SEI and IBM move beyond generative chatbots to deploy autonomous agentic AI in finance

The financial services industry is currently undergoing a fundamental shift in its approach to digital transformation, moving beyond the experimental phase of generative chatbots toward the implementation of agentic artificial intelligence.[1][2] This evolution represents a transition from passive AI systems that merely summarize data or respond to prompts to active, goal-oriented agents capable of executing complex, multi-step workflows with minimal human intervention.[3][4][5][6] At the forefront of this movement is a significant strategic collaboration between SEI, a leading global provider of financial technology and asset management, and IBM Consulting.[7][8][4] This initiative aims to modernize SEI’s internal operations by building a data-centric foundation that allows autonomous AI agents to handle intricate operational tasks, effectively speeding up the pace of automation across the enterprise.

To understand the significance of this shift, it is necessary to distinguish agentic AI from the traditional automation tools that have populated the financial sector for decades. While robotic process automation is typically restricted to rigid, rule-based tasks and standard generative AI focuses on content creation, agentic AI possesses the ability to reason, plan, and adapt.[3][5] These systems are designed to pursue specific objectives by determining the necessary steps to achieve them, interacting with various software environments, and making decisions within predefined parameters.[4][3][5][9] In the context of a firm like SEI, which manages or administers approximately 1.9 trillion dollars in assets, the deployment of such technology is not merely a technical upgrade but a structural redesign of how financial services are delivered.

A primary requirement for successful agentic AI deployment is a robust, data-enabled foundation.[7] This is the central pillar of the SEI and IBM partnership. Financial institutions often struggle with fragmented data residing in legacy silos, which prevents AI models from gaining the holistic view required for autonomous action. The joint initiative focuses on process redesign and targeted system updates to ensure that data is not only accessible but also governed and high-quality. By leveraging IBM’s Enterprise Advantage platform, SEI is creating an infrastructure where AI agents can reliably pull information from across the organization to execute tasks such as portfolio rebalancing, trade settlements, and client reporting. This data-centric approach is critical; without it, autonomous agents risk making decisions based on incomplete or inaccurate information, which could have significant regulatory and financial consequences.

The operational implications of this technology are vast, particularly concerning productivity and human capital. One of the major goals of the SEI-IBM collaboration is to automate routine, manual tasks that currently consume significant employee bandwidth. By delegating these repetitive processes to AI agents, financial firms can shift their human workforce toward higher-value, relationship-driven activities.[8][10] This is a common theme in the broader industry as firms seek to mitigate a "margin squeeze" caused by high operating costs and fluctuating interest rates. Industry data suggests that nearly 54 percent of tasks in the banking and finance sector have a high potential for automation.[5] Agentic AI is expected to be the catalyst that finally unlocks this potential, with some platforms already demonstrating productivity boosts of up to 50 percent in specific consulting and operational use cases.

Beyond internal efficiency, agentic AI is poised to redefine the client experience in finance.[11][3][12] Traditional client service in the sector often involves lengthy turnaround times for complex inquiries, as human agents must navigate multiple disconnected systems to find answers. Agentic AI can unify these workflows, acting as an intelligent intermediary that can verify identities, retrieve transaction histories, and execute account updates in real time. For SEI, this means providing a more consistent and responsive experience for its diverse client base, which includes institutional investors, wealth managers, and private banks. The goal is to move toward a model of "autonomous finance," where routine management is handled by AI, allowing human advisors to focus on bespoke financial planning and strategic consulting.

However, the rapid adoption of agentic AI also presents a new set of challenges for the financial industry, particularly regarding risk management and regulatory compliance. Unlike traditional software, autonomous agents can learn and adapt, which introduces a level of unpredictability that regulators are currently scrutinizing. Issues such as algorithmic bias, transparency in decision-making, and the need for "human-in-the-loop" guardrails are at the center of the debate. Financial institutions must ensure that their AI agents operate within strict ethical and legal boundaries, particularly in sensitive areas like credit scoring, fraud detection, and anti-money laundering. The SEI and IBM initiative addresses this by embedding process intelligence and governance directly into the transformation framework, ensuring that as the AI scales, it remains compliant with the rigorous standards of the regulated financial environment.

The broader AI industry is watching these developments closely, as the success of early movers like SEI will likely set the template for the rest of the sector. Market analysts predict a steep growth curve for agentic AI in finance, with global spending in the category expected to rise significantly through 2028.[4] The shift is often described as the "year of hiring AI" rather than just "chatting" with it, signaling that firms are now looking for ROI through operational execution rather than experimental pilots. As companies move out of "pilot purgatory," the focus has shifted toward building integrated AI-enabled operating frameworks that can support long-term, sustainable growth.[13]

In conclusion, the integration of agentic AI into financial operations marks a turning point in the industry’s digital journey.[1][11][2] By partnering with IBM to modernize its infrastructure and focus on a data-centric operating model, SEI is positioning itself to capitalize on the next wave of automation.[13][8][10] This transition is not just about replacing old systems with new ones; it is about reimagining the very nature of financial work.[11] As autonomous agents take over the heavy lifting of data processing and routine execution, the industry is moving toward a future where operational velocity and data-driven insights are the primary drivers of competitive advantage. The success of this transition will depend on the ability of firms to balance the undeniable efficiency of autonomous systems with the essential oversight and strategic intuition of the human professionals who guide them.