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The State of AI Data Connectivity: A Critical Report

The recent report by CData Software, titled The State of AI Data Connectivity 2026 Outlook, brings to light a concerning statistic: only 6% of AI leaders believe their data infrastructure is fully equipped to support AI initiatives. This finding underscores what many in the field have suspected—data infrastructure maturity is a crucial determinant for successful AI implementation.

Understanding the Readiness Gap

CData’s report, which is based on insights from over 200 data and AI leaders from both software providers and enterprise organizations, highlights a significant readiness gap. This gap is not just a minor obstacle; it is becoming one of the most critical challenges in the path of AI progress. Companies with mature data infrastructures are likely to excel in AI applications. In contrast, a staggering 53% of organizations struggling with AI implementations are hindered by inadequate and outdated data systems.

According to CData’s CEO, Amit Sharma, the era when AI was limited by the capabilities of models alone is a thing of the past. “Today, AI is constrained by data,” he asserts. Therefore, organizations investing in developing integrated, contextual, and consistent data infrastructures are the ones making memorable strides in AI.

Key Findings on AI and Data Infrastructure

1. Data Plumbing Takes Precedence

The report indicates that a considerable 71% of AI teams allocate over a quarter of their time on tasks related to data plumbing, which detracts significantly from innovation.

2. Connectivity Challenges

Moreover, 46% of organizations require real-time access to six or more data sources for just a single AI use case. This escalating need for diverse data connectivity continues to complicate efforts towards integrating AI capabilities.

3. Real-Time Data is Non-Negotiable

Real-time data has been identified as essential across the board, with 100% of respondents agreeing on its critical nature for AI agents. Yet, 20% of organizations still lack the necessary integration capabilities for real-time data.

4. Differentiation Between AI-Native and Traditional Software

Interestingly, AI-native software providers demand three times more external integrations compared to traditional firms. For instance, while 46% of AI-native companies require more than 26 integrations, only 15% of traditional companies report similar needs.

5. Maturity as the Dividing Line

A clear divide in infrastructure maturity also emerges from the findings: 80% of organizations demonstrating low AI maturity have yet to implement centralized, semantic integration layers, unlike their high-maturity counterparts.

Shifting Investment Priorities

In light of these findings, the report suggests a pivotal shift in AI strategy. Currently, only 9% of organizations regard AI model development as their top investment priority; conversely, 83% are channeling resources into centralized and semantically consistent data access layers.

“Organizations must understand that the sophistication of their models does not determine AI success; rather, it is the maturity of their data infrastructure that truly matters,” emphasizes Sharma. The organizations yielding significant returns from AI are those that prioritized connected, real-time data access from the beginning, setting them apart from competitors who approach data integration with hesitance.

Conclusion

CData’s report, The State of AI Data Connectivity 2026 Outlook, suggests a vital need for organizations to reassess their approach towards data infrastructure. The report draws valuable benchmarks for both enterprises and software providers about enterprise AI adoption and product AI strategy, further stressing how existing gaps limit AI success. To thrive in the rapidly advancing landscape of AI, a robust data infrastructure must not just be a goal for organizations—it should be the foundation upon which their AI strategies are built.

For those looking to dive deeper into these insights and strategies, the full report can be accessed here.