Rebuilding the Data Stack for AI
Enterprise AI adoption is hindered by fragmented and low-quality data, despite growing boardroom interest in the technology. Experts emphasize the need for unified, governed data architectures to enable accurate and trustworthy AI outputs. Building AI-ready data infrastructure is essential for organizations to unlock automation, efficiency, and new business opportunities.
- ▪Enterprise AI success depends on high-quality, unified, and well-governed data infrastructure.
- ▪Fragmented data across legacy systems and siloed applications prevents AI from generating reliable, context-rich results.
- ▪Leaders like Databricks and Infosys advocate for open data architectures that integrate structured and unstructured data with strong access controls.
- ▪AI initiatives should be tied to measurable business outcomes and governed by frameworks that prioritize value delivery.
- ▪The future of enterprise AI lies in evolving from systems of engagement to systems of action driven by autonomous AI agents.
- ▪Organizations must invest in AI literacy and foundational data readiness to fully leverage emerging AI capabilities.
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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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