The United States Army’s ability to deliver precision fires and effects is fundamentally tied to its doctrinal targeting methodology: decide, detect, deliver, assess (D3A).
Field Manual 3-60, Army Targeting prescribes the use of D3A as an integrative approach requiring cooperation across multiple warfighting functions.
As the Army advances under the pressures of multidomain operations as its operational concept, optimizes its contributions to US strategic competition with near-peer adversaries, and pursues its recently announced transformation initiative, the necessity of integrating artificial intelligence into targeting workflows is paramount.
AI technologies have already proven their utility across a range of defense applications, including intelligence, surveillance, and reconnaissance processing, decision support, and autonomous systems operations.
Over the past several years, a growing body of academic research has explored these capabilities, yielding insights with significant implications for military policy and doctrine.
Key takeaways from this body of work include:AI in targeting presents a moral dilemma—it must be employed as a tool, not as a substitute for the warfighter’s judgment.
Time is the most compelling performance metric for evaluating AI effectiveness in the targeting process.AI offers undeniable scaling advantages, particularly in data processing and decision acceleration.
Human commanders must remain the final arbiters of lethal force, preserving the principle of human-on-the-loop decision-making.
AI should augment—not replace—critical targeting functions, such as rules of engagement validation, proportionality assessments, and determinations of military necessity.
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