How UBS Built An AI Insight Flywheel

Many firms build an AI strategy. The smart ones build a business strategy and use AI to power it. By examining the practices of its most effective advisors and reconstructing those workflows with intelligence embedded throughout, UBS created a flywheel that strengthens with each use. The result is an insights engine with an 80% advisor adoption rate that now delivers 17 million insights annually.

The architect behind the flywheel is Joe Cordeira, Chief Data and Analytics Officer for UBS Wealth Management Americas. His perspective is shaped by two decades in financial services, including senior roles at Morgan Stanley and Merrill Lynch, where he led large-scale data, analytics, and CRM programs that transformed how advisors used information to serve clients. That foundation set the stage for the advisor-driven model he championed inside UBS.

"The platform is built by advisors, for advisors. We worked backward from how our best advisors serve their best clients—what they look for, what they wish they had—and built an insights engine around that," said Cordeira. UBS aimed to address a familiar issue in wealth management. Advisors were juggling diverse client needs and nonstop market information, while also seeking to link the firm's broad platform to each client's situation.

• Complexity crunch: "Our advisors have many clients with intricate financial lives and a vast global platform. The difficulty is connecting the right solutions from that platform to the right clients across their entire book of business." The challenge, Cordeira said, was finding a way to turn raw information into something advisors could act on without slowing them down.

To address this, UBS developed a "client experience engine" that scours internal and external data. The platform uses a knowledge graph to create a 360-degree view of every client, powering over 400 different types of proactive insights. These can range from acting as a compliance backstop for business-owner clients unaware of state-level retirement plan regulations, to spotting opportunities for hedging a concentrated stock position, to surfacing personal milestones like a client's child starting college—a detail that matters to the human relationship.

• Flywheel kicks in: That analytics platform created a flywheel effect. As the tool delivered value, advisors contributed more ideas, which were then scaled into new insights for the entire firm, driving further adoption. "This flywheel kicks in once the platform becomes so valuable that advisors are not only using it, but are actively contributing more and more ideas to grow the engine," Cordeira said. "The best ideas from our best advisors can then be scaled and leveraged across the whole business."
• A $20M insight: One of the clearest examples comes from a recurring pattern the engine surfaces. Advisors often discover client cash sitting in low-yield external accounts, which creates an immediate opening to offer a better solution. As Cordeira explained, "Sometimes it's idle cash in an external account that is barely earning anything. When the advisor sees it, they can step in with a better solution. That exact mechanism surfaced a twenty million dollar opportunity not long ago."