We’ve seen how Snowflake AI tools are transforming outcomes for our customers. From saving 4,000 hours a year on manual email intake to treating more patients in emergency rooms to saving 75% of costs, AI in Snowflake is making a real impact on businesses around the world. That same transformative power is at work within Snowflake, too.

The Head of Enterprise Data, Analytics & AI at IFF sat down with Fivetran to unpack how to successfully implement AI while balancing both innovation and governance concerns.

The divide between operational and analytical systems has long resulted in data inconsistencies, unreliability, and redundancies. Without a single, unified source of truth, teams interpret information in their own ways—often after the fact. This can lead to downstream data discrepancies, issues, and distrust. Meanwhile, changes to upstream data structures create ripple effects, breaking downstream systems and requiring manual intervention to fix issues.

In a world where data-driven decisions shape the future of every business, ETL (Extract, Transform, Load) processes are the backbone of operational intelligence. For organizations using Microsoft SQL Server, optimizing ETL pipelines isn't just a technical choice—it’s a strategic imperative. With over two decades in the ETL trenches, I’ve seen what works, what fails, and what silently erodes performance behind the scenes.

For many organizations across industries, the era of experimental AI has given way to the era of practical implementation. Even those companies still testing and evaluating AI solutions are shifting away from the art of the possible to focus more closely on what will soon produce measurable ROI. “It will no longer be enough for your organization to merely use AI to win the approval of company leadership,” says Samuel Lee, Product Marketing Director for Financial Services at Snowflake.