Proving the ROI of Agentic AI in Financial Services

CIOs and executives in financial services are under pressure to numerically prove the return on investment (ROI) of AI investments. Unlike traditional enterprise systems, Agentic AI has a dynamic and multi-variable cost structure involving database queries, external API calls, and inference loops, which traditional FinOps tools cannot handle.

To address these challenges, LangChain (LangSmith, LangGraph) and Pay-i have partnered to propose a solution that combines an engineering platform with an economic intelligence platform. LangSmith provides engineering visibility by tracing every agent execution to track token usage and costs per model. Meanwhile, Pay-i goes beyond these technical metrics by defining business KPIs per workflow and tracking them in real time to connect actual business outcomes with costs.

This article specifically demonstrates how to build multi-agent architectures and prove ROI to executives through observability and governance infrastructure, using two real-world financial services use cases: RFP processing automation and AML (Anti-Money Laundering) compliance monitoring.