Agentic AI in finance speeds up operational automation

Editor's take

SEI is partnering with IBM to implement agentic AI for modernizing its internal financial operations, focusing on a data-centric approach.

This collaboration signifies a pragmatic step towards leveraging advanced AI for tangible efficiency gains in a highly regulated sector. For SEI, it means streamlining complex processes like reconciliation and compliance checks. For IBM, it reinforces its strategy of embedding AI into enterprise solutions, moving beyond pure model development to practical application within established financial infrastructure. The move aligns with broader industry trends where AI is increasingly being deployed not for speculative breakthroughs, but for incremental, data-driven improvements in existing workflows.

The key to watch will be SEI's ability to translate raw data into actionable insights that drive meaningful cost reductions and error mitigation. Specific metrics on reduced processing times for tasks like trade settlement or client onboarding will be crucial indicators of success. Furthermore, observing how SEI navigates the inherent data privacy and security concerns within financial services will provide valuable lessons for other institutions considering similar agentic AI integrations.