Rebuilding the data stack for AI
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Dezain Radar summary
Enterprise organizations are shifting focus from experimental AI pilots to the foundational data infrastructure required for scaling. The success of internal AI initiatives depends on cleaning fragmented data sources and creating a unified 'data stack' that can feed large language models reliably.
Why this matters
As designers transition from UI prototyping to building data-driven systems, understanding the technical limitations of organizational data helps set realistic expectations for AI product performance and user trust.
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