Preparing Data for Agentic AI in Financial Services: Key Questions Answered
Financial services firms face a unique set of demands when adopting agentic AI—autonomous systems that plan and act independently. Unlike standard AI, these systems rely heavily on data that is not only accurate and timely but also secure and compliant with strict regulations. As Steve Mayzak, global managing director of Search AI at Elastic, puts it: "It all starts with the data." The success of agentic AI in this sector depends less on the sophistication of algorithms and more on the quality, accessibility, and governance of the underlying data. Below, we answer the most pressing questions about how financial services can achieve data readiness for this transformative technology.
1. Why is data readiness so critical for agentic AI in financial services?
Agentic AI systems make independent decisions based on real-time data, which means any flaw in the data—whether it's incomplete, outdated, or insecure—gets magnified. In financial services, the stakes are incredibly high because regulators demand full accountability. Mayzak emphasizes that "your systems are only as good as their weakest link", and that weakest link is often data availability and quality. Without a trusted, centralized data store that's easy to access and manage at scale, agentic AI cannot operate with the speed, confidence, and control required to handle rapidly shifting markets and strict compliance demands. Poor data readiness leads to errors, hallucinations, and loss of customer trust, making it a non-negotiable foundation.
2. What unique data challenges do financial services face with agentic AI?
Financial institutions operate in one of the most heavily regulated sectors, where every action must be auditable. They also deal with events that update by the second—market fluctuations, transactions, risk signals. Agentic AI requires integrating both structured data (spreadsheets, databases) and messy unstructured data (natural language from contracts, news, social media). Gartner found that over half of financial teams have already implemented or plan to implement agentic AI, yet many underestimate the complexity. As Mayzak notes, "Natural language is way more messy than structured data," and preparing it for AI requires robust search, security, and contextualization. Additionally, the need for real-time responses with zero tolerance for error means data pipelines must be both fast and flawless.
3. How does agentic AI amplify both data strengths and weaknesses?
Because agentic AI can autonomously plan and execute tasks, it relies on the data it's given far more heavily than traditional AI that just generates responses. If the data is high-quality, well-governed, and secure, agentic AI can optimize complex workflows and incorporate real-time insights with incredible efficiency. Conversely, if the data has gaps, biases, or inaccuracies, the AI will propagate those flaws at scale. Mayzak explains that agentic AI "amplifies the weakest link in the chain". For example, a single outdated interest rate or misclassified transaction could cause an agentic system to make costly trading decisions. Therefore, financial firms must treat data readiness as a strategic priority before deploying autonomous systems.
4. What role do regulations play in shaping data readiness for agentic AI?
Regulation in financial services demands a high degree of accountability for all data-driven tools. Simply explaining inputs and outputs is not enough. As Mayzak states, "You need an auditable and governable way to explain what information the model found and the logic of why that data was right for the next step." This means that every piece of data used by an agentic AI must be traceable through its entire lifecycle—from source to transformation to decision. Firms must implement robust governance frameworks that log data provenance, maintain version control, and allow for transparent reporting. Without such measures, they risk regulatory fines, reputational damage, and loss of customer confidence. Data readiness, in this context, is not just about quality but also about compliance with frameworks like GDPR, SOX, and local financial authority rules.
5. Why is the ability to handle both structured and unstructured data essential?
Financial services generate vast amounts of structured data—transaction records, account balances, risk metrics—but also massive volumes of unstructured data such as emails, call transcripts, regulatory filings, and news feeds. Agentic AI systems need to parse natural language from these complex sources to gain a complete picture. For instance, a trading agent might combine structured market data with sentiment from news articles to make a real-time decision. If the AI can only process spreadsheets, it misses critical context. Unstructured data, however, is messy and difficult to prepare. It requires advanced techniques like entity extraction, semantic search, and data normalization. Financial firms that invest in unifying these data types into a searchable, secure repository will enable their agentic AI to deliver more relevant and accurate insights, reducing the risk of hallucinations and errors.
6. What are the consequences of poor data readiness, such as hallucinations?
Hallucinations—where AI generates plausible but incorrect information—are a major concern in financial services, where a single mistake can lead to significant financial loss or regulatory penalties. Agentic AI, by design, acts on its outputs, so a hallucination could trigger unauthorized trades, faulty risk assessments, or incorrect customer advice. Poor data readiness directly contributes to this: if the underlying data is incomplete, inconsistent, or not properly governed, the AI model is more likely to fill gaps with fabricated information. Mayzak warns that there is "no tolerance for error" in this environment. To mitigate hallucinations, financial firms must provide agentic AI with high-quality, well-governed data that includes historical context and is updated in real time. They also need robust monitoring to detect and correct anomalies quickly.
7. How can financial services prepare their data infrastructure for agentic AI?
Preparation begins with building a trusted, centralized data store that is searchable, secure, and scalable. This involves integrating data from diverse sources—transactions, customer interactions, risk signals, policies, and external feeds—into a unified platform. Mayzak recommends focusing on search, security, and contextualization. Search enables rapid retrieval; security ensures compliance and access control; contextualization adds meaning by linking data pieces with business logic. Financial firms should also implement strong data governance: version tracking, lineage documentation, and audit trails. Finally, they must invest in tools that can handle both structured and unstructured data at scale, using natural language processing to parse messy text. By tackling these steps, companies can deploy agentic AI with speed, confidence, and control—turning data from a liability into a competitive advantage.