63%
of organizations lack or are unsure whether they have the right data management practices for AI.26%
of surveyed CDOs are confident their data capabilities can support new AI-enabled revenue streams.29%
of technology leaders strongly agree their enterprise data meets the quality, accessibility, and security standards needed to scale Generative AI.Is Your Enterprise Ready to Move AI from Pilot to Production?
Enterprise AI requires more than a powerful model. AI applications depend on reliable data, connected systems, scalable infrastructure, secure access, effective governance, and production-ready data pipelines.
Disconnected data, outdated information, legacy systems, inconsistent definitions, weak governance, and integration gaps can slow AI development and limit the reliability and scalability of AI initiatives.
What You’ll Gain from This Blueprint
- Assess your enterprise data readiness across quality, completeness, freshness, accessibility, and relevance.
- Identify data silos and integration gaps that could prevent AI applications from accessing reliable enterprise information.
- Evaluate your AI-ready architecture across APIs, retrieval layers, data pipelines, system dependencies, and scalability.
- Prepare for Generative and Agentic AI by addressing enterprise knowledge retrieval, permissions, structured and unstructured data, and controlled actions.
- Strengthen security and AI governance with identity, access controls, encryption, auditability, lineage, and least-privilege policies.
- Determine whether your infrastructure can support AI at scale across compute, storage, networking, availability, observability, performance, and cost.
- Use the AI readiness scoring framework to prioritize gaps and build an action plan for remediation, testing, deployment, and enterprise-scale AI.