The cleanup trap: Stop asking RAG to fix bad data

The article argues that many stalled enterprise generative AI projects are misdiagnosed as model failures when the real problem is poor data infrastructure. It warns against the Cleanup Trap, where companies expect RAG systems and retrieval layers to fix inconsistent legacy data after the fact.

As enterprises move from pilots to production, data quality, governance, and pipeline reliability are becoming core AI success factors rather than back-office concerns. The trend suggests winners will invest in data readiness before model orchestration, reducing hallucinations, stale outputs, and failed deployments.