Wells Fargo has named Faraz Shafiq, currently field chief technology officer for generative and agentic AI at Amazon Web Services, as its new Head of AI Products and Solutions, effective February 9. In this role, Shafiq will report directly to Saul Van Beurden, Head of AI and Co-CEO of Consumer Banking and Lending. He will be based in San Francisco and charged with defining the bank’s AI vision, product roadmap and development priorities across consumer, commercial and institutional lines of business.

Charlie Scharf, Wells Fargo’s Chairman and CEO, has highlighted AI as a core growth accelerator, noting that over the past year the bank has trained more than 90,000 employees on AI tools and deployed solutions to over 180,000 desktops. Under Shafiq’s leadership, the bank aims to scale generative and agentic AI platforms to automate routine workflows, enhance customer engagement through personalized digital assistants and drive efficiency gains across underwriting, fraud detection and back-office processing.

Shafiq brings over 15 years of experience leading technology and product innovation at AWS, Verizon, AT&T and Google. At AWS he spearheaded the commercialization of large-scale AI services, growing adoption to thousands of enterprise customers and reducing model development times by up to 50%. His expertise in responsible AI practices and platform scalability is expected to accelerate Wells Fargo’s existing collaborations, including its multi-year partnership with Google Cloud to roll out AI agents across branch and call center operations.

Since naming Van Beurden to lead AI in November, Wells Fargo has begun embedding generative AI tools into engineering workflows, achieving a reported 30–35% improvement in developer productivity. The bank’s four reportable segments—Consumer Banking and Lending, Commercial Banking, Corporate and Investment Banking, and Wealth & Investment Management—are all slated to integrate Shafiq’s roadmapped solutions over the next 12 months. Stakeholders will monitor quarterly updates for key performance indicators such as time-to-market for new AI features, cost savings from automated processes and adoption rates among both customers and frontline employees.