Judgment-Centric Epistemic Niche (J-CEN) - Reclaiming Epistemic Commitment in AI-Augmented Decision Systems

Abstract As artificial intelligence becomes deeply embedded in organizational decision systems, a paradox emerges: while analytical capacity, information access, and predictive modeling are increasingly automated, organizations suffer from decision paralysis, responsibility diffusion, and judgment avoidance. This paper argues that the true scarcity in AI-augmented systems is no longer expertise or execution, but epistemic judgment—the human capacity to commit to what is believed, acted upon, and borne under conditions of uncertainty, irreversibility, and accountability. This paper proposes the Judgment-Centric Epistemic Niche (J-CEN), a conceptual framework that redefines the non-substitutable role of humans in AI-augmented environments. J-CEN distinguishes judgment from analysis, prediction, and optimization, positioning it as an act of epistemic commitment rather than computational selection. The framework articulates four interdependent layers—Perception, Abstraction, Judgment, and Structuring—that together constitute a sustainable and defensible human niche within intelligent systems. By reframing AI as a judgment amplifier rather than a judgment substitute, J-CEN provides theoretical grounding and practical guidance for governance design, executive decision-making, and portfolio careers. The framework is particularly relevant to emerging professional roles that operate across organizational, technical, and institutional boundaries in the age of AI.