Are Electronics Warfare Technicians at Risk Due to AI?

All military enlisted tactical operations and air/weapons specialists and crewmembers not listed separately.

The occupation "Military Enlisted Tactical Operations and Air/Weapons Specialists and Crew Members, All Other" has an automation risk of 0.0%, reflecting the uniquely complex, adaptive, and often unpredictable nature of military field operations. These roles typically require split-second decisions in dynamic environments, direct human coordination, and a high threshold for situational awareness that artificial intelligence and robotics are currently unable to match. Tasks such as reading ambiguous battlefield cues, improvising under stress, and maintaining unit morale extend well beyond what algorithms can model or replicate. Furthermore, the occupation involves navigating hazardous or rapidly shifting scenarios that demand bodily presence and intuition, making it fundamentally resistant to current and foreseeable automation technologies. While some components of the role may appear automatable, they represent only a small fraction compared to the broader job requirements. The top three most automatable tasks might include routine equipment checks, inventory management, and the relay of basic operational information, all of which can be partially supported by emerging sensor and communications technologies. However, these aspects remain a small part of the total job function, as actual combat operations or rapid tactical decision-making cannot be safely entrusted to automation. The highly unpredictable variables and the moral and ethical dimensions of life-and-death choices on the battlefield further restrict the applicability of automation in these fields. The core bottleneck skills that prevent automation in this occupation include advanced critical thinking and problem-solving (requiring Expert level), real-time situational assessment and adaptability (Advanced level), and skilled teamwork/leadership under duress (Expert level). These proficiencies are supported by nuanced communication, physical dexterity, and the capacity to interpret non-verbal cues—skills that automated systems are far from mastering. The dynamic human intelligence required to interpret complex situations, integrate diverse data streams, and make rapid judgments in uncertain contexts becomes the defining barrier to automation. As a result, the technological ceiling for automating this comprehensive set of skills is well beyond the current and near-future capabilities of artificial intelligence and robotics.