The Data Foundation Gap Is Growing
61% of organizations have encountered AI issues attributable to data management problems — up from 13% in 2025
Only 47% say their strict data governance frameworks are very or extremely advanced — down from 62% in 2025
60% say AI can access their data, but the models often don’t understand what it means
Who this report is for:
This research is for data and analytics leaders, CIOs, CDOs, and technology executives who are scaling AI adoption and need to understand where their data foundation may be falling short — and what to prioritize to close the gap between AI investment and AI results.
Data & Analytics Leaders
Diagnosing why trust in AI-specific data quality is declining even as overall data confidence rises, and identifying the validation, context, and governance gaps that are undermining AI reliability.
CIOs & CDOs
Making the case for smarter, more targeted data management investment, and deciding which capabilities to prioritize before committing further budget to AI workloads.
Enterprise Architects
Designing a governed, integrated data layer that AI agents can draw on reliably without rebuilding data infrastructure from scratch for every new use case.
AI & Operations Teams
Shifting from supply-side data gathering to the precision, demand-driven approach AI agents need, and learning where data quality issues are most likely to cause failures in practice.
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