DECISION SUPERIORITY THROUGH AI-EMBEDDED COMMAND AND CONTROL: THE C5AISTAR FRAMEWORK FOR HUMAN-AI TEAMING IN MULTI-DOMAIN OPERATIONS

Andras TOTH, Sandor MAGYAR

Abstract


Contemporary multi-domain operations demand decision superiority under information saturation and persistent cyber contestation. This study examines how AI-embedded command and control architectures optimize predictive cyber threat analysis and real-time decision-making without undermining doctrinal legitimacy or commander authority. Introducing the C5AISTAR framework, the research applies a doctrine-first methodology combining evolutionary framework comparison, OODA loop mapping, and scenario-based analysis. The findings indicate that AI-embedded command and control structures reduce decision-cycle latency, improve coherence, and enhance resilience through governed, semi-autonomous functions. Decision superiority emerges from the architectural integration of human-AI teaming rather than from the simple automation of commander authority.


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