One of the most persistent misunderstandings about AI-assisted targeting is the claim that humans have somehow been removed from the\u00a0loop,\u00a0decisions are made solely on AI recommendations, or strikes are approved in seconds without meaningful review.\u00a0The human factor has not disappeared. Humans\u00a0remain\u00a0indispensable to targeting. What has evolved is not the elimination of human involvement, but the rapid synthesis of intelligence with target acquisition. That distinction matters because many assume there is little or no review before a strike is done. However, no\u00a0systems make decisions independently. They supplement human decision-making by sorting and ranking information to generate recommended outcomes that must still meet rigid criteria. Analysts verify\u00a0intelligence,\u00a0legal teams conduct\u00a0reviews,\u00a0commanders make the final decision, and human beings\u00a0remain\u00a0responsible for the outcome.\u00a0<\/p>\n
Critics\u00a0often\u00a0point to\u00a0ambiguity<\/a>\u00a0in strategic-level U.S. directives. The Department of War\u00a0Directive 3000.09<\/a>\u00a0attempted\u00a0to regulate certain AI-enabled systems, though the technology at the time was far less sophisticated than it is today.\u00a0Military doctrine undermines the myth of autonomous targeting\u00a0as well. The Army\u2019s\u00a0FM 3-60<\/a>\u00a0frames\u00a0targeting as\u00a0an iterative command process and states that commanders\u00a0remain\u00a0the final approval authority for targeting activities and acceptable levels of risk. Machines may\u00a0assist\u00a0with detection, but they do not inherit command responsibility.\u00a0The result is that humans\u00a0remain\u00a0in the loop because targeting is still a command process, not an autonomous one. Military doctrine frames\u00a0targeting as\u00a0a cycle of deciding, detecting, delivering, and assessing, but commanders\u00a0retain\u00a0authority over acceptable risks. AI can compress and organize\u00a0the\u00a0data, but it cannot make strategic or moral judgments.\u00a0<\/p>\n AI models\u00a0remain\u00a0central to identity-based targeting and advanced decision support.\u00a0Open-source reporting<\/a>\u00a0indicates that sophisticated models, such as\u00a0Anthropic\u2019s Claude<\/a>, integrated into systems such as\u00a0Palantir designed Maven Smart System<\/a>, have enabled rapid conversion of vast amounts of intelligence, surveillance, and reconnaissance (ISR), signals intelligence, and behavioral data into target packages.\u00a0<\/p>\n Human productivity has\u00a0also increased. Tasks that once\u00a0required\u00a0weeks and\u00a0a\u00a0large staff can now be completed in minutes with fewer personnel. However,\u00a0speed\u00a0and efficiency\u00a0do\u00a0not\u00a0mean AI\u00a0independence.\u00a0It\u00a0does not change what Clausewitz described as the\u00a0\u201cgrammar of war.\u201d<\/a>\u00a0\u00a0<\/p>\n The most consequential shift in conflict today is the compression of time within the targeting cycle\u00a0and its integration into intelligence.\u00a0In the past, high-value or high-payoff targets were often missed because manual processes relied heavily on human operators and overwhelming amounts of ISR data.\u00a0Past conflicts reflected these limitations. During Operation Desert Storm, Iraqi mobile Scud launchers exploited delays by firing and\u00a0relocating\u00a0before U.S. forces could strike them.\u00a0Kinetic precision still\u00a0frequently\u00a0exceeds\u00a0intelligence fidelity. A munition could hit its coordinates perfectly while the underlying intelligence remained flawed.\u00a0The use of intelligence to target enemy combatants predates modern technology. AI did not invent decapitation strategies; it made them more data-driven and less dependent on purely human intelligence sources. The Information Age once overwhelmed operators with data. AI now\u00a0provides\u00a0a way to navigate that environment.\u00a0This\u00a0is why cyber intelligence and persistent access are essential to modern targeting.\u00a0Pattern-of-life targeting relies on multiple streams of surveillance and behavioral data. AI\u2019s greatest strength is its ability to combine these streams\u00a0on\u00a0a scale that would overwhelm most military units. Yet the central question\u00a0remains\u00a0unresolved by algorithms alone: should the target be neutralized? That decision is legal, moral, political,\u00a0and\u00a0strategic.\u00a0<\/p>\n A clear example of AI-integrated intelligence limits came on the first day of the 2026 Iran War, when a U.S. missile\u00a0struck an elementary school<\/a>\u00a0in Minab, Hormozgan province, killing civilians, including children, in one of the war\u2019s deadliest civilian incidents.\u00a0The incident underscored a basic truth: AI-enabled targeting is only as\u00a0dependable\u00a0as its data. Here, the system likely\u00a0relied on outdated intelligence<\/a>\u00a0that missed the school\u2019s proximity to an Islamic Revolutionary Guard Corps compound.\u00a0\u00a0<\/p>\n AI was not the\u00a0likely\u00a0source\u00a0of failure. More likely, flawed intelligence and the fog of war were to blame. Human operators still\u00a0validated\u00a0the strike with satellite imagery and intelligence reviews, even though the target was effectively co-located with the school.\u00a0\u00a0<\/p>\n The episode showed both the limits of AI models and the need for human review. Systems like\u00a0Maven Smart System\u2019s Target Workbench<\/a>\u00a0sort, correlate, and\u00a0reveal\u00a0intelligence, but humans still\u00a0approve of\u00a0final actions. AI can\u00a0aid\u00a0target validation, but legal review and command authorization remain essential.\u00a0<\/p>\n CONCLUSION\u00a0<\/p>\n The effectiveness of any algorithm depends entirely on the intelligence architecture\u00a0and data\u00a0supporting it. AI does not create certainty; it produces probability. If the underlying data is manipulated, incomplete, stale, or inaccurate, the output will reflect those flaws.\u00a0The greater danger is not the removal of the human in the loop, but the compression of human judgment into groupthink. AI-generated recommendations can create an aura of probabilistic certainty that encourages agreement instead of scrutiny. Human operators may still make the final call, but the risk is that they increasingly\u00a0validate\u00a0model\u00a0logic rather than independently challenge it.\u00a0<\/p>\n Humans\u00a0remain\u00a0in the loop today, but intelligence is now sorted at machine speed while generative systems provide recommendations to reviewers within the targeting cycle. Doctrine should evolve to ensure that human judgment takes precedence over AI-generated\u00a0recommendations. The defining feature of this so-called first AI war is therefore not the replacement of human agency, but the intensification of human responsibility to judge, restrain, and decide at machine speed.\u00a0<\/p>\n Lieutenant Colonel Matthew J. Fecteau is an information operations officer working with artificial intelligence, and a PhD\u00a0researcher\u00a0at King\u2019s College London.\u00a0The views expressed in this report are those of the author and do not necessarily reflect the official policy or position of the Department of the Army, the Department of War, or the US Government.\u00a0<\/p>\n Matthew J. Fecteau<\/p>\n Matthew J. Fecteau is a graduate of the Harvard Kennedy School of Government and a veteran of the Iraq War. He specializes in national security issues. Follow him on Twitter @matthewfecteau<\/a> or email him at matthew.fecteau@gmail.com<\/a>.<\/p>\n ![\"\"](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/06\/2026-Download-Button26.png\")
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