Data fabrics as AI backbone
MIT Technology Review’s analysis emphasizes that the data fabric—an integrated, scalable data infrastructure—will determine how effectively organizations deploy copilots, agents, and predictive systems. The article notes that by the end of 2025, roughly half of companies used AI in at least three business functions, but data quality, lineage, and governance are central to turning ad hoc AI experiments into repeatable value. The fabric acts as the connective tissue that enables data to flow securely and consistently across finance, supply chains, HR, and customer operations. Without it, AI-driven workflows can stumble on data silos, inconsistent schemas, and latency issues that erode trust in automation outcomes.
Practically, this means investing in data contracts, standardized schemas, and data quality controls—along with tools that monitor data drift and lineage. It also implies a governance-aware implementation approach, where data stewards work with AI engineers to ensure that models see clean, reliable data. For executives, the takeaway is that AI ROI hinges on a foundation: a modern data fabric that sustains governance and scale while enabling experimentation and iteration. As AI matures in the enterprise, those who prioritize data infrastructure will likely see faster, safer, and more impactful deployments.
Key takeaways: data fabric is foundational for AI; governance and data quality drive value; scale requires disciplined data architecture.