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Data Governance & Quality

Why do so many well designed enterprise AI initiatives collapse the moment they encounter...

Why do so many well designed enterprise AI initiatives collapse the moment they encounter real-world operational pressure?

It is rarely due to a system crash.

It is because of four silent project killers that slowly degrade accuracy, adoption, and alignment behind the scenes.

At PM Ignite 2026, I mapped out these four tactical vulnerabilities.

  1. Accountability Void: unclear ownership
  1. Governance Deficit: treating data owners, lineage tracking, and metadata cataloging as a retroactive checklist before deployment.
  1. Adoption Collapse: Intense frontline worker resistance
  1. Quality Degradation: fragmented, stale data pipelines breaking accuracy the moment the model exits a clean sandbox environment.

First shared on LinkedIn.

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Weekly thinking on data and AI governance from Ash Srivastava.