Automation powered by agentic artificial intelligence (AI) could cut financial process times by up to 40%. The productivity gains are pushing large financial firms to rebuild internal workflows around AI-driven operational systems.
SEI has partnered with IBM to modernize its internal operations using AI and automation tools. The project combines process redesign with targeted system upgrades aimed at improving operational efficiency and client service. IBM Consulting will lead the technical implementation using its Enterprise Advantage platform.
The collaboration also includes a full audit of SEI’s existing operational processes. Analysts from both companies are mapping how data moves across the firm to identify where intelligent agents can replace repetitive administrative work.
Can Agentic AI Fix Finance’s Legacy Workflows?
Many financial institutions still run critical processes on legacy infrastructure built decades ago. Integrating AI into those environments often fails when organizations attempt automation without first repairing broken data pipelines.
Industry studies suggest automating routine inquiries and data entry can reduce processing times by roughly 40%. That efficiency gain allows employees to focus on client relationships and complex financial analysis rather than manual administration.
Still, implementing agentic AI requires strict governance over how systems access and interpret data. Can intelligent agents operate reliably in one of the world’s most regulated industries?
Sean Denham, Chief Financial and Chief Operating Officer at SEI, said operational transformation is central to the company’s next phase of growth.
“By deploying and scaling AI across the enterprise through a disciplined, data-driven approach, we will work more efficiently, innovate faster, and scale with confidence,” Denham said.
The initiative emphasizes building a strong data foundation before deploying AI agents widely. Subject matter experts from SEI are working alongside IBM engineers to evaluate system architecture, daily workflows, and governance controls.
IBM Consulting executives say the approach combines institutional financial expertise with AI process intelligence. Glenn Finch, Head of US Financial Services at IBM Consulting, said embedding data-driven insights directly into operational workflows can unlock efficiency gains across complex financial organizations.
Interest in agentic AI is expanding beyond traditional finance into digital asset infrastructure and blockchain analytics platforms. The next phase will test whether large institutions can deploy intelligent agents at scale without compromising regulatory compliance or operational resilience.
