Rebuilding the Data Stack for AI
Enterprise AI adoption hinges on overcoming fragmented and siloed data by building unified, governed, and AI-ready data infrastructure. Experts from Databricks and Infosys emphasize that high-quality, contextual data is essential for accurate AI outputs and measurable business value. Organizations must move beyond isolated AI experiments and adopt strategic data architectures that support precision, governance, and scalability. Success lies in consolidating data into open formats, aligning AI initiatives with business outcomes, and fostering AI literacy across teams.
- ▪Enterprise AI effectiveness depends on high-quality, unified, and well-governed data infrastructure.
- ▪Fragmented data across legacy systems and SaaS platforms undermines AI accuracy and trustworthiness.
- ▪Businesses need measurable AI outcomes, with successful deployments requiring over 92% output precision.
- ▪A strategic AI data framework includes value management, adaptability, and responsible AI governance.
- ▪Leading companies are shifting from isolated AI projects to integrated data architectures that support agentic AI and autonomous workflows.
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SponsoredArtificial intelligenceRebuilding the data stack for AIEnterprise AI hinges on high-accuracy outputs, requiring better data context, unified architectures, and rigorous measurement frameworks, says Bavesh Patel, senior vice president at Databricks, and Rajan Padmanabhan, unit technology officer at Infosys. By MIT Technology Review Insightsarchive pageApril 27, 2026In partnership withInfosys Topaz Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data.
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