Andre Mansour joined Wells Fargo’s Wealth and Investment Management team in May to lead its AI efforts. 

He is “focused on shaping our AI roadmap, specifically how we use AI to better support advisers, further refine the client experience and strengthen our platform,” Mansour previously told FinAi News.  

Mansour joined the $1.7 trillion bank from tech giant Google, where he led capital markets, spearheaded gen AI initiatives across the sector and facilitated adoption of first-party LLMs and AI platforms for top-tier asset managers, according to his LinkedIn profile. He has joined Wells as the WIM’s head of AI.

At Wells Fargo, his plans include advancing practical uses of AI for: 

• Knowledge retrieval; 
• Use of copilots; and 
• Compliance supervision. 

Mansour joined Wealth and Investment Management (WIM), the bank’s arm that serves high-net-worth individual clients, on May 11. 

He talked to FinAi News about his AI plans for WIM and the $1 billion tech spend that the bank’s investment arm has committed to revamping its infrastructure. 

The following conversation has been edited for length and clarity. 

FinAi News: Wells Fargo’s WIM division has committed roughly $1 billion to technology investment. How much of that is directed toward AI-driven capabilities versus core infrastructure modernization and what’s the timeline for that spend?  

Andre Mansour: WIM has invested $1 billion in technology over the past several years to build a more integrated, adviser-centric platform focused on meaningful client outcomes. A portion of that investment is focused on modernizing our core infrastructure, particularly Advisor Gateway, which serves as the foundation for how we deliver AI at scale.  

At the same time, we’re directing investment into AI-enabled capabilities. Rather than treating those as separate efforts, we view them as tightly linked because modernizing the platform is what makes AI meaningful and usable day to day.  

In terms of timing, we’re taking a phased approach by rolling out initial AI capabilities and then broader integration and more advanced functionality within several release cycles as we continue to build on that foundation. 

FinAi News: Where in the adviser and client journey is AI delivering the clearest ROI today and how are you measuring that impact?

AM: The clearest ROI is in adviser productivity. When AI is embedded into core workflows, there’s a reduction in manual work and advisers have more time with clients. It’s measured through capacity created, faster turnaround times and strong adoption.  

There’s also growing impact in personalized planning. AI can help synthesize complex data into real-time insights, which strengthens advice. There, we look at the depth of client conversations and how effectively insights translate into action. The client-facing experience requires a deliberate and very thoughtful approach focused on reducing friction and improving transparency while ensuring the adviser remains central to the relationship. 

FinAi News: How has the AI roadmap evolved from pilot projects to something advisers rely on day to day?

AM: Our roadmap is evolving from pilots to everyday use by focusing on the highest-frequency tasks in an adviser’s day and embedding AI directly into those workflows. Instead of standalone tools, we’re focused on integrating capabilities so they become part of how advisers work, not something separate.  

In terms of how we evaluate what to build, it starts with a simple principle: If it’s not useful, it won’t be used. We prioritize use cases that reduce friction, save time and provide greater insights, then measure adoption and feedback closely to refine them over time.  

We’ve created multiple avenues for feedback. That includes direct adviser input during pilots and development, ongoing feedback loops and programs like our AI Champions network, where employees who are more advanced in AI help others and surface opportunities from the field.  

FinAi News: How is WIM balancing build-versus-buy decisions on AI capabilities given the pace of change in foundation models and the need for risk and compliance sign-off in wealth management?

AM: Balancing speed and innovation with risk management starts with priorities and controls. We focus on the highest-impact use cases while maintaining dedicated capacity to explore new model capabilities and emerging areas like agentic AI.  

Our approach is intentionally platform- and model-agnostic, so we can match the right solution to each use case based on data sensitivity, control needs and cost. Across all of this, human oversight, auditability and embedded risk and compliance guardrails ensure we can innovate at pace without compromising on accountability. 

FinAi News: What’s an AI use case in wealth management that you think is overhyped, and one that’s underhyped?

AM: One area that’s a bit overhyped right now is fully autonomous, end-to-end AI — systems that can independently manage complex workflows without human involvement. 

The technology is evolving, but in wealth management, there’s still work to be done with what’s possible in controlled environments and what meets the bar for accuracy and oversight.  

What’s underhyped is AI embedded directly into everyday adviser workflows, things like meeting preparation, summarizing client interactions or surfacing the right insight at the right moment. Those use cases may sound simple, but they deliver real value by saving time, reducing friction and helping advisers have more time with clients. 

Register here for the FinAi Lending Summit, set for Oct. 7-8 in Las Vegas.