Agentic AI in Finance Hinges on Data Quality: Industry Experts Warn of Critical Gap

Financial services firms are facing a make-or-break moment: deploying autonomous AI agents without ironclad data readiness will amplify failures and expose firms to regulatory and operational risks. Industry leaders and analysts warn that the success of so-called agentic AI depends less on algorithmic sophistication and more on the quality, security, and accessibility of underlying data. Steve Mayzak, global managing director of Search AI at Elastic, told Breaking News, “It all starts with the data. Agentic AI amplifies the weakest link in the chain: data availability and quality.”

Agentic AI systems can independently plan and execute tasks—un like generative AI that only produces responses. Gartner reports that more than half of financial services teams have already implemented or plan to implement agentic AI. However, introducing autonomy magnifies both the strengths and weaknesses of the data foundation. “Your systems are only as good as their weakest link,” Mayzak added.

Background

Financial services operate in one of the most heavily regulated sectors, with information that updates by the second—from market prices to customer transactions. Traditional AI struggled with hallucinations; agentic AI, which must act on its decisions, requires near-perfect data. “Natural language is way more messy than structured data,” Mayzak noted, underscoring the difficulty of parsing unstructured sources like news feeds, contracts, and customer emails.

Regulatory frameworks demand full accountability for decisions made by AI. Financial firms must not only know what data entered the model and what output resulted but also understand the logic the model used to select certain information for the next step. “You can’t just stop at explaining where the data came from,” Mayzak emphasized. “You need an auditable and governable way to explain what information the model found and why that data was right.”

At the same time, speed and accuracy are table stakes. Markets shift in seconds; risks and opportunities move with them. Agentic AI that can blend structured spreadsheet data with messy natural language gives users a competitive edge. But any error—especially a hallucination—is unacceptable in a sector where compliance and trust are paramount.

What This Means

For financial institutions, the immediate takeaway is that data readiness must become a boardroom priority, not an IT afterthought. Firms that invest in unified, secure, and searchable data stores will be able to deploy agentic AI with speed and confidence. Those that neglect data quality risk operational failures, regulatory fines, and loss of customer trust.

Mayzak’s advice is blunt: “You need a trusted, centralized data store that is easy to access, dependable, and can be managed at scale.” The data must span transactions, interactions, risk signals, policies, and historical context. Preparing that data for AI is a heavy task, but it is also non-negotiable.

Industry observers predict that the gap between data-ready and data-poor firms will widen rapidly. Early adopters of agentic AI may pull ahead, but only if they solve the data challenge first. As Mayzak concluded, “To deploy agentic AI with speed, confidence, and control, financial services companies must first be able to search, secure, and contextualize their data at scale.”

Breaking News will continue to monitor this fast-evolving story.