Without this context, AI can generate answers quickly but still make the wrong decision, says Irfan Khan, president and chief product officer, SAP Data & Analytics.
“AI is incredibly good at generating results,” he says. “It moves fast, but without context it can’t make a good decision, and a good decision is what creates a return on investment for a business. Speed without judgment doesn’t help. It can actually hurt us.”
In the emerging era of autonomous systems and intelligent applications, that context layer is becoming essential. To provide context, companies need a well-designed data fabric that does more than integrate data, says Khan. The right data fabric allows organizations to safely scale AI, integrate decisions across systems and agents, and ensure that automation reflects real business priorities rather than decisions made in isolation.
Recognizing this, many organizations are rethinking their data architecture. Instead of simply moving data into a single repository, they are finding ways to connect information across applications, clouds, and operational systems while preserving the semantics that describe how the business works. This shift is fueling the growing interest in data fabric as the foundation for AI infrastructure.
Losing context is a key problem in AI.
Traditional data strategies have largely focused on collection. Over the past two decades, organizations have invested heavily in extracting information from operational systems and loading it into central warehouses, lakes and dashboards. This approach makes it easy to run reports, monitor performance, and generate insights across the business, but in the process, much of the meaning associated with that data—how it relates to policies, processes, and real-world decisions—is lost.
Take two companies using AI to manage supply chain disruptions. If one uses raw signals such as inventory levels, lead times, and supply scores, while the other adds context to business processes, policies, and metadata, both systems will analyze the data quickly but will likely draw different conclusions.
Information such as which customers are strategic accounts, which trades are acceptable during shortages, and the status of extended supply chains will allow one AI system to make strategic decisions, while another may not have the appropriate context, Khan says.
“Both systems move very fast, but only one moves in the right direction,” he says. “That’s the contextual premium and benefit you get when your data foundation keeps all processes, policies and data secure by design.”