapid war-gaming and assessment of COAs against enemy models, continuously refining plans through hundreds of iterations in seconds. This facilitates near real-time battle damage assessment and supports adjustments to operations.
Benefits of AI in Targeting
The integration of AI offers several advantages, including speed and scale, improved accuracy, synchronisation and integration, and an asymmetric advantage. AI performs complex analyses and iterative war-gaming orders of magnitude faster than human teams, compressing what traditionally takes hours or days into minutes or seconds, thereby enabling faster and more informed decision-making. Enhanced threat detection with AI reduces false alarms and increases identification reliability, leading to better targeting decisions and fewer collateral effects.
AI can coordinate a wide range of battlefield functions, ensuring a unified approach to targeting and operational execution. By exploiting AI-driven targeting effectiveness and integrating data poisoning offensives against adversary AI systems, the U.S. secures an edge in electronic and cognitive warfare domains, undermining enemy targeting systems covertly.
Challenges of AI in Targeting
Despite the benefits, the integration of AI into targeting operations presents several challenges. Data vulnerabilities make AI systems susceptible to adversarial data poisoning that can cause misclassification or malfunction, threatening the reliability of targeting and decision systems. Ethical and legal considerations require the military to ensure AI use complies with the law of armed conflict and ethical guidelines, especially when relying on autonomous decisions in lethal targeting.
Operational complexity arises when integrating AI into multi-domain operations, requiring careful human oversight and rigorous testing to avoid errors or unexpected AI behaviour in dynamic, unpredictable operational environments. The human-machine interface necessitates that commanders maintain situational awareness and judgment, ensuring AI-generated plans remain aligned with strategic objectives and rules of engagement.
In conclusion, the Army's incorporation of AI into D3A is enabling faster, more precise targeting cycles by automating planning, detection, and assessment while posing challenges related to data security, ethical use, and complexity that require vigilant management and human oversight.
This article represents the author's views and does not reflect the official position of the United States Military Academy, Department of the Army, or Department of Defense.
References:
- Crifasi, J. R. (2021). Artificial Intelligence and Targeting: Ethical, Legal, and Doctrinal Considerations. Naval Engineers Journal.
- Crifasi, J. R. (2022). Artificial Intelligence and Targeting: A New Era in the D3A Methodology. Military Review.
- Crifasi, J. R. (2023). AI in Targeting: Augmenting, Not Replacing, Critical Functions. Armor.
- Crifasi, J. R. (2024). AI in Targeting: A Modular and Doctrinally Grounded Approach. Journal of Electronic Defense.
National security is bolstered by the Army's integration of artificial intelligence (AI) into its D3A methodology, as AI aids in generating and refining courses of action, quicker threat detection, precise targeting, and rapid war-gaming and assessment. This technological advancement offers an asymmetric advantage by compressing decision-making processes and improving operational synchronization. However, it presents challenges such as data vulnerabilities, ethical and legal considerations, and operational complexity that demand vigilant management and human oversight. The military must ensure AI use in lethal targeting complies with the law of armed conflict and ethical guidelines, and maintain situational awareness and judgment to avoid misclassification, malfunction, or errors in dynamic, unpredictable operational environments.
