Agentic AI in finance speeds up operational automation
In finance, achieving operational automation by integrating agentic AI requires a data-centric foundation to drive real value.
Financial infrastructure provider SEI has engaged IBM to modernise its internal operations via AI and automation. The joint initiative focuses on process redesign and targeted system updates to deliver consistent client experiences, building a modern and data-enabled foundation in the process.
Deploying intelligent agents involves more than simply selecting a foundation model. The actual return on investment relies on auditing existing workflows and finding exact points where human effort is wasted on repetitive administrative tasks.
Financial institutions are increasingly finding that when automation handles standard queries and basic data entry, they can reduce processing times by up to 40 percent, allowing personnel to manage high-value client relationships.
Auditing legacy finance processes for agentic AI readiness
Adoption often stalls when companies apply new technologies to broken pipelines. SEI and IBM Consulting are conducting a comprehensive review of the financial firm’s current operational systems to map a better path forward.
Subject matter experts from SEI are working directly with IBM to assess the underlying data architecture, systems, and daily routines. This discovery phase aids governance and risk management.
Identifying exact opportunities to embed intelligent agents ensures the tools operate within defined boundaries to meet changing business needs. The IBM Enterprise Advantage platform acts as the technical base for this overhaul, guiding the deployment to improve decision-making across the firm and enhance the client experience.
Sean Denham, Chief Financial and Chief Operating Officer at SEI, explained: “As SEI enters its next phase of growth, investing in how we operate is just as critical as investing in what we deliver.
“IBM brings deep industry and technical expertise that will build on our strong operational foundation and strategic vision. 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.”
Directing human oversight toward value creation
Implementing agentic AI systems can directly impact workforce productivity, and not just in the finance sector. Expanding the automation of routine tasks helps companies improve the consistency of their output and streamline client interactions. Employees freed from manual data entry can focus on complex problem-solving and proactive client support.
“Automation will enable our teams to spend less time on manual, repetitive work and more time on higher‑value, relationship‑driven activities—further elevating service quality, strengthening trust among our clients, and creating more opportunities for professional growth,” said Denham.
Machine learning models require clean, well-governed information to function without generating errors. Partnerships between financial incumbents and major technology vendors highlight the necessity of combining deep regulatory knowledge with engineering resources.
Glenn Finch, Head of US Financial Services at IBM Consulting, commented: “SEI has a long-standing reputation for operational excellence and building integrated solutions in a complex, highly regulated industry.
“By combining SEI’s deep knowledge of its business with IBM’s expertise in process intelligence and agentic AI, we can unlock new levels of efficiency across the enterprise. With streamlined operations and data‑centric insights embedded into how work is performed, SEI is strengthening its ability to scale while further differentiating itself in the market.”
Prioritising operational resilience and strict data hygiene allows finance organisations to implement agentic AI safely. Achieving P&L improvements requires mapping out business processes thoroughly before writing any code.
See also: Mastercard brings agentic payments to life in Singapore with DBS and UOB
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