Why 94% of AI Leaders Are Unsatisfied with Their Data Infrastructure

Enterprise generative AI initiatives often fail to produce measurable returns because inadequate data infrastructure limits access to clean, timely, contextual, and governed business data, according to CData’s 2026 research on AI data connectivity. The research found that integration work consumes substantial implementation time, many AI use cases require real-time access to numerous data sources, and data quality, schema consistency, security, and compliance remain major barriers. Organizations with greater AI maturity were also more likely to report mature data infrastructure, underscoring the importance of three capabilities: context, connectivity, and control. CData presents its Connect AI platform as a managed, MCP-compliant option for centralizing semantic data access, supporting real-time connections across hundreds of systems, and applying governance controls, while its Embed Cloud for AI product targets software providers that need secure customer-data integrations.

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