AI Readiness Gap and Data Health Assessment Framework

This white paper presents a comprehensive framework for understanding the AI Readiness Gap within organizations, focusing on the underlying data foundation necessary for effective AI deployment. It outlines the structural challenges faced by enterprises that have historically optimized data platforms for human analysts rather than autonomous AI agents. The paper introduces a diagnostic framework, including the Data Health Check and the Six-Dimension AI Readiness Model, to assess an organization's current state regarding AI readiness. It details the vital signs of data health required for AI, emphasizing the need for a semantic layer and trust signals to support agentic reasoning. Furthermore, the document discusses two strategic paths for organizations: optimizing existing platforms or leapfrogging to new architectures. The paper concludes with a roadmap for building capabilities necessary for successful AI implementation, highlighting the importance of aligning data practices with the unique requirements of AI agents.