Without that context, AI can generate answers quickly but still make the wrong decisions, says Irfan Khan, president and chief product officer of SAP Data & Analytics.
“AI is incredibly good at delivering results,” he says. “It moves fast, but without context it can’t make good decisions, and good decisions are what create the return on investment for a business. Speed without decisions 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, Khan says. The right data fabric allows organizations to safely scale AI, coordinate decisions between systems and agents, and ensure that automation reflects true business priorities rather than making decisions in isolation.
Recognizing this, many organizations are rethinking their data architecture. Instead of simply moving data into a single repository, they are looking for ways to connect information across applications, clouds, and operational systems while preserving the words that explain how the business works. This shift is driving growing interest in data fabric as the foundation of AI infrastructure.
Losing context is a serious AI problem
Traditional data strategies have focused on large-scale aggregation. Over the past two decades, organizations have invested heavily in extracting information from operational systems and loading it into centralized warehouses, lakes, and dashboards. This approach makes it easier 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, procedures, and real-world decisions – is lost.
Take two companies using AI to manage supply-chain disruptions. If one uses raw signals like inventory levels, lead times, and supply scores, while the other adds context to business processes, policies, and metadata, both systems will analyze the data faster but will likely draw different conclusions.
Khan says information such as which customers are strategic accounts, what tradeoffs are acceptable during shortages, and the status of extended supply chains will allow one AI system to make strategic decisions while another would not have the proper context.
“Both systems move very quickly, but only one moves in the right direction,” he says. “This context premium is the benefit you get when your data foundation preserves context in processes, policies, and data by design.”
