Without this context, AI can quickly generate answers but still make the wrong decision, says Irfan Khan, president and chief product officer at SAP Data & Analytics.
“AI is incredibly good at delivering results,” he says. “They move fast, but without context they can’t exercise good judgment, and good judgment is what creates ROI for the company. Speed without judgment doesn’t help. It can actually hurt us.”
In the emerging era of autonomous systems and intelligent applications, this context layer has become essential. To provide context, companies need a well-designed data fabric that does more than just integrate data, Khan says. The right data fabric allows organizations to safely scale AI, coordinate decisions across 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 just moving data into a single repository, they are looking for ways to connect information across applications, clouds and operating systems while maintaining the semantics that describe how the business operates. This shift is driving increased interest in data fabrics as a foundation for AI infrastructure.
Context loss is a critical problem in artificial intelligence
Traditional data strategies have largely focused on aggregation. Over the past two decades, organizations have invested heavily in extracting information from operational systems and uploading it to central repositories, 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 is lost – how it relates to real-world policies, processes and decisions.
Take, for example, two companies using AI to manage supply chain disruptions. If one uses raw signals such as inventory levels, lead times, and display results, while the other adds context via business processes, policies, and metadata, both systems will analyze the data quickly but will likely come to different conclusions.
Information such as which customers are strategic accounts, what trade-offs are acceptable during shortages, and the state of extended supply chains, will allow one AI system to make strategic decisions, while another will not have the appropriate context, Khan says.
“Both systems are moving very quickly, but only one is moving in the right direction,” he says. “This is the advantage of context and the advantage you gain when your data organization maintains context across processes, policies, and data by design.”
