The enterprise technology ecosystem is experiencing a costly trend where generative AI pilots fail before reaching production. While leadership often blames model limitations, data engineers identify the underlying issue as an unprepared enterprise data foundation. This is termed the 'Cleanup Trap,' the misconception that fragmented data can be fixed at the retrieval layer. Standard retrieval-augmented generation architectures, simplified by easy vector database setup, falsely suggest the data engineering problem is solved. However, raw, unvalidated data injected into embedding models creates noisy vector spaces. Silent degradation in data pipelines, like schema drift, directly impacts vector stores, preventing AI from providing accurate intelligence. No amount of prompt engineering can fix a compromised ingestion pipeline. To escape this trap, data quality must be treated rigorously before data reaches AI orchestration. This requires a shift towards zero-trust ingestion, structured validation, and anomaly detection. Hardening ingestion pipelines with inline, explicit schema validation at the earliest point is crucial. Multi-tiered algorithmic validation, combining structural checks with statistical profiling for data drift, is also essential. Security and compliance must be decoupled from the model, managed at the data infrastructure tier with strict access controls and lineage tracing. Production AI readiness hinges on tracing flawed responses to pipeline executions and ensuring synchronized data. The focus must shift from solely the model to data reliability, engineering discipline, and pipeline resilience. In the production era, data engineering becomes the control plane for enterprise intelligence.