2026 State of Data Management for AI

As AI becomes more embedded across business functions, the pressure to ensure data accuracy, consistency, and governance is rising. A new study conducted by FT Longitude on behalf of Boomi highlights the critical role of advanced data management in building trusted AI.

Learn how to:

Diagnose why trust in AI-specific data quality is falling even as overall data confidence rises
Prioritize the three areas that matter most: data validation, smarter context, and governance for the agentic era
Shift from supply-side data gathering to the precision approach AI agents actually need
Build a governed data layer that subsequent AI use cases can draw on, enabling deployment in higher-value processes

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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.

What our thought leaders are saying

1/1

The gap between the promise of AI and the reality of AI feels to many organizations like it’s widening. It’s not that organizations aren’t spending enough. They’re just not spending in the right places.

Matt McLarty — CTO and Vice President, Boomi Innovation Group, Boomi

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