Agentic AI differs from traditional predictive or generative AI in autonomy and goal-driven execution. As such, the data requirements for agentic AI are critical and the various data types must be considered—structured, unstructured, real-time streaming—to ensure data quality and completeness to facilitate autonomous decision-making. The challenge for bank executives is how to handle multi-source data integration (core banking, CRM, third-party feeds, open banking APIs, etc.) and create an infrastructure for scalability. The roundtable will discuss issues related to data governance and regulatory compliance, security and risk (for agent-to-agent communication. among other things), and interoperability and integration with legacy core banking systems to enable cross-domain decision-making.

The future of agentic AI in banking and financial services is projected to have an enormous impact on the industry. In early days, understanding the potential roles of agentic AI and identifying use cases with the highest impact are critical to creating a successful strategic playbook and implementation. Whether it’s personalized wealth management, customer service automation, credit and lending decisions, or fraud detection, it’s imperative to understand the business value and return on investment (i.e., the reduction in cost-to-serve or an increase revenue through hyper-personalization). The roundtable will discuss the early use cases with the greatest promise and what key performance indicators are being used to measure successful agentic AI initiatives.