Most AI projects fail before the first model, on a data problem: three sources that contradict each other, no shared definition of an active customer, incomplete history.
The foundation
Ingestion pipelines, a warehouse, automated quality tests, catalog and lineage. It is not the seductive part of the project. It is the part that decides whether it succeeds.
What stands on it
Decision dashboards, predictive analytics, segmentation, demand forecasting and anomaly detection.