You picked a model. You built a RAG pipeline or an agent loop. You ran evals, the results looked good, you shipped to production.
Three weeks later, outputs are degrading. Your pipeline logs show no errors. Ingestion is succeeding. The vector index is updating. The model is responding. Everything is green, and something is quietly wrong.
This is the failure pattern PromptCloud's Data for AI 2026 report documents across production AI deployments, drawing on research from IDC, Gartner, and McKinsey alongside live pipeline observations. The model is almost never the problem. The data infrastructure underneath it is.
Specifically: freshness guarantees are missing, schema drift is unmonitored, and the engineering work required to fix both is underestimated at planning time by almost every team going through it.
Here is what the report found, framed for the engineers who build and maintain these systems.
