The data foundation every enterprise AI program skips

Enterprises consistently underinvest in the unglamorous data foundation work that determines whether an AI program can scale at all: data lineage, quality monitoring, and a shared semantic layer across business units.

Without a semantic layer, every new AI use case re-derives its own definition of basic business concepts — 'active customer,' 'net revenue' — which produces model outputs that quietly disagree with each other across departments. That inconsistency is what erodes stakeholder trust in AI programs faster than any accuracy metric.

The fix isn't a multi-year data warehouse rebuild. It's scoping a minimal, well-governed data foundation around the handful of entities that the first few AI use cases actually depend on, then expanding coverage as new use cases are approved — treating the data foundation as a product with its own roadmap, not a prerequisite checkbox.