IT is at a pivotal moment, and the biggest hurdle might not be what you think. You may think the biggest obstacle is building the best AI model or assembling big GPU farms; but what’s keeping teams up at night is something simpler yet far more consequential: a new style of data integration designed for AI.
A recent CData survey of data and AI leaders finds only 6% of AI leaders believe their data infrastructure is ready for AI. This finding highlights a critical reality: AI requires something fundamentally different: real-time access, strong governance, and semantic context. Without these elements, even the most advanced AI models can’t deliver reliable outcomes. That’s why building AI-ready data infrastructure is no longer optional; it’s mission-critical.
And analysts and customers are reinforcing this message. Through conversations with leading industry analysts and enterprise customers such as B.J.’s Wholesale Club, Credit Agricole, Office Depot, and SaaS application providers e.g., Google, Salesforce, Palantir, UIPath, three themes consistently emerge:
A new standard for AI Data Integration, MCP. Anthropic’s Model Context Protocol (MCP) has taken the software industry by storm. MCP defines a new standard communication language between AI models and enterprise data, laying the groundwork for accurate, secure, scalable enterprise AI. CData has led the way with the first enterprise-class managed MCP service, Connect AI.
A new approach to building data pipelines for AI. Getting AI to talk to data takes a new type of data pipeline. The data demands of AI are no longer the data demands of BI. Data pipelines must be more automated. They must be built by business analysts and data engineers alike. They must have advanced transformations and document workflows designed for natural language. These AI-native data pipelines democratize access to enterprise data by giving users greater visibility and control, real-time monitoring, automated data validation, and built-in governance to ensure every workflow is accurate, compliant, and transparent. This new approach to AI data pipelines also auto-generates human-readable documentation for better cross-functional collaboration. Learn more about how to build AI-ready data pipelines here.
A new software stack. In the BI era, data integration was designed to facilitate human interaction; in the AI era, data integration must facilitate two audiences: human consumption and AI consumption. This shift requires integration with a new set of tools to “get the job done” for AI, such as MCP servers that allow models to access enterprise systems safely and with the context they need. These tools enable AI agents to access data securely and natively across platforms like Agent Bricks and Microsoft Copilot. See how this works in practice: CData provides native connections to Agent Bricks and Microsoft Copilot.
Bridging strategy to action
While the three themes above outline the core shifts reshaping the technical foundations of AI integration, customers also tell us they face another challenge: figuring out where AI can deliver the most value and how to get started quickly. Even with the right standards, pipelines, and stack, identifying and validating AI use cases is often where teams get stuck.
With Vibe Querying, we help developers and business stakeholders explore and understand their data in natural language, accelerating use-case discovery and enabling faster iteration on AI applications.
We’re not done! Looking ahead to 2026
What’s next?
New ways are emerging to remove the friction between data and human wisdom. The tools developers use are changing. Governance demands are increasing. Trust is job # one. We’re committed to building the foundational data capabilities that help organizations operationalize AI at scale.
Thank you to our customers and partners for joining us on this journey.
Watch this space for more. And in the meantime, read the full 2025 Gartner® Magic Quadrant™ for Data Integration Tools Report.
