AI needs a strong data fabric to deliver business value

2026-04-22 · Source: MIT Technology Review · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

SAP, in partnership with MIT Technology Review Insights, highlights that while enterprise AI adoption is accelerating, with half of companies using AI in at least three business functions by the end of 2025, the primary obstacle is not model performance but data quality and context. Irfan Khan, president and chief product officer of SAP Data & Analytics, emphasizes that AI requires understanding business context to make sound judgments and deliver ROI, as speed without judgment can be detrimental. The solution proposed is a well-designed data fabric that integrates, rather than consolidates, data across applications, clouds, and operational systems, preserving semantic meaning. This approach enables safe AI scaling, coordinated decision-making, and ensures automation aligns with business priorities, moving beyond traditional data aggregation strategies that often lose critical context.

Key takeaway

For CTOs and VPs of Engineering deploying AI, recognize that your biggest hurdle is likely data context, not model performance. Prioritize implementing a data fabric that integrates existing enterprise knowledge and preserves semantic meaning across disparate systems. This will enable your AI agents to make context-aware decisions, ensuring automation aligns with strategic business priorities and delivers tangible ROI, rather than generating technically correct but operationally flawed outcomes.

Key insights

Enterprise AI success hinges on data context and judgment, not just speed, necessitating a robust data fabric.

Principles

• AI requires business context for good judgment.
• Data fabric integrates, not consolidates, data.
• Preserve data semantics across systems.

Method

Implement a data fabric with intelligent compute, a knowledge pool for context, and agents for autonomous action, ensuring federation, semantic layers via knowledge graphs, and consistent governance.

In practice

• Connect existing operational data and policies.
• Utilize knowledge graphs for natural language queries.

Topics

• Data Fabric
• Artificial Intelligence
• Business Context
• Knowledge Graphs
• Enterprise Data Architecture

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, AI Architect, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by MIT Technology Review.