AI data readiness: The key to scaling impact

2026

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AI-generated summary

1) Scaling AI impact hinges on establishing a governed, reusable data foundation that connects structured and unstructured data, ensuring reliability and trust in AI outputs.
2) This article explores the challenges of data readiness in AI scaling, outlines four enterprise technology shifts stalling AI adoption, and provides six concrete steps for data leaders to build a robust data foundation.
3) It also details structural changes needed, including observability, data quality management, metadata management, data lineage, governance and controls, and platform and tooling architectures.
Tags: AI, data readiness, scaling, governance, unstructured data

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