How Can Banks Modernize Their Data to Support AI Agents?

Legacy systems, data silos, and outdated data pipelines serve as a drag on innovation in banking.

They lead to fragmented inputs that blind AI agents, stale data that undermine fresh decisions, and brittle integrations that slow rollouts.

But banks don’t need to fix all their data and technology problems before starting with AI agents.

Instead, it pays to launch a few high-value pilots where data is already accessible and clean enough, while running broader data-integration, quality-governance, and real-time-processing upgrades in parallel. Early pilots will surface hidden gaps, build momentum, and guide full-scale modernization without letting imperfect foundations hinder AI adoption.