The success of agentic AI in financial services hinges not just on smarter models but also on robust data. Financial institutions must ensure their data is accessible, reliable and governed at scale.

Agentic AI systems can independently plan and take actions to complete tasks, making them invaluable for real-time data handling and complex workflows. However, these systems amplify the weaknesses of the underlying data, so quality and security are paramount.

Data must be well-indexed, consolidated across different locations, and free from silos to ensure AI agents provide consistent answers and decisions that can be easily traced and explained. Poorly indexed or fragmented data leads to inconsistent results, undermining confidence among regulators and customers.

Financial services firms face significant challenges in preparing their data for AI. With 57% of financial organizations still developing the necessary capabilities, ensuring accuracy is crucial, as there's no 'good enough' solution without it being flawless.