Many organizations lack the data management strategies needed to support AI, with 63% either unprepared or uncertain about their readiness, according to a Gartner survey of 1,203 data management leaders. The findings highlight a growing problem: companies are not adapting their data practices to meet AI’s demands, which could undermine their AI initiatives.
Gartner projects that by 2026, 60% of AI projects without AI-ready data will be abandoned. As businesses increase AI adoption, leaders must rethink how they handle data to prevent failures.
“AI-ready data is not a one-time task,” said Roxane Edjlali, senior director analyst at Gartner. “It is a continuous process that requires organizations to improve their data management infrastructure as AI use cases evolve.”
To bridge the gap, Gartner advises businesses to refine their existing data strategies while incorporating AI-specific enhancements. This could involve adopting technologies like vector data stores, chunking, sampling, and retrieval-augmented generation (RAG) integration.
AI governance
“Traditional data management practices are often too slow and rigid for AI teams,” Edjlali said. “Data is frequently stored in siloes across multiple platforms, making it difficult to assess its readiness for AI applications.”
For AI to succeed, organizations must define what qualifies as AI-ready data, ensure it aligns with AI use cases, and implement governance measures to manage risks. Metadata is crucial in this process, enabling businesses to improve data quality and automate key workflows.
“As AI moves from pilot projects to full-scale operations, companies need a governance strategy that ensures responsible AI decisions,” Edjlali said. “CIOs and chief data officers should adopt flexible governance models to balance risks, costs, and business objectives.”
Gartner advises businesses to start strengthening their governance and metadata management now, as organizations with weak data foundations may struggle to support AI in the long run.