You're deciding whether to keep building point-to-point integrations between your AI tools and enterprise systems — or adopt a standardised protocol that every major AI vendor can connect to. The architecture decision you make in the next six months will determine how much your AI investments cost to maintain in 2027 and beyond. Model Context Protocol is the standard that changes this calculus.
What Is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open standard developed by Anthropic, released in November 2024, that defines how AI models connect to external data sources, tools, and enterprise systems. Think of it as USB-C for AI integration: a single, standardised connector that works across vendors, replacing the need for custom integrations with every new AI tool you deploy.
Before MCP, connecting an AI model to your CRM, document management system, or internal database required custom engineering work for each combination. An AI assistant connected to Salesforce needed different integration code than the same AI connected to ServiceNow. Every new AI tool meant new integration projects — and a growing maintenance burden that had nothing to do with delivering business value.
MCP solves this by introducing a common language between AI models and the external systems they need to access. An enterprise that builds an MCP server for its internal knowledge base can then connect any MCP-compatible AI model to that knowledge base — Claude, GPT-4, Gemini, or whatever model your organisation uses — without rebuilding the integration each time.
As of mid-2026, MCP has been adopted as a standard across major AI platforms including Anthropic's Claude, and is supported by connectors for GitHub, Slack, Google Drive, Salesforce, and dozens of other enterprise systems. It has become the de facto integration backbone for enterprise AI deployments.
How MCP Works: The Architecture Explained
MCP operates on a client-server architecture: an MCP client (the AI application) connects to MCP servers (connectors to external systems). Each MCP server exposes a standardised set of capabilities — reading files, querying databases, executing actions — that any compatible AI client can discover and use. This separates the AI reasoning layer from the data access layer, making each independently upgradable.
Understanding the MCP architecture helps enterprise leaders frame the right infrastructure questions. There are three components.
MCP Hosts
The AI application or environment where the AI model runs. This could be Claude Desktop, a custom enterprise AI application, or an AI-powered tool like an intelligent document processor. The host initiates connections to MCP servers and manages what resources the AI can access.
MCP Clients
The component within the host that communicates with MCP servers using the protocol. Clients handle the connection lifecycle, send requests for data or tool execution, and receive results back into the AI context.
MCP Servers
Lightweight connectors that expose specific systems to AI models in a standardised way. An MCP server for your internal SharePoint library, for example, lets any compatible AI model search and retrieve documents from that library — without the AI needing direct database access or custom API logic.
The strategic implication: your IT team builds and maintains MCP servers for your core enterprise systems once, and every AI tool you adopt going forward connects through those same servers. The integration work compounds rather than multiplies.
Why MCP Changes the Enterprise Integration Equation
Before MCP, each new AI tool required a separate integration project for every enterprise system it needed to access — creating an exponential maintenance burden. MCP reduces this to a one-to-many model: build the connector once for each enterprise system, and every compatible AI tool connects through it. This fundamentally changes the economics of enterprise AI adoption.
The pre-MCP integration problem is one that every Head of IT or COO who has deployed AI at scale has encountered. Your pilot went well. Then you tried to connect the AI to your actual enterprise data — customer records, internal policies, project documentation — and the integration work doubled the project timeline and cost.
With MCP, the integration economics shift in two ways.
First, the build-once principle. An MCP server for your Salesforce instance, built once by your IT team or a technology partner, can serve any MCP-compatible AI model. When you switch from one AI vendor to another — or run multiple AI tools simultaneously — you do not rebuild Salesforce connectivity. The MCP server handles it.
Second, the vendor ecosystem effect. Because MCP is an open standard, technology vendors are building and publishing MCP servers for their own products. Salesforce, GitHub, Slack, Atlassian, and many others have released or are building official MCP servers. Your organisation can adopt these rather than building from scratch, dramatically reducing the integration burden.
For a mid-market organisation with 50 to 200 enterprise systems and growing AI ambitions, this is not an incremental efficiency gain. It is a structural change in how AI integration scales.
MCP vs. Traditional API Integration: The Strategic Difference
Traditional API integration is point-to-point: each AI tool connects to each enterprise system via custom code. MCP is a hub-and-spoke model: enterprise systems expose MCP servers once, and any compatible AI tool connects through them. The difference is not technical sophistication — it is maintenance scalability and vendor flexibility over time.
This comparison matters because most enterprise AI projects in Hong Kong today are still being built with traditional API integrations. Here is the decision framework.
When Traditional API Integration Is Appropriate
You have a single, stable AI tool that you expect to use indefinitely. The integration is to a system with highly specific, non-standard requirements. You need maximum control over every aspect of the data flow. In these narrow cases, a custom API integration may deliver more precision than an MCP server.
When MCP Is the Better Architecture
You are deploying multiple AI tools across your organisation. You expect to change or add AI vendors over the next two to three years — a near-certainty given how rapidly the market is evolving. You want your enterprise systems to remain accessible to AI regardless of which AI platform you use. You want to reduce the ongoing engineering cost of maintaining AI integrations.
