Representational Readiness: A Missing Dimension of Enterprise AI Readiness Why Enterprise AI Fails When Organizations Are Machine-Legible but Not Reality-Legible

Enterprise AI initiatives are routinely evaluated for technical and organizational readiness, including data infrastructure, model access, talent, governance, and executive sponsorship. Yet a growing body of evidence suggests that organizations can satisfy conventional readiness criteria and still experience AI systems that fail to scale, lose stakeholder trust, or produce unreliable outcomes in production environments. This paper introduces **Representational Readiness** as a distinct dimension of enterprise AI readiness. Representational Readiness refers to the degree to which an organization’s actual operating reality—including tacit workflows, informal decision paths, exception-handling practices, and contextual judgment—has been faithfully captured in the machine-legible representations upon which AI systems depend. Conventional readiness assessments evaluate whether an organization is prepared to deploy AI; they do not evaluate whether organizational reality has been adequately prepared to be acted upon by AI. Positioned within the broader Representation Economy framework and the SENSE–CORE–DRIVER governance architecture, the paper argues that many enterprise AI failures originate not from insufficient model capability but from deficiencies in organizational representation. To make this problem measurable, the paper proposes a four-dimensional assessment model consisting of Capture Fidelity, Update Latency, Exception Coverage, and Legitimacy Alignment, together with a five-level Representational Readiness Maturity Model (R0–R4). The central claim is that organizations can be machine-legible without being reality-legible. As enterprise AI systems increasingly participate in judgment, decision-making, and execution, representational readiness becomes a critical prerequisite for reliability, trust, governance, and scalable AI value creation.