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As AI becomes part of everything—from customer service to logistics—the real challenge isn’t just building smarter models. It’s getting them to work together.
According to Alex Fillimonov, Product Owner of AI Projects at Albato, the future of AI isn’t about standalone tools, but about how well they connect and integrate.
“In most enterprises, AI systems are still stitched together with custom scripts, brittle APIs, and siloed tools,” he explains. “That approach worked when AI was experimental. But now, it’s mission-critical—and it has to scale.”
That’s where MCP (Model Context Protocol) comes in. It marks a shift toward unified orchestration, where models, tools, and data sources can collaborate through a single, shared protocol.
In this article, together with Alex, we’ll explore how MCP can become the backbone of enterprise AI—and why now is the time to adopt it.
What is MCP in simple terms?
A Model Context Protocol (MCP) is like a universal connector between AI models like ChatGPT or Grok and your business tools or data.
You can think of it as similar to an API, but easier and more flexible.
- An API connects one system to another with specific code.
- An MCP lets the AI connect to many systems in a standard way — without custom coding each time.
So instead of building a new integration every time you want AI to use your CRM, database, or email system, MCP makes it plug-and-play.
It’s convenient because:
- You can link your tools once, and any AI that supports MCP can use them.
- It saves development time and cost.
- It keeps data secure — the AI only sees what you allow through the protocol.
In short: MCP is a smarter, more flexible version of an API, made for AI. It makes it easy for AI to work with your existing business systems safely and quickly.
This might sound like MCPs are going to make all integration platforms redundant, which is not a good news for many companies out there. But the truth is: MCP helps AI connect to data and tools, but it doesn’t replace the automation, workflow management, and data transformation that iPaaS provides.
MCP is about access—giving AI models a way to understand and use connected systems. iPaaS is about orchestration—moving data between systems, keeping everything in sync, and managing complex business logic.
In fact, MCP makes iPaaS more valuable: integration platforms can expose their connected apps through MCP, so AI can use them instantly.
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How Model Context Protocol works
We have already explained more about how MCP works and what it consists of in this blog about Model Context Protocol.
But briefly here's how MCP helps connect AI models and AI agents with tools and data.
MCP uses a client–server model, just like an API.
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The client is the AI model (for example, ChatGPT or Grok).
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The server is any system or app that provides data or actions — like your CRM or database.
When the AI needs information, it sends a request to the MCP server. The server replies with the needed data or performs an action.
But there are also differences from and API:
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MCP is built specifically for AI models, not for human developers.
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It uses a standard, open format that lets any AI understand any MCP-enabled service without custom coding.
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It focuses on context sharing — giving AI the right data safely and efficiently during a conversation or task.
Why MCP is emerging now
The Model Context Protocol (MCP) is gaining attention because AI systems are shifting from isolated models to agents that can talk to each other and work together behind the scene. This shift requires a shared standard for how models exchange context, not just prompts.
As companies begin to deploy multiple AI agents that need to work together — for example, a CRM assistant pulling data from Gmail or a research agent updating a company knowledge base — fragmented integrations become costly. Without a standardized protocol, every connection turns into a custom engineering project, slowing down innovation.
In this sense, MCP is to AI agents what HTTP was to the web: an interoperability layer. It enables a future where different models and tools can seamlessly integrate without requiring manual glue code. For developers, it means faster prototyping and cleaner architectures, for users―smarter, more consistent AI experiences across platforms.
What are the key benefits of MCP?
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Faster AI adoption
MCP makes it easy to connect AI models to existing business systems without custom development. Companies can start using AI with their tools—CRM, ERP, analytics, etc.—much faster. -
Lower integration costs
Because MCP uses a standard format, one connection can work across multiple AI models. Businesses save time and money compared to building separate APIs for each use case. -
Secure data access
MCP only shares the data the company allows. That means AI can use business data safely, without exposing sensitive information. -
Better automation and insight
By linking AI directly to real business data, MCP lets models analyze information, trigger workflows, and assist in decision-making — improving efficiency and accuracy. -
Future-ready flexibility
As more AI tools support MCP, businesses won’t need to rebuild integrations. They can switch or add AI models easily, keeping systems compatible and scalable.
Do you need to implement MCP for your integrations?
Gartner predicts that by 2026, 40% of enterprises will have formalized multi-model AI strategies to improve agility and resilience. MCPs are the infrastructure layer that will make this feasible.
We're entering an era where the AI stack must resemble the cloud-native stack: composable, portable, and intelligent by design. Check out our state of integrations report for more insights.
Summing up
AI agents and automation are transforming the way businesses operate. With tools like MCP, companies can connect multiple AI agents, access real-time data, and make smarter decisions faster. This approach reduces complexity, saves time, and enables teams to focus on higher-value work rather than manual tasks.
If you’re ready to see what AI can do for your team, now is the perfect time to dive deeper into AI automation and connected AI tools.
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