Market Trends: Data Readiness For AI Agents

Executive Summary

This report examines how AI agents raise the threshold for enterprise data readiness, moving the requirement beyond data availability and model access towards information that is authoritative, contextualized, permitted for use and traceable through the workflow. Drawing on Verdantix survey data, it shows why firms face a material gap between AI ambition and operational readiness, with 62% of our survey respondents prioritizing the scaling of AI projects and 52% describing insufficient enterprise architecture and data readiness as a significant barrier to adoption. The analysis identifies the fragmented records, inconsistent definitions, missing metadata and cross-system control gaps that limit agent reliability, then assesses the technologies that convert dispersed data into usable operational context. These encompass document AI, data platforms, catalogues and policy engines, semantic and graph retrieval, controlled tool access, and live process context. The report also examines how delivery models determine whether customer-specific implementation work becomes a reusable foundation, evaluating the role of forward-deployed teams, incremental context models, and build, buy and partner decisions in scaling reliable agent workflows.