AI Scale Depends on Data Orchestration, Not Models

Enterprise AI often stalls in production because data can’t be coordinated across fragmented clouds, SaaS, partner feeds, and legacy systems—not because models are weak. The article explains how fragmentation creates latency, inconsistent inputs, and unclear lineage that become risky once AI drives real workflows, especially under European regulatory and audit constraints. It recommends incremental data orchestration: map dependencies, prioritize key flows, automate quality checks, monitor pipeline health, and embed governance into the flow.