For most Hong Kong enterprises with genuine AI ambitions, MCP is the right foundational architecture. The question is not whether to adopt it, but when and how.
Enterprise Use Cases Where MCP Creates Real Value
MCP creates the most value in use cases that require AI to access real-time enterprise data rather than working from a static training snapshot. High-value applications include intelligent document retrieval, customer service AI with live CRM access, internal knowledge assistants, and AI-powered compliance monitoring against current policy libraries.
The following use cases represent where MCP-enabled AI integration is generating measurable results in enterprise environments.
Intelligent Document Retrieval and Summarisation
Legal, compliance, and professional services firms are using MCP to connect AI to their internal document libraries. An AI model can retrieve the relevant policy, contract, or precedent, summarise it in context, and surface it within a workflow — without any document leaving the secure internal environment.
Customer-Facing AI with Live CRM Context
Customer service teams are deploying AI assistants that connect to CRM systems via MCP, giving the AI real-time access to account history, open tickets, and product records. The result is AI-generated responses that are accurate to the customer's current situation, not a generic training data snapshot.
Internal Knowledge Assistants
Operations teams are building internal AI assistants that connect to HR systems, project management tools, and internal wikis via MCP servers. Employees can ask natural-language questions and receive answers grounded in current internal data — without IT building a new integration for each question type.
AI-Powered Compliance Monitoring
Financial services and regulated industries are using MCP to connect AI to live policy and regulatory libraries. The AI can flag potential compliance gaps in documents or communications by comparing them against the current version of applicable policies — not a version that was current at model training time.
Governance and Security: What Enterprise Leaders Need to Confirm
MCP servers require careful governance because they determine what data AI models can access within your enterprise. Key security considerations include: access control at the MCP server level, audit logging of all AI data access, data residency compliance, and preventing MCP servers from granting AI access beyond what the human user is authorised to see.
Enterprise security teams will raise legitimate questions about MCP. Here are the governance frameworks you need before deployment.
Access Control Scoping
MCP servers should expose only the data that the AI application genuinely needs. Principle of least privilege applies: if your customer service AI only needs to read CRM records, the MCP server should not expose write access or administrative functions. Each MCP server should be scoped to a specific use case, not built as a general-purpose connector to the entire system.
Audit Logging
Every AI data access through MCP should be logged — what data was retrieved, by which AI application, at what time, and under what user context. This audit trail is essential for security investigations, compliance reporting, and understanding how AI is actually using enterprise data in production.
Data Residency and PDPO Compliance
For Hong Kong enterprises, MCP deployments must ensure that data accessed through MCP servers does not leave jurisdictions that violate the Personal Data (Privacy) Ordinance (PDPO) requirements. If your MCP-connected AI model is hosted overseas, confirm with your legal team whether the data accessed through MCP constitutes a cross-border data transfer requiring compliance measures.
User Authorisation Inheritance
A well-designed MCP implementation respects the permissions of the human user on whose behalf the AI is acting. If a user does not have access to a particular document in SharePoint, the AI should not be able to retrieve that document via MCP on the user's behalf. Confirm that your MCP implementation enforces user-level authorisation rather than granting AI blanket system access.
Questions to Ask Before Adopting MCP in Your Organisation
Before committing to MCP-based integration architecture, enterprise leaders should confirm: which AI vendors and enterprise systems support MCP natively, who will own MCP server development and maintenance internally, how MCP fits into the existing API management and security framework, and what the migration path looks like for existing AI integrations.
The right architecture decision is not the one with the longest feature list — it is the one that your team can govern, maintain, and evolve as the AI market changes. Here are the questions that distinguish a sound MCP strategy from a premature adoption.
Which of your AI tools and enterprise systems have native MCP support? Build your MCP roadmap around systems where official connectors already exist — the maintenance burden is lowest and the security validation is done by the vendor.
Who owns MCP server development internally? MCP servers require engineering resources to build, test, and maintain. If your organisation lacks this capacity, you need a technology partner who can own this layer of your AI infrastructure.
How does MCP fit into your existing API management framework? MCP servers are not a replacement for your API gateway or identity and access management (IAM) systems — they work alongside them. Confirm that your security architecture can govern MCP connections with the same rigour as traditional API integrations.
What is your migration path for existing AI integrations? If you have custom API integrations for current AI tools, a phased migration to MCP reduces disruption. Define which integrations to migrate first based on maintenance burden and strategic importance.
How will you measure MCP's business impact? Define integration reliability, developer time saved, and time-to-deployment for new AI tools as key metrics. Without these baselines, you cannot demonstrate to your CFO or board that the architectural investment delivered value.
Ready to Build Your Enterprise AI Integration Architecture?
Understanding MCP is the first step. The next is assessing whether your current AI infrastructure is ready to adopt it — and identifying where to start. We'll walk you through every step: from AI readiness assessment and integration architecture planning, to MCP implementation and performance tracking, backed by 28 years of enterprise technology experience in Hong Kong.
