Data Readiness Is the Hidden Barrier to Agentic AI in Finance, Experts Warn

Financial institutions racing to deploy autonomous AI agents face a critical obstacle: their own data infrastructure. New analysis from industry experts reveals that the success of agentic AI—systems capable of independent decision-making—hinges less on model sophistication and more on the quality, security, and accessibility of underlying data. “It all starts with the data,” says Steve Mayzak, global managing director of Search AI at Elastic, in an exclusive interview.

Unlike traditional generative AI, agentic AI can plan and execute tasks autonomously by ingesting real-time information. A recent Gartner survey found that more than half of financial services teams already use or plan to adopt the technology. However, introducing autonomy magnifies both the power and the fragility of existing data systems.

Background: The Agentic AI Revolution and Financial Services

Agentic AI represents a leap beyond chatbots. These systems can independently analyze market shifts, execute trades, adjust risk models, or personalize customer interactions without human intervention. For financial services—a sector governed by strict regulations and second-by-second data updates—the promise is enormous: faster, more accurate decisions. Yet experts caution that without a trusted, centralized data store, these systems will amplify weaknesses. “Agentic AI amplifies the weakest link in the chain: data availability and quality,” Mayzak warns. “Your systems are only as good as their weakest link.”

Regulatory bodies demand full accountability. Financial firms must not only show what data was used but also the logic behind each AI decision. “You can’t just stop at explaining where the data came from and what it was transformed into,” Mayzak explains. “You need an auditable and governable way to explain what information the model found and why that data was right for the next step.” This requires real-time monitoring of both structured spreadsheets and messy unstructured natural language sources.

What This Means: A Data Urgency for Financial Firms

The implications are clear: the race to agentic AI is actually a race for data readiness. Financial services companies must invest in searchable, secure, and contextualized data at scale—before deploying autonomous agents. There is zero tolerance for error in an environment where hallucinations from early AI models could trigger regulatory fines or customer distrust. Mayzak notes that “natural language is way messier than structured data,” underlining the complexity of preparing diverse datasets for AI consumption.

To succeed, firms need a unified platform that can manage transactions, customer interactions, risk signals, policies, and historical context—all with strict governance. As competition intensifies and markets shift second by second, data quality has become the critical differentiator. Any firm that neglects this foundational step risks falling behind—or worse, facing costly failures.

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