AI agents are designed to automate tasks and processes, making them a valuable tool for organizations looking to streamline their operations. However, despite their potential benefits, AI agents often struggle to succeed without a solid data foundation.
According to Niels Zeilemaker, global CTO at Xebia, the data that powers an AI agent is crucial in determining its effectiveness. "If you don't think about that, you can end up with agents that are […]" he said. "They're only as good as the data they're being trained on, and if that data is weak or incomplete, they'll struggle to get anything right."
Zeilemaker's words highlight the importance of making data available for AI consumption from the outset. This means not just providing raw data, but also ensuring it's accurate, complete, and relevant to the specific tasks and processes being automated. Without a strong foundation in data quality, AI agents can quickly become unreliable and ineffective, leading to wasted resources and decreased productivity.