Digital Anthropology for Enterprise AI: Why Understanding Human Reality Is Becoming a Core Capability for Machine-Legible Organizations

Enterprise AI discussions frequently focus on models, data, governance, agents, and automation. Yet many AI initiatives continue to struggle when moving from controlled pilots to enterprise-scale deployment. This paper introduces Digital Anthropology as a critical enterprise capability for the age of AI. Building on the Representation Economy framework and the SENSE–CORE–DRIVER architecture, the paper argues that Digital Anthropology strengthens both the SENSE layer, where reality becomes machine-legible, and the DRIVER layer, where AI reasoning becomes legitimate action. In SENSE, Digital Anthropology helps organizations capture tacit knowledge, informal workflows, contextual decision-making, and operational reality that often remain invisible in formal process documentation. In DRIVER, it helps organizations understand evolving patterns of human-AI interaction, approval behavior, accountability, trust formation, automation complacency, and human-in-the-loop effectiveness. The paper introduces the concepts of the Enterprise AI Reality Gap, the Human-in-the-Loop Illusion, and the Synergetic Workforce, and proposes a Digital Anthropology Operating Model for enterprise AI environments. It argues that as AI systems evolve from recommendation systems to increasingly autonomous and agentic systems, understanding human reality becomes a foundational capability for building trustworthy, legitimate, and effective enterprise AI systems.