Enterprises are pouring staggering resources into the adoption of generative AI, and yet the returns aren’t matching the hype: this year’s highly publicized MIT NANDA report found that only ~5% of enterprise GenAI pilots produce measurable ROI/rapid revenue impact, while the rest stall without meaningful P&L lift.
To better understand what sets those successful few apart, CData launched a comprehensive research initiative focused on the underlying factors driving AI outcomes. We surveyed and interviewed hundreds of AI leaders—across enterprises adopting AI, and software providers like Google, AWS, and Microsoft who are building in response to these enterprise needs—to produce the State of AI Data Connectivity Report: 2026 Outlook. Rather than centering the analysis on model innovation or cloud investments, the report zooms in on a critical but often overlooked variable: data infrastructure.
Our findings show that the most AI-mature organizations are those that have prioritized building infrastructure capable of delivering rich business context to AI systems. In particular, our research identified three critical capabilities — the three Cs — that define data readiness for AI: context, connectivity, and control. Without these capabilities, even widely deployed AI pilots often fail to scale because the plumbing isn’t built to deliver clean, connected, contextual data at the right time and with the right controls in place.
Why data infrastructure—not AI model performance—is the real bottleneck
Our research surfaced three structural dynamics that explain why so many organizations struggle to scale AI:
Integration workload consumes too much time. 71% of AI teams spent over 25% of total implementation time on data connectivity and integration alone—resources that might otherwise go toward strategic AI features.
Real-time and multi-source integration is often required. Nearly half of the organizations said a typical AI use case depended on real-time access to six or more data sources. AI-native SaaS companies accommodate as many as 26+ external integrations.
Governance and data quality are under-addressed. 73% cited poor data quality or schema inconsistency as a major blocker. 66% flagged security and compliance as top concerns when exposing enterprise data to AI systems.
In our survey, 60% of enterprises with “leading” AI maturity also reported having the most mature data infrastructure, while 53% of enterprises stuck at early AI maturity admitted their infrastructure was weak or fragmented. In short: without robust infrastructure, even the most advanced models underperform—or worse, deliver unreliable or unsafe outputs.
How a modern connectivity platform fixes the plumbing
For AI to deliver on its promise—smarter decisions, agentic automation, real-time insights—enterprises need a data infrastructure that connects models to operational data sources in real-time, embeds source-level semantics, and enables centralized governance. That’s where a purpose-built platform like CData Connect AI becomes essential.
CData Connect AI enables enterprises and software providers to:
Establish a centralized, semantically consistent data access layer, giving meaning and business context to data across 300+ systems, including SaaS applications, APIs and databases.
Provide real-time or near-real-time data connectivity across databases, APIs, and cloud platforms—eliminating brittle custom pipelines.
Enforce governance, security, and data quality controls, ensuring data feeding AI agents and copilots is accurate, compliant, and trustworthy.
By solving for the three Cs of context, connectivity, and control, CData Connect AI removes the biggest blockers to AI readiness and clears your data plumbing—so models don’t choke on data fragmentation, delays, or ambiguity.
The strategic imperative: invest in data infrastructure first
AI adoption is no longer a question of if. It's assumed. What differentiates the winners isn’t the fanciest model or the biggest compute budget: it’s the capacity to ground GenAI models by connecting them with clean, real-time, context-rich data.
Enterprises and software providers that treat data infrastructure as a strategic asset, not a side project, are the ones turning AI from experiments into business impact. If your AI roadmap depends on agents, copilots, real-time decisioning, or data-driven automation, the first investment shouldn’t be building or tuning models. Instead, you should ensure your data infrastructure meets the three Cs. With CData Connect AI, enterprises and software can supplement their data infrastructure with these capabilities out-of-the-box.
For enterprise IT leaders, CData Connect AI provides a managed, MCP-compliant solution for delivering governed, real-time, semantically enriched data access to AI models and agents.
For software providers, CData Embedded Cloud for AI enables you to deliver native, secure, MCP-compliant connectivity to customer data within your AI-powered products without the overhead of building and maintaining integrations yourself.
