The Importance of Model Context Protocol

About this episode

In this episode of "Full Tech Ahead," host Amanda Razani interviews Amit Sharma, CEO and Founder of CData. They discuss the critical challenge of enterprise AI: securely connecting advanced AI models to proprietary enterprise data (like CRM and accounting systems). Sharma explains that while AI models have vastly improved, the real bottleneck is providing them with the right business context. 

He introduces the Model Context Protocol (MCP) as a key solution for this. The conversation also covers the shift toward Agentic AI—which demands near-perfect accuracy since there is no human in the loop—and data infrastructure, where Sharma advocates for data virtualization (leaving data where it resides, including on-premise) rather than moving everything into a massive central warehouse. 

Ultimately, he views AI as a massive enhancer of human capital that will radically accelerate business timelines.

Key Quotes

"The real power of AI is only captured when AI can actually connect to enterprise data."
"The models aren't the issue. The issue is, how do we make the data and context available to AI?"
"If you have a case for keeping data on prem, they should keep the data on prem. We in fact favor solutions like virtualization, where you can leave the data where it is..."

Takeaways

Context is King, Not Just the Model: Stop waiting for a "better model" to fix your AI problems. Recent models are already highly advanced; the actual challenge is securely feeding them your specific enterprise data and business context.

Embrace the Model Context Protocol (MCP): To effectively connect AI to business data without massive token waste, organizations should adopt MCP, which is becoming the standard for securely structuring and governing how context is brought into AI models.

Agentic AI Requires Extreme Accuracy: When moving from conversational AI to Agentic AI (where AI takes actions autonomously), the margin for error shrinks to zero. Without a human-in-the-loop to catch mistakes, data accuracy and strict agent governance become paramount.

Virtualize, Don't Centralize: You don't necessarily need to move all your data into a massive central data warehouse to use AI. Leaving data where it naturally resides (including on-premise) and using data virtualization is often more secure, compliant with data residency rules, and highly efficient.

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