This report establishes data foundation guidelines to help organizations standardize and operationalize data for AI.
TDWI Research has released a new exclusive research report. The TDWI Blueprint Report: Building an AI-Ready Data Foundation draws on survey and focus group data to describe a feature stack that enables organizations to support AI success.
While many organizations have had local successes, the Blueprint findings suggest that the long-term success of AI depends on the strength of the underlying data foundation. ”
— Dr. Fern Halper
This report, written by Dr. Fern Halper, TDWI Vice President of Research, helps organizations understand the demands that AI places on their data environments and how they can improve their architectures to support multicloud deployments, reusable semantic context, and controlled AI access to enterprise systems.
“While many organizations have had local successes, the Blueprint findings suggest that the long-term success of AI depends on the strength of the underlying data infrastructure,” Halper said in the report. She explains how fragmented data environments, inconsistent governance, weak semantic alignment, and poor data accessibility pose major constraints as AI efforts move from experimentation to production.
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Report highlights
Key findings from this report include:
• Generative and agentic AI are driving major changes in the enterprise data landscape. Unstructured data such as documents, emails, chat records, and multimedia content is becoming central to AI efforts.
• Organizations that are experiencing a significant business impact with AI are significantly more likely to implement unified data platforms, open table formats, vector data stores, and governance built directly into the data layer.
• More than half (58%) of organizations most affected by AI believe a strong data foundation is essential to AI success, with a further 37% believing 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%), highlighting the importance of shared meaning in scaling AI.
• High-impact organizations are increasingly viewing data foundations not just as infrastructure, but as a strategic differentiator that enables scalable, production-grade AI.
This complete report examines how successful organizations are creating trusted, managed, and well-designed data environments to support AI. Consider data-enabling technologies such as modern platforms, scalable computing, data governance tools, metadata and semantic layer management, open table formats, and new standards.
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