Data Readiness Is the Real Bottleneck for Agentic AI in Finance
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- Elastic (via Steve Mayzak, its global managing director of Search AI) argues that agentic AI success in financial services depends on an authoritative, accessible, and governed context data store — not on smarter models.
- Gartner found more than half of financial services teams have already implemented or plan to implement agentic AI, while a Forrester study cited in the piece says 57% of financial organizations are still building the internal capabilities to fully leverage it.
- Financial services firms must reconcile a core tension: they need deterministic outputs from non-deterministic AI models, and Mayzak noted a 50-year-old bank might have 60 different PDF types describing the same trade execution.
- Agentic AI use cases highlighted include continuous client exposure monitoring, trade exception resolution across formats, and regulatory reporting that is automatically auditable end-to-end.
- Mayzak recommends starting with a single manageable use case rather than automating a full 70-step business process at once, iterating to build a feedback loop of measurable, governable results.
Why it matters: With 57% of financial organizations still building capabilities to leverage agentic AI, the competitive gap will widen between firms that invest in unified, governed data infrastructure and those that don't — especially in a sector where regulators demand traceable, deterministic explanations and, as Mayzak put it, there is often no 'good enough.'
