CData Software is warning that data, not models, has become the primary bottleneck for enterprise AI. In a new study, the integration and connectivity specialist reports that only 6% of AI leaders believe their organizations’ data infrastructure is fully ready for AI, despite heavy investment in models and tooling over the past few years.
The report, titled “The State of AI Data Connectivity: 2026 Outlook,” is based on an independent survey of more than 200 data and AI leaders at software providers and large enterprises. Its central thesis is that AI maturity tracks closely with data infrastructure maturity, and that organizations are now discovering structural limitations in how their data is connected, contextualized and governed.
CData’s analysis highlights a pronounced split between companies that are successfully scaling AI and those that remain stuck in pilot mode. Among organizations at the highest level of AI maturity, 60% have invested in what the firm calls “advanced data infrastructure,” including centralized integration layers and semantically consistent data models. By contrast, 53% of companies struggling with AI implementations cite immature or fragmented data systems as a critical constraint.
That gap is increasingly visible in how AI teams spend their time. According to the survey, 71% of AI teams now devote more than a quarter of their efforts to “data plumbing” – connecting, cleaning and normalizing data across systems – instead of building models, agents or applications. For many enterprises, that translates directly into higher costs and slower time to value, as scarce AI engineering talent is absorbed by integration work.
Connectivity demands are also accelerating. Nearly half of respondents (46%) say they need real-time access to six or more data sources for a single AI use case, underscoring how conversational agents, recommendation engines and decision-support systems depend on multiple live streams and operational systems. At the same time, 100% of respondents agree that real-time data is essential for AI agents, yet one in five still lacks real-time integration capabilities altogether. The result is a growing mismatch between what AI use cases require and what the underlying data fabric can deliver.
AI-native software providers appear to be at the leading edge of this complexity. The study finds that these companies require roughly three times more external integrations than traditional software vendors: 46% of AI-native providers report needing 26 or more integrations, compared to 15% of non-AI-native organizations. As AI features become embedded across products, the integration surface area expands, increasing the pressure on connectivity and governance architectures.
Amit Sharma, CEO and co-founder of CData, frames the situation as a shift in where the real constraints lie. For years, organizations focused on model types, frameworks and training approaches as the primary levers for AI performance. According to Sharma, that era is ending. The firms gaining value from AI, he argues, are not necessarily those with the most sophisticated models, but those with “connected, contextual, and semantically consistent data infrastructure” capable of feeding those models with the right information at the right time.
The survey suggests that investment priorities are already pivoting in that direction. Only 9% of organizations now cite AI model development as their top investment priority. In contrast, 83% say they are investing in, or planning, centralized and semantically consistent data access layers. These layers are designed to abstract underlying systems and present a coherent, governed view of data to AI agents and applications, reducing the need for bespoke integrations on a per-use-case basis.
CData’s report also emphasizes the role of a “centralized, semantically consistent integration layer” as a baseline for high AI maturity. All organizations in the survey’s top tier of AI maturity have implemented such a layer, while 80% of low-maturity providers have not yet started. That pattern points to integration architecture as a key differentiator between AI leaders and laggards, especially as the number of data sources, APIs and event streams continues to grow.
For B2B technology leaders, the findings reinforce a wider industry narrative: AI strategy is increasingly inseparable from data strategy. As enterprises move beyond proofs of concept into production-scale deployments, the ability to orchestrate data across SaaS platforms, legacy systems, cloud data warehouses, operational databases and event streams is becoming a decisive factor in whether AI delivers tangible business outcomes.
The report, ‘The State of AI Data Connectivity: 2026 Outlook,’ sets out benchmarks for enterprises and software vendors both in terms of AI adoption and product strategy. It highlights how gaps in connectivity, context and control are not just technical issues, but structural barriers to AI-driven transformation.
Executive Insights FAQ
What is the main takeaway from CData’s research for enterprise technology leaders?
The core message is that AI outcomes now depend more on data infrastructure than on model sophistication. Organizations with centralized, semantically consistent integration layers and real-time connectivity are significantly more likely to report high AI maturity. Those without such foundations are seeing AI teams spend disproportionate time on integration work rather than innovation.
How are investment priorities shifting according to the report?
The CData study indicates a clear pivot away from prioritizing model development alone. Only a small minority of respondents still treat models as the top investment area, while a large majority are investing in centralized data access and integration layers. This suggests that budgets are being redirected toward building the plumbing that enables models and agents to operate effectively at scale.
Why are AI-native providers experiencing higher integration demands?
AI-native providers embed AI features deeply into their products and often rely on a wide ecosystem of third-party data and services. As a result, they require far more external integrations than traditional software companies. This higher integration density increases the importance of robust data connectivity, governance and standardization to keep complexity manageable.
What role does real-time data play in AI readiness?
Real-time data is seen as essential for AI agents that need to make context-aware decisions, respond to user inputs, or act on current operational states. However, a notable portion of organizations still lack real-time integration capabilities, creating a gap between strategic ambitions for AI and the practical ability to feed systems with timely information.
How should enterprises respond to the identified data infrastructure gap?
Enterprises aiming to improve AI maturity should focus on building or strengthening centralized integration layers that standardize semantics and provide governed, real-time access to key data sources. This typically involves consolidating ad hoc integrations, investing in connectivity platforms and aligning data architecture with AI roadmaps so that new use cases can be delivered without rebuilding data pipelines from scratch each time.
