Classical organizational theory assumes that cognition, coordination, and learning are exclusively HUMAN functions embedded within bounded structures. In HUMAN–AI enterprises, these assumptions no longer hold. This paper reframes organizations as hybrid cognitive architecture systems in which HUMAN judgment, AI amplification, and governance jointly determine performance. Three operational mechanisms cognitive amplification, coordination restructuring, and AI mediated environmental coupling are defined as measurable transformations of information flow, task allocation, and responsiveness to external signals.Each mechanism is formally linked to a corresponding operator in the underlying mathematical model: amplification maps to the AI operator A, restructuring to the governance operator G, and environmental coupling to the structure of the state space X and its admissible domain U. These mappings allow the managerial constructs to be expressed as measurable changes inthroughput, coordination load, verification depth, and environmental sensitivity. The resulting cognitive architecture space replaces contingency based design with a structural mapping between HUMAN–AI alignment, governance constraints, and enterprise performance.The theory is falsifiable: it predicts that performance improves when amplification increases effective cognitive capacity without violating governance stability thresholds, when restructuring reduces coordination bottlenecks, and when environmental coupling enhances situational awareness without inducing volatility. Inequality emerges when differences in HUMAN–AI integration produce heterogeneous operator geometries across units, leading to dispersion in performance functionals. This provides leaders with a rigorous basis for designing workflows, managing risk, and shaping capability in high amplification environments.
Usman Zafar (Sun,) studied this question.