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AnalysisAI platforms — data science & MLdbt
dbt Labs Blog · Read source ↗

AI projects often stall not due to model issues but because of a lack of trusted context. The article suggests that solving this context gap is key to moving AI initiatives forward.

MyDataWork POV — Blaming the 'context gap' for stalled AI projects feels like a convenient scapegoat. The real issue might be deeper: a fundamental misunderstanding of the data environment. Many organizations rush into AI without a clear map of their existing workflows or data dependencies. Adding context involves understanding the intricate web of data interactions that already exist. Until teams grasp this, AI will remain a stalled promise.
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