"The gap between AI ambitions and implementation appears to be greater than expected. Although 78 percent of organizations have now passed the pilot phase, only 17 percent have achieved a mature AI implementation in which ROI is measurable. CData's study, "The State of AI Data Connectivity: 2026 Outlook," shows that the biggest barrier is not AI models or vendor solutions, but the underlying data infrastructure."
"No less than 71 percent of teams spend more than a quarter of their implementation time on data integration. This involves tasks such as data modeling, setting up ETL pipelines, and configuring connectors. This means that significant resources are tied up in infrastructure work rather than innovation. The impact of this is tangible: 71 percent of the organizations surveyed report higher costs and resource pressure due to integration challenges."
Companies are investing heavily in AI, but only 6 percent of data and AI leaders believe their infrastructure can actually support AI projects. Seventy-eight percent of organizations have passed pilot phases, yet only 17 percent report mature implementations with measurable ROI. The primary barrier to broader AI deployment is weak data infrastructure rather than models or vendor solutions. Sixty percent of the most AI-mature companies also have mature data systems, while 53 percent of immature AI adopters have weak data systems. Seventy-one percent of teams spend over a quarter of implementation time on data integration, driving higher costs and delaying AI features; 45 percent report delays due to connectivity. Access to real-time data is identified as essential for effective AI.
Read at Techzine Global
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