What's often underutilized in these decisions is the operational data that already exists in the business. Where are current network quality issues most strongly correlated with customer churn? Which geographic markets are showing demand trajectory that warrants infrastructure acceleration? What's the actual revenue uplift from markets where prior infrastructure investment was deployed?

Telecom CFOs manage at the intersection of financial discipline and technology strategy. The financial models exist. The network performance data exists. The customer revenue data exists. What's frequently missing is the ability to join those worlds fast enough and fluidly enough to support a capital allocation conversation that's genuinely grounded in operational evidence.

Databricks Genie is an AI-powered natural language interface built on the Databricks Data Intelligence Platform. It allows business users, including finance leaders with no SQL or data engineering background, to query structured enterprise data by typing questions in plain English. In a telecom context, Genie sits on top of a unified data environment where network performance metrics, billing records, customer ARPU, and infrastructure investment history are all accessible in one place. Rather than routing a question through a data team and waiting days for a report, a CFO can ask Genie directly and get an answer drawn from the actual systems of record, governed by the access controls already in place.

Databricks Genie enables telecom finance leaders to query across financial, network, and commercial data in natural language. A CFO can ask: 'In markets where we completed 5G densification in the past 18 months, what has been the change in enterprise customer ARPU and churn rate, compared to markets that are still on our deployment roadmap?' That question surfaces from your actual systems.