Elastic: Agentic AI in finance fails without quality data readiness

Financial services companies face unique challenges when deploying agentic AI—systems that independently plan and act rather than just generate responses. According to Elastic's Steve Mayzak, success depends less on model sophistication and more on the quality, security, and accessibility of underlying data. Highly regulated environments demand 100% accuracy and fully auditable processes, where models must explain not only inputs and outputs but also the logic behind data selection for each step.

A Forrester study reveals that 57% of financial organizations still lack the internal capabilities to fully leverage agentic AI. Challenges include fragmented data across siloed systems, multiple PDF formats for identical documents, and the difficulty of making non-deterministic large language models produce deterministic, repeatable results. An effective search platform that can index and consolidate structured and unstructured data is critical to enable faster, more consistent AI-driven decisions while maintaining regulatory compliance.