New TDWI Research Reveals the Data Strategies Behind High-Impact AI

Report establishes guidelines for a data foundation that will enable organizations to standardize and operationalize data for AI.

TDWI Research has released a new original research report. Based on survey data and focus group data, the TDWI Blueprint Report: Building an AI-Ready Data Foundation explains a capability stack that will allow organizations to support successful AI.

Although many organizations have achieved localized successes, the findings in this Blueprint suggest that long-term AI success depends on the strength of the underlying data foundation.”

— Fern Halper, Ph.D.

Written by the VP of TDWI research, Fern Halper, Ph.D., the report helps organizations understand the demands AI is placing on their data environments and how they can improve their architecture to support multicloud deployments, reusable semantic context, and controlled AI access to enterprise systems.

In the report, Halper says, “Although many organizations have achieved localized successes, the findings in this Blueprint suggest that long-term AI success depends on the strength of the underlying data foundation.” She explains how fragmented data environments, inconsistent governance, weak semantic alignment, and poor data accessibility become major constraints as AI initiatives move from experimentation into production.

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Report Highlights

Among this report’s key findings:
• Generative and agentic AI are driving a major shift in enterprise data environments. Unstructured data, including documents, emails, chat transcripts, and multimedia content, is becoming central to AI initiatives.
• Organizations achieving the greatest business impact from AI are significantly more likely to implement unified data platforms, open table formats, vector data stores, and governance embedded directly into the data layer.
• More than half of organizations (58%) seeing the most impact from AI believe a strong data foundation is essential for successful AI, and another 37% believe it is important.
• High-impact organizations are significantly more likely to adopt domain-level semantic models (60% vs. 17%) and enterprise taxonomies, or business glossaries (36% vs. 7%), underscoring the importance of shared meaning in scaling AI.
• High-impact organizations also increasingly view the data foundation not simply as infrastructure, but as a strategic differentiator that enables scalable, production-grade AI.

The complete report examines how successful organizations are building trusted, governed, and well-architected data environments to support AI. It explores enabling technologies for data such as modern platforms, scalable compute, data governance tools, metadata and semantic layer management, open table formats, and emerging standards.

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