Telecom companies face an AI paradox: despite generating massive data volumes, most AI initiatives stall before reaching production scale. The core problem isn't model quality or compute power — it's data debt: fragmented, ungoverned, and semantically opaque data. Three unifications are needed for AI readiness: unified data access across disparate systems (Amdocs, Oracle, Salesforce, etc.), unified governance meeting GDPR, CPNI, and CALEA requirements, and unified semantics so AI agents understand business context like what 'FTTH' or 'CDR' means. Databricks Unity Catalog is presented as the solution, offering lakehouse federation, Delta/Iceberg format support, attribute-based access control, dynamic masking, audit logging, and Metric Views that define canonical KPIs. The argument is that competitive advantage in AI comes not from better foundation models but from a well-governed, semantically rich data foundation.
