Banks are transitioning from AI-assisted productivity to agent-led execution, enabled by advances in foundation models, orchestration runtimes, API-centric modernization, and regulatory clarity. Agentic AI is enabling banks to plan, decide, and execute multistep workflows across servicing, payments, lending, and compliance with minimal human intervention. Institutions are prioritizing scalable execution areas to demonstrate measurable ROI while redesigning complex, high-impact processes for phased autonomy. Governance, auditability, and ecosystem integration are emerging as critical enablers for responsible enterprise-wide deployment.

These trends are examined in Avasant’s Agentic AI Use Cases and Adoption in the Banking Industry Playbook. The playbook provides a structured framework to help banks assess where agentic AI can create immediate value and where deliberate redesign is required. It introduces a 2×2 scaling framework that maps banking processes by complexity and agentic AI adoption potential, enabling banks to target near-term wins and prepare for long-cycle transformations.

We assessed 25 banking processes across customer and account life cycle, payments and transaction processing, risk and compliance, lending and credit, cards and merchant operations, and customer service. Each process is positioned within one of four quadrants: foundational automation, scalable execution, strategic transformation, and legacy constraints. The positioning reflects expected adoption trajectory and structural readiness rather than current-state maturity.

The playbook covers agentic AI adoption across four major segments:

• • Retail and corporate banking
  • Cards and payments
  • Mortgage and lending
  • Financial crime and compliance

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.
  • A layered ecosystem is essential for regulator-ready deployment. Rather than deploying point solutions, banks must architect for ecosystem-wide coordination to ensure scalability, compliance, and operational resilience.
  • Banking processes with low complexity and high adoption of agentic AI offer immediate opportunities to industrialize autonomous workflows.
  • Processes with high complexity and increasing adoption of agentic AI require end-to-end redesign, strong governance, and phased autonomy.
  • Legacy-constrained environments must address system fragmentation and regulatory dependencies before scaling autonomy.

The playbook equips banking leaders with a clear decision-making lens, guiding when to scale, when to redesign, and when to defer. This approach ensures that agentic AI investments are aligned with enterprise value, risk tolerance, and operational readiness.