AI teams across various industries face a significant challenge: achieving scale requires addressing data connectivity issues. A mere 6% of companies express satisfaction with their data integration architecture for AI. This dissatisfaction leads to inefficiencies, with 71% of AI teams spending most of their implementation efforts on data management rather than focusing on model training. The report highlights the growing importance of real-time connectivity and semantic intelligence as foundational elements for AI maturity. Leading organizations are actively constructing the necessary connectivity layers to support Generative AI and agentic AI systems. The report emphasizes that enterprises need to identify and bridge their data readiness gaps to enhance AI adoption. It also outlines key infrastructure capabilities that define AI success, based on insights from high-performing organizations. Notably, 83% of enterprises are either building or planning to build a semantic data access layer to unify and contextualize their data. The insights aim to guide AI leaders in their investments for 2026, focusing on areas that drive tangible business impact, such as real-time data connectivity and semantic intelligence. Addressing these connectivity issues is critical for enterprises looking to leverage AI effectively.