As artificial intelligence (AI) becomes increasingly embedded in core business workflows, organizations are realizing that the biggest obstacle to unlocking its full potential is not model performance or computing power, but rather the quality and context of the data on which those systems rely.

According to Irfan Khan, president and chief product officer of SAP Data & Analytics, AI introduces a new requirement: systems must not only access data, but also understand the business context behind it. Without this context, AI can generate answers quickly, but still make the wrong decision.

To provide context, companies need a well-designed data fabric that does more than just integrate data. The right data fabric allows organizations to scale AI safely, coordinate decisions across systems and agents, and ensure that automation reflects real business priorities rather than making decisions in isolation.

Traditional data strategies have largely focused on aggregation, resulting in the loss of meaning attached to the data. However, with the emerging era of autonomous systems and intelligent applications, a context layer is becoming essential. Many organizations are rethinking their data architecture, seeking ways to connect information across applications, clouds, and operational systems while preserving the semantics that describe how the business works.

This shift is driving growing interest in data fabric as a foundation for AI infrastructure. By recognizing the importance of context, businesses can unlock the full potential of AI and create a return on investment.

Photo by Arc touche on Pexels
Photos provided by Pexels