Note: This is the fifth of a series of five posts I’ll be making this week on MCP and how I see its transformational impact on the architecture for the next generation of AI systems.  Check out the first post here on MCP as the Open API Standard for the AI Era.  Post 2 here on The New Vocabulary of AI Orchestration. Post 3 here on Building Chainable AI Systems. Post 4 here on Simulating AI Solutions with AI.

Post 5 in the Series: Introducing MCP to Marketing Tech Leaders

By Dan Jaye, CEO

As I demonstrated in my previous post about simulation-driven development, we’re using AI to refine AI systems with unprecedented sophistication. But here’s what I’ve learned from working with marketing teams: marketing AI needs its own specialized language.  In other words, universal standards need industry dialects.

The foundational MCP architecture I introduced earlier provides a universal foundation – but universal doesn’t mean one-size-fits-all. Marketing teams need components that inherently understand campaigns, customer journeys, attribution models, and conversion funnels.

How About MCMAP: The Marketing and Advertising MCP Standard?

This is why I’m suggesting what I will call MCMAP: the Model Context Marketing and Advertising Protocol. Think of it as OpenRTB for AI – a domain-specific extension that speaks marketing’s native language.

MCMAP would standardize:

• Schema definitions for how product and marketing metadata are tokenized as well as customer profile and historical context data, as MCP is largely silent on the context formats.
• Preferred orchestration patterns for MADtech use cases (MCP Client driven, MCP Server driven, MCP Tool driven, hybrid approaches)
• MCP Client SDK extensions for MADTech use cases including governance
• MCP Registry filters and certifications for MADtech 

Without these marketing-specific standards, every AI system constructs its own terminology AND technology. When these systems attempt to interoperate, translation breaks down and value diminishes.

The Strategic Land Grab Opportunity

The enthusiasm for MCP isn’t just about novelty – it’s about solving real pain points in AI deployment. As recent analysis from the developer community shows, by early 2025, teams realized that building intelligent agents was simpler than connecting them to real-world data.

The organizations that define MCMAP today will:

• Shape reference implementations that others follow
• Influence component marketplaces and ecosystem development
• Control metadata layers that enable trust, transparency, and optimization
• Establish governance frameworks for marketing AI compliance

This represents a once-in-a-decade opportunity to establish industry standards rather than adapt to them. The companies that move first will architect the foundation that others build upon.

Beyond Standards: Building the Marketing AI Ecosystem

The open-source nature of MCP has fostered a vibrant developer community, leading to continuous enhancements and a growing repository of tools and integrations. MCMAP would accelerate this trend specifically for marketing applications.

Imagine browsing a marketplace where you can discover, evaluate, and deploy marketing AI components like app store applications. A/B test different attribution models. Swap personalization engines without rewriting integration code. Deploy new optimization algorithms and measure performance impact immediately.

This isn’t speculative – it’s the logical evolution of MCP adoption in marketing technology. On Monday, I’ll tie it all together and pull out my crystal ball.  If you’re in Cannes the week of June 16th, and want to chat about any of this, drop me a note at djaye@aqfer.com.  Thanks for reading.