What Is Model Context Protocol (MCP), and What Does It Mean for Modern Data Architectures?

Despite widespread excitement, investment, and industry noise, the advantages of AI remain elusive for today’s enterprises.

A recent MIT report found that, despite growing enterprise investment in agentic AI, 95% of initiatives failed to reach production or deliver measurable ROI. The core issue was not model quality, but brittle workflows and poor integration with company systems, leaving only 5% of pilots extracting real business value.

Deploying AI successfully is a challenge in itself. While 78% of companies have implemented at least some form of AI tool or application, the number of companies driving value from AI has stayed stubbornly low at around 26%.

Leveraging standalone LLMs or off-the-shelf agents is one thing. Building the connective tissue that allows agentic systems to pull context from enterprise data and source systems in order to be effective—that’s another challenge entirely.

That’s where Model Context Protocol (MCP) can change the game. A recent innovation by the AI company, Anthropic, MCP is the key to connecting AI to the source systems and data it needs to be effective.

So what is MCP, and what does it mean for enterprise data architecture?

This guide will walk you through the protocol, how it works, and why it matters for the modern data enterprise. We’ll look at key use cases for this integrative technology and explore the impact of MCP for enterprises looking to derive value from agentic systems at scale.

Fill out this short form and get your copy today.