Model Context Protocol: MCP as an Infrastructure for AI Integration

Why a Development Standard Becomes a Strategic Issue

In recent months, one term has been cropping up more and more in strategic discussions about AI: Model Context Protocol, or MCP for short. What might initially sound like a topic for development teams actually has business implications that extend far beyond that. That’s because MCP offers an efficient way to connect AI assistants to a company’s business systems in many scenarios—including CRM, ERP, knowledge databases, product catalogs, and customer data.

This article explains why MCP is relevant to decision-makers without delving too deeply into the technical details.

The problem that MCP solves

Until now, connecting an AI assistant to an internal system was, in most cases, not easily possible. An internal system could only be connected to an external assistant if the respective provider offered a suitable integration. If none was available, that route was closed off and the only alternative was time-consuming in-house development. And even where integrations existed, each one was a siloed solution. Anyone wanting to connect ChatGPT to their CRM needed a different connection than for Claude or Gemini. As the number of models and tools grew, the effort and maintenance required multiplied accordingly.

A simple illustration: Before MCP, every connection was a custom-built plug. MCP works like a USB standard for AI. Once implemented, it can be used by any compatible assistant. This isn’t just a marketing metaphor. Even companies like Cloudflare report that integration efforts can be significantly reduced.

Why this issue is gaining momentum right now

MCP was released by Anthropic as an open standard in late 2024. By early 2026, it had become one of the fastest-adopted technical standards in recent years:

• Anthropic, OpenAI, Google, and Microsoft support the standard.
• Governance is handled by the Agentic AI Foundation under the umbrella of the Linux Foundation, a sign of neutrality and investment security.
• There are now hundreds to thousands of publicly accessible MCP servers and integrations.
• Industry analysts expect that by the end of 2026, a significant portion of API gateway providers will offer MCP capabilities or comparable mechanisms.

With this, MCP has crossed a threshold. It is no longer a bet on the future, but a requirement that is becoming evident in RFPs, architectural decisions, and partner agreements.

What's Changing for the Business

This will result in three specific shifts for businesses:

1. The time-to-market for new AI use cases can decrease significantly in the medium term. Although the initial setup of the MCP interface takes time, this investment pays off. Once the interface is set up, new requests can increasingly be handled through it without having to develop a separate endpoint each time. This is how MCP delivers value step by step, as the effort required decreases with each additional use case.
2. Dependence on individual AI providers can decrease. Until now, switching providers was only possible to a limited extent anyway, since connections were tied to the integrations of a specific provider. MCP decouples the connection from the specific model. This makes switching providers later on easier and strengthens companies’ negotiating position.
3. In-house data becomes a platform. Once internal systems are accessible via MCP, they can be made available not only internally but also—depending on the business model—to partners, customers, or external AI assistants. This gives rise to new sales and service channels.

When Caution Is Warranted

This rapid adoption has a downside. MCP was not primarily designed with enterprise security and governance requirements in mind. Industry analyses warn of uncontrolled growth in access rights, manipulated integrations from open directories, and a lack of traceability regarding which assistant accessed which data.

Gartner has been warning for years that a high proportion of agent-based AI projects could fail by 2027—not because of the technology itself, but due to unclear benefits, rising costs, and weak governance.

For senior management, this means that MCP is a strategic decision, not an IT detail. Those who fail to define guidelines for access, approval, and auditing risk precisely the kind of data exposure that AI was actually meant to turn into productive value.

MCP is here to stay. It is becoming the silent infrastructure that determines how effective AI will be within a company.

valantic: Your Partner for AI-Driven Integration

valantic combines many years of experience in enterprise architectures with proven expertise in AI and commerce platforms. We help companies leverage the opportunities offered by MCP in a targeted manner—from strategic positioning and the selection of relevant use cases to secure integration into existing system landscapes. Our focus is on measurable business value, clear governance, and an architecture that remains compatible with future standards.

Feel free to contact our expert Fabian Ritter to discuss how MCP can be integrated into your AI and integration strategy: