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.
This realization is changing how companies think about AI readiness. Most acknowledge that they do not have the mature data processes and infrastructure in place to trust their data and their AI systems. Only one in five organizations consider their approach to data to be highly mature, and only 9% feel fully prepared to integrate and interoperate with their data systems.
Don’t consolidate, integrate
The emerging solution is a data fabric: An abstraction layer that spans infrastructure, architecture, and logical organization. For agentic AI, the fabric becomes the primary interface, allowing agents to interact with business knowledge rather than raw storage systems. Knowledge graphs play a central role, enabling agents to query enterprise data using natural language and business logic.
The value of the data fabric relies on three components: Intelligent compute to provide speed, a knowledge pool to provide business understanding and context, and agents to provide autonomous action are grounded in that understanding. What makes this powerful is how these capabilities work together, says Khan.
The technology provides the architecture — a foundation that makes agent-to-agent communication and coordination possible. The process will define how businesses and IT share ownership, and establish governance and a culture in which people trust enough to adopt it. Now all three things must work together for a business data fabric to truly be successful.
“It empowers confident, consistent decisions, and when these elements all come together, AI just doesn’t analyze and interpret the data — it drives smarter, faster decisions that really create business impact,” he says. “This is the promise of a thoughtfully designed business data fabric, where every part reinforces the other, and every insight is grounded in trust and clarity.”
Technically, building a data-fabric layer requires several capabilities. Data must be accessible across multiple environments through federation rather than forced consolidation. A semantic or knowledge layer is needed to harmonize meaning across systems, often supported by knowledge graphs and catalog-driven metadata. Governance and policy enforcement must also operate across the fabric so that AI systems can access data securely and consistently.
Together, these elements create a foundation where AI interacts with business knowledge instead of raw storage systems — an essential step for moving from experimentation to real enterprise automation.
Beyond data isolation and dashboards
In the emerging era of agentic AI, the responsibility for monitoring, analyzing, and making decisions based on data increasingly shifts to software. AI agents can monitor events, trigger workflows, and make decisions in real time, often without direct human intervention. That speed creates new opportunities, but it also raises the stakes. When multiple agents operate across finance, supply chain, procurement, or customer operations, they must be guided by the same understanding of business priorities.
Without a common knowledge layer connecting disparate data together, coordination between systems quickly breaks down. One system might optimize for margin, another for liquidity, and another for compliance, each working from a different slice of data.
Importantly, most enterprises already possess much of the knowledge needed to make this work, says Khan. Years of operational data, master data, workflows, and policy logic already exist across business applications — companies just need to make it accessible. Companies that deploy data fabrics gain greater trust in their data, with more than two thirds of enterprises seeing improved data accessibility, data visibility, and exerting more control over their data.
“The opportunity isn’t just inventing context from scratch, it’s activating and connecting the context across your business that already exists,” he continues, adding that a data fabric is the “architecture that ensures data semantics, business processes and policies are connected as a unified system across all the clouds.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.