Recent advances in artificial intelligence have significantly enhanced decision efficiency, yet they have also introduced a less examined challenge: the transformation of human cognition within system-driven environments. While prior research has primarily focused on trust, fairness, and transparency, limited attention has been given to the cognitive structure underlying decision coherence in human–AI interaction.This paper introduces a proposed theory of conscious leadership that conceptualizes cognition as an emergent interaction among environment, memory, systems, and the human agent. Within this framework, cognitive balance is defined as the equilibrium among these forces, and perceptual integrity is positioned as its measurable manifestation, reflecting the extent to which individuals maintain coherence and authorship over their decisions when interacting with intelligent systems.We hypothesize that cognitive balance positively predicts perceptual integrity, which in turn influences trust in AI-assisted decisions, and that awareness—defined as the individual’s conscious recognition of system influence—moderates this relationship. An experimental study (N = 602) was conducted to test these propositions. Results indicate that perceptual integrity significantly predicts trust (β = 0.48, p < 0.001) and mediates the relationship between decision mode and trust (indirect effect = 0.42, 95% CI [0.31, 0.54]). Furthermore, awareness moderates the effect of system-driven imposition on perceptual integrity (β = 0.23, p < 0.01), such that higher awareness reduces the negative impact of algorithmic enforcement.These findings extend leadership theory by shifting the focus from behavioral control to the management of cognitive balance and contribute to human–AI research by introducing perceptual integrity and awareness as foundational constructs for preserving human coherence in increasingly automated environments.