
This record hosts a public archival copy of the author’s independent research work. The document is provided for open access, citation, and long-term preservation purposes. This upload represents an archival snapshot of an existing manuscript.Version development and canonical authorship are maintained outside this record. The author retains full authorship and intellectual responsibility for the content. Research Context This manuscript examines the structural role of human judgment in complex systems and proposes a conceptual framework in which human judgment functions as a structural boundary within high-complexity environments. In AI-mediated and automated decision environments, operational processes increasingly rely on algorithmic interpretation, distributed information flows, and large-scale system replication. While such systems improve efficiency and scalability, they also introduce structural challenges related to accountability, interpretability, and long-run governance stability. The study argues that human judgment cannot be fully replaced in complex and non-closure systems. Instead, human judgment operates as a stabilizing interface that constrains automated decision processes and maintains structural coherence within evolving technological environments. Keywords Complex SystemsAI GovernanceDecision ArchitectureEnterprise TransformationHuman JudgmentAutomation BoundariesStructural StabilityAI-Mediated Systems Related Research Tu, X. (2026).Judgment Before Momentum (v1.0.2)https://doi.org/10.5281/zenodo.18571480
accountability, human–machine boundary, human judgment, decision architecture, structural stability, automation boundaries, enterprise transformation, complex systems, responsibility drift, AI-mediated systems, high-complexity systems, responsibility architecture, AI governance
accountability, human–machine boundary, human judgment, decision architecture, structural stability, automation boundaries, enterprise transformation, complex systems, responsibility drift, AI-mediated systems, high-complexity systems, responsibility architecture, AI governance
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