Causal Reasoning and Large Language Models for Military Decision-Making: Rethinking the Command Structures in the Era of Generative AI

Military decision-making is inherently complex and highly critical, requiring com-manders to assess multiple variables in real-time, anticipate second-order effects, and adapt strategies based on continuously evolving battlefield conditions. Traditional ap-proaches rely on domain expertise, experience, and intuition, often supported by deci-sion-support systems designed by military experts. With the rapid advancement of Large Language Models (LLMs) such as ChatGPT, Claude, and DeepSeek, a new research question emerges: Can LLMs perform causal reasoning — the ability to understand and predict cause-and-effect relationships — at a level that can support or enhance military decision-making? This paper explores the causal reasoning capabilities of LLMs for op-erational and strategic military decisions. Unlike conventional AI models that rely primarily on correlation-based predictions, LLMs are now able to engage in mul-ti-perspective reasoning, intervention analysis, and scenario-based assessments. We in-troduce a structured empirical evaluation framework to assess LLM performance through 10 de-identified real-world-inspired battle scenarios, ensuring models reason over pro-vided inputs rather than memorized data. Critically, LLM outputs are systematically compared against a human expert baseline, composed of military officers across multiple ranks and years of operational experience. The evaluation focuses on precision, recall, causal reasoning depth, adaptability, and decision soundness. Our findings provide a rigorous comparative assessment of whether carefully prompted LLMs can assist, com-plement, or approach expert-level performance in military planning. While fully au-tonomous AI-led command remains premature, the results suggest that LLMs can offer valuable support in complex decision processes when integrated as part of hybrid hu-man-AI decision-support frameworks. Since our evaluation directly tests this capability, this paradigm shift raises fundamental question: Is there a possibility to fully replace high-ranking officers/commanders in leading critical military operations, or should AI-driven tools remain as decision-support systems enhancing human-driven battlefield strategies?