As enterprises adopt agentic and generative artificial intelligence (AI) within service delivery platforms, “AI readiness” has emerged as a formal discipline, typically operationalized through automated assessment tooling that flags configuration gaps and structural data-completeness issues before deployment. This paper examines a representative instance of that tooling — ServiceNow’s AI Readiness Evaluation (AIRE) application, used to assess IT Service Management (ITSM), Customer Service Management (CSM), and HR Service Delivery (HRSD) instances prior to enabling agentic AI capabilities — and argues, based on the publicly available documentation and community evidence reviewed in this paper, that its assessment scope appears to exclude a dimension that the Retrieval-Augmented Generation (RAG) research literature treats as foundational: the freshness, ownership, and accuracy of the knowledge content an AI system actually retrieves from at inference time. Drawing on ServiceNow’s own Knowledge-Centered Service (KCS) methodology, which independently names “content health” as a distinct knowledge management discipline, this paper proposes a four-dimension content-layer readiness framework — ownership accountability, staleness and expiry discipline, feedback-loop capture, and retrieval-scope correctness — and outlines a concrete validation methodology using a ServiceNow Personal Developer Instance (PDI). This paper is positioned explicitly as a framework and protocol paper: the four-phase validation methodology described in Section 5 has been designed in full but has not yet been executed, and no empirical results are claimed. The intended contribution is a practical, auditable framework and a falsifiable research protocol that could help address the gap between what current AI readiness tooling checks and what the RAG literature indicates actually determines the trustworthiness of an AI-generated answer in HR service delivery.