Why simplifying your data model breaks AI

追加: 2026-05-01

When simplifying data models, critical business semantics are lost, causing AI systems to make incorrect assumptions because they cannot fill in the removed contextual meaning; for example, the term 'completed' in an order context may mean shipped, delivered, invoiced, or paid, and losing this semantic distinction leads to ambiguous interpretations and wrong predictions at scale.