When AI initiatives fail, the model is usually blamed.
But that explanation is structurally wrong.

In this episode, Roland reframes AI failure as a data architecture accountability problem, not a mathematical one. AI doesn’t invent new issues, it faithfully exposes the decisions, trade-offs, and ambiguities that already exist upstream.

This conversation moves the focus from model performance to:

• meaning
• fitness for use
• ownership
• observability
• trust

Because accuracy is downstream.
Architecture is upstream.

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