Only 6% of enterprise AI leaders say their data infrastructure is fully ready for AI: a readiness gap that has become one of the biggest constraints on AI progress. That’s a central finding of CData Software’s new report, The State of AI Data Connectivity: 2026 Outlook, which draws on independently collected survey data from more than 200 data and AI leaders at software providers and enterprise organizations. The report establishes a direct link between data infrastructure maturity and AI maturity, identifying the core capabilities that define AI-ready data infrastructure and revealing how gaps in data connectivity, context, and control are stalling AI initiatives across industries.
Infrastructure Gaps Hold Back AI Progress
The research exposes a stark divide: 60% of companies at the highest level of AI maturity have also invested in advanced data infrastructure, while 53% of organizations struggling with AI implementations are hampered by immature data systems. The gap is costing companies time, money, and competitive advantage.
Key Findings:
- AI teams are drowning in data plumbing: 71% of AI teams spend over a quarter of their time on data plumbing instead of innovation
- Connectivity complexity is exploding: 46% of organizations need real-time access to six or more data sources for a single AI use case
- Real-time data is universally critical — but still missing: 100% agree real-time data is essential for AI agents, yet 20% still lack real-time integration capabilities
- AI-Native Providers Are Outpacing Traditional Software in Integration Demands: AI-native software providers require 3x more external integrations than traditional companies (46% need 26+ integrations vs. 15%)
- Infrastructure maturity is the great divide: All high-AI-maturity organizations have built centralized, semantically consistent integration layers — 80% of low-maturity providers haven’t even started
Investment Priorities Shifting from Models to Infrastructure
The report signals a fundamental shift in AI strategy. Only 9% of organizations now rank AI model development as their top investment priority, while 83% are investing in or planning centralized, semantically consistent data access layers.
Download the full report: https://www.cdata.com/lp/ai-data-connectivity-report-2026/
About the Report
The State of AI Data Connectivity: 2026 Outlook provides benchmarks for both enterprises and software providers in two key areas:
- Enterprise AI Adoption — How data infrastructure gaps are limiting AI success and what separates high performers from laggards
- Product AI Strategy — How software companies are embedding AI capabilities and managing escalating integration complexity
The research references findings from the August 2025 MIT report, The Generative AI Gap: The State of Business AI in 2025.
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This entry was posted on December 3, 2025 at 12:52 pm and is filed under Commentary with tags CData. You can follow any responses to this entry through the RSS 2.0 feed.
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CData Study Finds Only 6% of AI Leaders Believe Their Data Infrastructure Is Ready for AI
Only 6% of enterprise AI leaders say their data infrastructure is fully ready for AI: a readiness gap that has become one of the biggest constraints on AI progress. That’s a central finding of CData Software’s new report, The State of AI Data Connectivity: 2026 Outlook, which draws on independently collected survey data from more than 200 data and AI leaders at software providers and enterprise organizations. The report establishes a direct link between data infrastructure maturity and AI maturity, identifying the core capabilities that define AI-ready data infrastructure and revealing how gaps in data connectivity, context, and control are stalling AI initiatives across industries.
Infrastructure Gaps Hold Back AI Progress
The research exposes a stark divide: 60% of companies at the highest level of AI maturity have also invested in advanced data infrastructure, while 53% of organizations struggling with AI implementations are hampered by immature data systems. The gap is costing companies time, money, and competitive advantage.
Key Findings:
Investment Priorities Shifting from Models to Infrastructure
The report signals a fundamental shift in AI strategy. Only 9% of organizations now rank AI model development as their top investment priority, while 83% are investing in or planning centralized, semantically consistent data access layers.
Download the full report: https://www.cdata.com/lp/ai-data-connectivity-report-2026/
About the Report
The State of AI Data Connectivity: 2026 Outlook provides benchmarks for both enterprises and software providers in two key areas:
The research references findings from the August 2025 MIT report, The Generative AI Gap: The State of Business AI in 2025.
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This entry was posted on December 3, 2025 at 12:52 pm and is filed under Commentary with tags CData. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.