Financial services institutions are moving beyond AI-driven assistance toward agent-led operating models, driven by advances in foundation models, orchestration capabilities, real-time market infrastructure, and evolving regulatory frameworks. Agentic AI enables firms to coordinate and execute multistep workflows across investment, trading, servicing, and post-trade functions with minimal human intervention. Firms are prioritizing high-impact, repeatable processes for near-term efficiency gains while redesigning complex workflows for phased autonomy, supported by strong governance, auditability, and ecosystem integration.

These trends are examined in Avasant’s Agentic AI Use Cases and Adoption in the Financial Services Industry Playbook. The playbook provides a structured framework to help financial institutions identify where agentic AI can create immediate operational value and where deliberate process redesign is required. It introduces a 2×2 scaling framework that maps financial services processes by complexity and agentic AI adoption potential, enabling firms to prioritize near-term execution opportunities while preparing for structural transformation.

We assessed 25 financial services processes across key segments, including asset and wealth management, investment banking, retail and digital brokerages, custodians, and trade finance. Each process is positioned within one of four quadrants: foundational automation, legacy constraints, strategic transformation, and scalable execution. The positioning reflects expected adoption trajectory and structural readiness rather than current-state maturity.

The playbook covers agentic AI adoption across five major segments:

• Asset and wealth management
• Investment banking
• Retail and digital brokerages
• Custodians
• Trade finance

The report provides several findings, including the following:

• Agentic AI represents a structural shift from AI in the loop to AI running the loop execution models across financial services value chains.
• A layered ecosystem is essential for regulator-ready deployment. Rather than deploying isolated tools, firms must architect for interoperability across models, domain platforms, orchestration layers, and enterprise systems.
• Financial services processes with low complexity and high agentic AI adoption offer immediate opportunities to scale autonomous workflows and deliver measurable efficiency gains.
• High-complexity, high-impact processes require end-to-end redesign, strong governance, and phased autonomy to unlock full value.
• Legacy-constrained environments must address system fragmentation, data silos, and regulatory dependencies before scaling agentic AI.

The playbook equips financial services leaders with a clear decision-making lens—offering guidance on when to scale, when to redesign, and when to defer. This ensures agentic AI investments are aligned with enterprise value, risk tolerance, and operational readiness across capital markets and investment ecosystems.