Only 6% of enterprise AI leaders report that their data infrastructure is fully prepared for artificial intelligence (AI) deployments, revealing a readiness crisis that has emerged as one of the most significant barriers to AI advancement, according to new research from CData Software.
The company’s report, “The State of AI Data Connectivity: 2026 Outlook,” surveyed more than 200 data and AI leaders across software providers and enterprise organizations, uncovering a stark correlation between data infrastructure maturity and successful AI implementation. The findings suggest that organizations’ struggles with AI adoption stem less from technological limitations than from fundamental gaps in data connectivity, context, and control.
The research exposes a troubling divide in organizational readiness: While 60% of companies demonstrating high AI maturity have invested substantially in advanced data infrastructure, 53% of organizations struggling with AI implementations are handicapped by underdeveloped data systems — a disparity that leads to lost time, resources, and competitive positioning.
“The era of AI being constrained by models is over. Today, AI is constrained by data,” CData CEO Amit Sharma said. “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.”
The report also reveals AI teams are spending excessive time on foundational work rather than innovation, with 71% dedicating over a quarter of their time to data plumbing. Meanwhile, connectivity demands are intensifying. Nearly half (46%) of organizations require real-time access to six or more data sources for individual AI use cases. Despite universal agreement that real-time data is essential for AI agents, 20% of organizations still lack real-time integration capabilities.
AI-native software providers face particularly acute challenges, requiring three times more external integrations than traditional companies. Nearly half need 26 or more integrations, compared to just 15% of conventional firms.
Infrastructure maturity has emerged as the defining factor separating successful AI adopters from stragglers. All high-maturity organizations have established centralized, semantically consistent integration layers, while 80% of low-maturity providers haven’t begun this work.
The report signals a fundamental strategic pivot. Only 9% of organizations prioritize AI model development as their top investment, while 83% are investing in or planning centralized data access layers.
