Executives are increasingly focused on whether AI agents—software systems designed to perform tasks autonomously—are ready for use in real-world business settings. However, some experts argue that the real challenge lies not in improving the AI models themselves, but in ensuring the quality and reliability of the data these systems rely on. AI agents make decisions based on the data they are given, and if that data is poorly managed or unreliable, the decisions they make can be flawed, affecting thousands of transactions and business outcomes. Many companies are rushing to implement AI without first addressing deeper data issues, which has led to most AI projects failing to deliver a return on investment. AI governance—rules and processes around how AI systems operate—must consider not only the data an agent uses but also the data it generates, such as decisions, instructions, and documents. Ensuring that AI decisions can be traced back and justified is now a key concern for businesses, especially as these systems become more integrated into daily operations. Data governance is not a new challenge. Large organizations have long struggled with managing information that is spread across different platforms and systems. Agentic AI, which can act on its own, can magnify these issues by processing large amounts of data quickly, making it even more urgent to address underlying data problems. Before AI agents are given access to data, IT departments must identify sensitive or regulated information, apply consistent classification and retention policies, and ensure that the data sources used are up-to-date and reliable. Legacy data—information from older systems that may no longer be in use—requires special care. While it can contain valuable historical business insights, it may also include outdated, duplicate, or legally restricted information. Organizations should preserve the context and relationships that make this data meaningful, and provide AI agents with only the data that is necessary and controlled by clear policies. AI agents should only have access to data that is essential for the tasks they are assigned. Giving them more than needed can increase risks, such as exposing sensitive information or making incorrect decisions. Like employees, AI agents should follow the principle of least privilege, meaning they should only have access to the data required for their specific functions. Understanding the source of data is also critical. AI agents need to know where the data came from, when it was updated, and which policies apply to it. Without this context, they may make costly mistakes. Governance doesn't end once AI agents receive approved data. The data they produce is becoming increasingly important in legal disputes, regulatory compliance, and customer complaints. Organizations should keep detailed records of what AI agents were asked to do, what data they accessed, which policies were applied, and the decisions they made. This ensures that reviewers can trace AI decisions back to their original data and to the person or function that authorized the activity. While technology teams play a role, accountability for AI decisions should not fall solely on them. Security, data, privacy, legal, and compliance teams all have responsibilities, but a specific business owner must ultimately be held accountable for the outcomes of AI actions. Data readiness is not a minor issue—it is a critical factor in moving AI projects from testing to real-world use. If an organization cannot trust the data it uses, control access to it, and clearly explain the decisions it leads to, then the AI agent is not yet ready for greater autonomy.