7 Crucial Steps to Ensure Data Readiness for Agentic AI in Banking and Finance

Introduction

Agentic AI—autonomous systems that plan and execute tasks without human intervention—is revolutionizing financial services. But its success hinges not on algorithmic complexity, but on the quality, security, and accessibility of the underlying data. As Steve Mayzak, global managing director of Search AI at Elastic, puts it: “It all starts with the data.” With over half of financial firms already deploying or planning agentic AI (per Gartner), the race is on to prepare data for these high-stakes systems. Below are seven essential steps to ensure your data is ready for agentic AI, from building a trusted foundation to enabling real-time auditing.

1. Build a Trusted, Centralized Data Foundation

Agentic AI magnifies both strengths and weaknesses in your data. In financial services, a centralized data store that is easy to access and managed at scale is non-negotiable. Without a single source of truth, autonomous agents may pull inconsistent or outdated information—leading to erroneous trades, compliance breaches, or poor customer experiences. Create a unified data repository that integrates structured data (e.g., transactions, risk models) and unstructured data (e.g., customer emails, news feeds). This foundation allows AI to make decisions based on the most current and accurate information available, reducing the risk of hallucinations and regulatory penalties.

2. Ensure Rigorous Data Governance and Auditability

Regulation demands full transparency in AI decision-making. As Mayzak notes, “You can’t just stop at explaining where the data came from—you need an auditable and governable way to explain what the model found and why that data was right for the next step.” Financial firms must implement detailed lineage tracking for every data point used by agentic AI. This includes recording transformations, sources, and the logic behind each decision. With robust governance, you can confidently answer regulators, auditors, and clients about how AI reached a particular conclusion—turning a potential liability into a competitive advantage.

3. Master Real-Time Data Ingestion and Processing

Markets move by the second. Agentic AI systems that rely on stale data will make outdated decisions. To stay ahead, financial institutions need infrastructure capable of ingesting and processing data in real time. Use streaming platforms (e.g., Kafka) and in-memory databases to handle high-velocity data streams—such as stock prices, news feeds, or transaction alerts. This enables your AI to react instantly to shifting risks and opportunities, from automated hedging to fraud detection, without human lag.

4. Incorporate Both Structured and Unstructured Data

Traditional analytics favor structured spreadsheets, but agentic AI thrives on natural language. Unstructured data—earnings call transcripts, regulatory filings, social media sentiment—contains rich context that structured data often misses. Combine both types into a single, searchable corpus. For instance, an AI can scan a central bank’s policy statement (unstructured) alongside historical interest rate changes (structured) to forecast market moves. This holistic approach gives your agents a more complete picture, improving accuracy and relevance.

5. Prioritize Data Quality and Consistency

Agentic AI amplifies the weakest link: “Your systems are only as good as their weakest link,” warns Mayzak. In financial services, even small data errors can multiply into major losses or compliance failures. Implement automated data quality checks that flag missing values, duplicates, outliers, and stale entries. Use machine learning to monitor data consistency across sources. With high-quality input, your AI can operate with confidence, reducing the risk of hallucinations and costly mistakes.

6. Embed Security and Access Controls at Every Layer

Sensitive financial data—customer PII, trade secrets, risk models—must be protected from unauthorized access. When agentic AI systems interact with this data, security becomes paramount. Implement role-based access controls, encryption both at rest and in transit, and real-time monitoring of data usage. Ensure that AI agents only access what they need for their specific tasks, and log every query for audit trails. A breach could not only incur fines but also erode trust in your AI capabilities.

7. Design for Explainability and Human Oversight

Regulators and customers demand accountability. Agentic AI must be able to explain its decisions in understandable terms. Build interpretable models that highlight which data drove each action—and why. For example, if a loan rejection occurs, the system should point to specific credit history entries or policy rules. Pair this with human-in-the-loop mechanisms for high-stakes outcomes. This transparency builds trust and meets compliance requirements, ensuring that AI augments rather than replaces human judgment.

Conclusion

Agentic AI holds transformative potential for financial services—but only if the data it relies on is trustworthy, secure, and well-governed. By following these seven steps—building a centralized foundation, ensuring auditability, mastering real-time processing, blending structured and unstructured data, prioritizing quality, embedding security, and designing for explainability—your organization can deploy autonomous AI with speed, confidence, and control. As the industry moves forward, the firms that invest in data readiness today will lead the tomorrow.