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.
Download the full report: https://www.cdata.com/lp/ai-data-connectivity-report-2026/
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.
“The era of AI being constrained by models is over. Today, AI is constrained by data,” said Amit Sharma, CEO and Co-founder of CData. “The organizations winning with AI aren’t the ones with the best algorithms; they’re the ones with connected, contextual, and semantically consistent data infrastructure.”
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.
“Organizations are realizing that AI success isn’t determined by the sophistication of their models. It’s determined by the maturity of their data infrastructure,” said Sharma. “The companies gaining real value from AI are the ones that invested early in connected, real-time data access. Those that haven’t will find themselves at a significant competitive disadvantage.”
