The Human Reality Gap: Why Enterprise AI Transformation Fails Before the Model Runs

Enterprise AI initiatives continue to struggle despite rapid advances in models, data platforms, and automation. This paper argues that many enterprise AI failures originate before a single model is executed—in the gap between how organizations actually function and how they are represented inside machine-legible systems. The paper introduces the concept of the Human Reality Gap: the structural distance between lived organizational reality and the formal representations used by AI systems. Building on the Representation Economy, Digital Anthropology for Enterprise AI, the SENSE–CORE–DRIVER governance architecture, and the Intelligence Reuse Index, the paper explains how representation failures propagate through enterprise AI systems—from weak sensing, to degraded reasoning, to illegitimate action. The paper further examines why AI pilots often succeed while enterprise-scale transformations fail, why representation quality is emerging as a strategic capability, and how organizations can improve AI governance, intelligence reuse, and business value by reducing the gap between reality and representation. Key concepts include: • Human Reality Gap• Representation Economy• Digital Anthropology for Enterprise AI• SENSE–CORE–DRIVER Architecture• Representation Integrity• Intelligence Reuse Index• Enterprise AI Governance• Machine-Legible Organizations Author: Raktim SinghORCID: 0009-0002-6207-602X This work extends a broader research program exploring the role of representation quality, institutional legitimacy, and reusable intelligence in enterprise AI transformation.