Agentic AI in Financial Services

The Banking and Financial Services (BFS) industry is entering a new AI adoption phase. While enterprises have achieved success with generative AI pilots and rule-based automation, most efforts remain confined to narrow use cases such as chatbots, document summarization, or predictive models. This incremental approach is no longer sufficient to address the mounting complexities of regulatory requirements, operational inefficiencies, and customer demands for hyper-personalized experiences. Financial institutions are now turning to agentic AI as the next frontier.

Agentic AI introduces goal-oriented systems that can act autonomously or semi-autonomously, coordinate across workflows, and continuously learn from enterprise context. Unlike generative AI, which is primarily informational, or RPA, which is rule-based, agentic AI offers institutions the ability to interpret objectives, apply reasoning, use enterprise tools, and deliver outcomes with limited human intervention. By doing so, it provides a pathway for BFS firms to shift from task-level efficiencies to structural transformation.

This Viewpoint outlines a readiness approach to adopt agentic AI in financial services. It emphasizes the importance of building a holistic foundation across orchestration, memory, governance, and runtime design, rather than pursuing isolated deployments. The report introduces a use case prioritization framework to help institutions identify high-value entry points and build momentum for scale. It also examines practical applications across customer onboarding, compliance, capital markets, treasury, and wealth management, and highlights the evolving ecosystem of hyperscalers, enterprise platforms, and niche providers enabling this shift. By combining trusted governance mechanisms with scalable architecture, agentic AI can help institutions unlock new operating models, enhance resilience, and create value at enterprise scale.