Enterprises are investing in AI copilots and agents, yet most struggle to turn pilots into durable, ROI-positive capabilities. In many cases, it is because the underlying data infrastructure cannot keep pace.
In its new State of AI Data Connectivity: 2026 Outlook, CData surveyed more than 200 enterprise and software leaders and found that only 6% consider their data infrastructure genuinely AI-ready, even as AI becomes embedded in day-to-day operations.
On paper, these organizations look prepared: they have access to advanced models, active pilots, and growing portfolios of copilots and agents. They are, however, held back by fragile integrations and messy data. They lack a single, consistent access layer that gives AI systems reliable, governed access to operational data, including real-time streams.
Carlisia Campos, AI software engineer at Grokking Tech, described this as “the paradox of AI readiness” in the report. She explained, “Our data infrastructure becomes more powerful not through endless adaptability, but through intentional semantic boundaries that give LLMs the predictable contracts they need to orchestrate complex workflows.”
This insight shows why credible AI roadmaps now rely on data connectivity. What limits most enterprises from realizing value from AI is their ability to route clean, governed, real-time data into models in ways that improve work.
How Data Readiness Affects AI Outcomes
The new report from CData found 71% of AI teams spend more than a quarter of their implementation time on data integration work, such as modeling data, building ETL pipelines, and configuring connectors.
When engineering and data talent is tied up in integration, it limits capacity for product innovation, new AI use cases, and better customer experiences. At the same time, the underlying integration demands keep rising.
According to the report, nearly half of respondents (46%) expect a typical AI use case to draw on six or more data sources in real time. It also found that AI-native software providers are three times more likely than traditional vendors to require more than 26 external integrations inside their products.
That means teams face more work to build, monitor, and repair data flows, which slows delivery and makes AI features more fragile when upstream systems change.
Expectations for real-time data delivery are also increasing. Every organization in the study says live data is essential for AI agents and automation, yet only 80% have begun implementing real-time integration, with most in the early stages.
A centralized, consistent data access layer appears to be the clearest line between AI leaders and those rushing to catch up. The report notes that all high–AI-maturity enterprises have already built centralized, semantically consistent data access, while the majority of low-maturity organizations have not even started.
Data Maturity Starts With an AI-Ready Fabric
AI success now rises and falls with data infrastructure maturity.
As Amit Sharma, CEO and co-founder of CData, explained, “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.”
That conclusion is supported by the report’s findings: 60% of companies at the highest level of AI maturity also have the most mature data infrastructure, while 53% of companies with immature AI are still relying on immature data systems.
CData aims to address this divide with its connectivity stack—including the Connect AI Model Context Protocol (MCP) platform—that serves as a layer to turn scattered application, ERP, and SaaS data into a consistent, governed fabric that AI systems can reliably consume at scale.
CData offers hundreds of prebuilt connectors across databases, clouds, ERPs, CRMs, and industry apps, so teams avoid building custom pipelines for each AI project. This provides a consistent, governed access layer between enterprise systems and AI tools, supporting both batch and real-time data access across operational systems.
Adopting a connectivity fabric like CData’s is a way to move AI from pilots to production and toward measurable ROI. As the CData report shows, organizations that build centralized, consistent data access are more likely to scale AI and gain long-term value.
What This Means for ERP Insiders
Integration work is crowding out AI innovation. When teams spend a quarter or more of every AI project on wiring sources together, they delay new use cases; shifting to reusable connectivity layers frees scarce talent to build differentiating ERP capabilities.
AI maturity tracks data maturity. Even with strong pilots and advanced models, enterprises stall when infrastructure is fragmented; the research links high AI maturity to organizations that have already invested in a unified, semantically consistent data fabric.
Data connectivity is a board-level concern. Leaders cannot treat integration as plumbing anymore; the CData study shows that AI ROI hinges on a centralized, governed access layer that keeps models reliably connected to clean, consistent operational data.
