Volume 1 - Issue 4, November - December 2025

The rapid evolution of Network Centric Warfare (NCW) has redefined the modern battlespace by emphasizing the importance of information sharing, distributed operations, and synchronized decisionmaking across geographically dispersed forces. Yet as sensor networks expanded and the density of collected data grew exponentially, early NCW systems exposed a structural weakness: the human cognitive domain became the primary bottleneck in transforming raw information into useful operational knowledge. Artificial Intelligence (AI) now offers a means of bridging this gap by introducing automated data-fusion, pattern recognition, and predictive analysis capabilities that fundamentally reshape how NCW architecture functions. This journal develops an expanded analytical model of AI-enhanced NCW and evaluates its performance across two contrasting scenarios—high-intensity peer conflict and urban/asymmetric operations—to determine how AI influences recognition speed, processing accuracy, network resilience, and the capacity for selfsynchronization. The findings indicate that AI dramatically compresses the Observe–Orient– Decide–Act (OODA) loop, strengthens dispersed force employment, and reduces the cognitive burden on human commanders. However, the study also identifies vulnerabilities rooted in algorithmic brittleness, adversarial data manipulation, and the potential erosion of contextual human judgment during complex engagements. These advantages and risks underscore the need for carefully designed human–machine decision frameworks and robust safeguards to ensure responsible and effective integration of AI into NCW systems.