SEI taps IBM to deploy agentic AI across financial operations

TL;DR:

• Financial infrastructure provider SEI has engaged IBM Consulting to redesign its internal processes and deploy AI agents across the business, using IBM’s Enterprise Advantage platform.
• The partnership prioritises auditing existing workflows before deploying agents, a pattern increasingly common among regulated firms that have learned from failed “bolt-on AI” attempts.
• Financial institutions using similar approaches report up to 40% reductions in processing times for standard queries and data entry tasks.

SEI, a financial infrastructure provider managing investment processing and asset management technology, has brought in IBM Consulting to overhaul its operations with agentic AI. The engagement focuses on process redesign and targeted system updates rather than a simple technology overlay.

The approach reflects a growing consensus in financial services: deploying AI agents without first auditing the workflows they will touch tends to produce expensive failures.

Audit first, automate second

IBM and SEI subject matter experts are jointly reviewing the firm’s data architecture, operational systems, and daily routines. The discovery phase maps where human effort goes to repetitive administrative tasks, including standard client queries and manual data entry, before any automation is introduced.

This audit-first model contrasts with earlier waves of AI adoption in finance, where firms often layered machine learning onto broken or poorly documented processes. SEI’s COO Sean Denham described the initiative as investing in “how we operate” alongside what the company delivers.

IBM’s Enterprise Advantage platform provides the technical base, guiding deployment to stay within defined boundaries, an important constraint in a heavily regulated sector where autonomous agents need clear guardrails.

Freeing staff for higher-value work

The business case centres on shifting employees from manual processing to client relationship management and complex problem-solving. Financial institutions applying similar automation strategies report processing time reductions of up to 40%, though results vary significantly depending on data quality and process maturity.

For UK financial services firms considering similar deployments, the SEI-IBM partnership reinforces a pattern: the firms seeing returns from agentic AI are those investing as much in data governance and process mapping as in the AI itself. Clean, well-governed data remains the prerequisite. Without it, autonomous agents generate errors that can compound through financial workflows with real regulatory consequences.