Most companies experimenting with AI run into the same roadblocks: messy data, stalled pilots, and unclear next steps. Brian Gannuscio has spent over a decade helping organizations move past those barriers by focusing on the architecture that makes AI possible. In this episode, he explains why waiting for perfect data is a mistake, how tools like Snowflake and Salesforce Data Cloud work together, and what it really takes to modernize without driving up costs. You’ll also hear concrete stories—from uncovering $50M in new sales to cutting approval queues by 70%—that show how smarter use of data translates into results.

Episode Highlights

(06:00) Why “perfect” data isn’t required to start using AI

(08:15) How one project uncovered $50M in new sales opportunities

(13:00) What a modern data architecture actually looks like in practice

(15:40) Using zero-copy connectors to simplify access for business users

(19:00) Turning a 25-page Word doc into an AI tool that cut approval queues by 70%

(22:30) Natural language as the next interface for data

(25:30) How AI shifts focus from manual “junk work” to higher-value priorities

(29:00) Why content quality matters just as much as data quality in AI projects

(37:00) Why so many AI pilots stall — and how to build use cases that scale