A fundamental weakness in the development of artificial intelligence (AI) systems is their reliance on data to function effectively. As AI algorithms are trained on vast amounts of structured and unstructured data, they struggle to make sense of it unless the underlying data is robust. Agentic AI, a type of AI that operates autonomously, relies on high-quality data for its decision-making processes.
Niels Zeilemaker, global CTO at Xebia, notes that "if you don't think about" the importance of building a strong foundation with relevant data, your AI agents can suffer. He emphasizes that agentic AI scales on data strength, meaning the more data available to train on, the better the agent's performance will be. However, poor-quality or inadequate data can lead to subpar results and even fail entirely.
The lack of a solid data foundation is not just an issue for individuals developing their own AI projects but also for organizations relying heavily on AI-powered tools. To overcome this hurdle, businesses must prioritize collecting, processing, and maintaining high-quality data that meets the standards required by their AI applications. By starting with a robust data base, companies can ensure their agentic AI agents are functioning effectively, driving business efficiency and innovation.