AI needs a strong data fabric to deliver business value

Artificial intelligence is moving quickly in the enterprise, from experimentation to everyday use. Organizations are deploying copilots, agents, and predictive systems across finance, supply chains, human resources, and customer operations. By the end of 2025, half of companies used AI in at least three business functions, according to a recent survey. But as AI becomes embedded in core workflows, business leaders are discovering that the biggest obstacle is not model performance or computing power but the quality and the context of the data on which those systems rely. AI essentially introduces a new requirement: Systems must not only access data — they must understand the business context behind it.

Without that context, AI can generate answers quickly but still make the wrong decision, says Irfan Khan, president and chief product officer of SAP Data & Analytics. "AI is incredibly good at producing results," he says. "It moves fast, but without context it can't exercise good judgment, and good judgment is what creates a return on investment for the 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 just integrate data, Khan says. 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.

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 semantics that describe how the business works. That shift is driving growing interest in data fabric as a foundation for AI infrastructure. Losing context is a critical AI problem Traditional data strategies have largely focused on 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 attached to 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 across business processes, policies, and metadata, both systems will rapidly analyze the data but likely come up with different conclusions. 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 the other will not have the proper context, Khan says. "Both systems move very quickly, but only one moves in the right direction," he says. "This is the context premium and the advantage you gain when your data foundation preserves context across processes, policies and data by design." In the past, companies implicitly managed a lack of context because human experts provided the missing information, but with AI, there is a shortfall and that creates serious limitations.

AI systems do not just display information; they act on it. If a system does not explain why data matters, an AI model may optimize for the wrong outcome. Inventory numbers, payment histories, or demand signals might be accurate, but they do not necessarily reveal which customers must be prioritized, which contractual obligations apply, or which products are strategically important. As a result, the system can produce answers that are technically correct but operationally flawed.