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Summary – Integrating AI into business processes without multiplying custom developments or increasing technical debt remains a challenge for Swiss decision-makers: how to deploy relevant, secure, and modular smart agents quickly? With the Model Context Protocol, you standardize exchanges between AI, CRM, ERP, and repositories, ensure real-time access, traceability, and seamless scalability—all while avoiding vendor lock-in.
Solution: adopt MCP to accelerate your POCs, industrialize universal connectors, and optimize costs and flexibility.
Model Context Protocol (MCP) is an open standard designed to connect any AI agent to your data and tools in real time, making it more effective and relevant. Launched in November 2024 by Anthropic—the company behind the Claude AI service—MCP defines a common language to guide the AI to the right sources and actions, whether it’s an in-house model (custom AI hosted on-premises) or a third-party API such as ChatGPT or Claude. This enables the AI to interact with multiple systems and deliver much broader capabilities. For decision-makers and technology leaders, MCP means rapid deployment of intelligent (or AI assistant) agents that are contextually relevant and secure, without sacrificing business agility or increasing technical debt.
MCP: A Contextual Protocol for Ecosystem-Connected AI
The MCP protocol stands apart from classic approaches by standardizing exchanges between AI and enterprise systems, providing instant, secure access to business data and automated triggers within your IT landscape.
MCP acts as a universal translator: it turns an AI agent’s request into calls to databases, CRMs, ERPs, document repositories, or any other part of your IT stack, then returns structured context to the model. Where every new integration once required bespoke code, MCP lets you build one connector that works with all compliant tools. This openness accelerates evolution of your system while minimizing maintenance costs.
By choosing a widely adopted open-source standard like MCP, you avoid vendor lock-in and retain full control over your connectors and models. Plus, the MCP community continuously enriches adapters—whether for enterprise AI platforms or open-source frameworks—ensuring sustainable interoperability. Today, this standard has become essential for anyone integrating AI into their business processes and value chain.
High-Performance, Scalable, Customizable, and Secure AI Agents
MCP enables you to build intelligent agents that draw on real-time data from your key systems and orchestrate complex processes, while delivering modularity, scalability, and security.
Here are some examples of what MCP can bring to organizations that integrate it effectively:
- Performance & Relevance
MCP-powered agents can query your CRM, document management system, or application logs to generate context-aware responses, greatly increasing the business relevance of model outputs. - Scalability
The standard protocol makes it easy to scale (adding new sources, handling increased traffic) without a full redesign—offering flexibility and true scalability. - Customization
Each agent can be configured to access only the required business data and actions, optimize its tone and governance rules, and comply with regulatory requirements. This boosts flexibility and contextualization of your model. - Security
MCP includes built-in authentication and auditing mechanisms under your control. No black-box data flows—every exchange is logged and access-restricted according to defined permissions. In Switzerland, and particularly in AI contexts, this level of security is crucial.
Enterprise Use Cases for MCP
From customer support to cybersecurity, and from administrative processes to IT operations, MCP powers AI agents that precisely address your business challenges.
- Customer Support
Deploy a virtual assistant that consults the CRM and knowledge base in real time. Contextualized replies can cut first-level ticket volume by up to 30 %. - HR/IT Automation
An “Onboarding” agent can automatically create user accounts, send welcome emails, and update the ERP based on an HR form—freeing IT from repetitive tasks. - Proactive Industrial Maintenance
An MCP agent monitors critical machine metrics (or servers) via SCADA, IoT, or supervision systems, predicts failures through trend analysis, and auto-generates preventive maintenance orders in a CMMS—reducing unplanned downtime by 20 %–40 % and extending equipment life. - Cybersecurity
An automated watcher correlates SIEM alerts and event logs, notifies analysts, and suggests actionable remediation plans—improving average response times by 40 %. - Business Intelligence
A conversational tool can query your data warehouse and reporting systems to deliver on-demand dashboards and ad-hoc analyses without mobilizing data analysts.
These five examples are generic; the possibilities are endless and depend on each company’s challenges and resources. While standalone AI could automate certain time-consuming tasks, MCP supercharges automation by enabling AI to understand context, personalize its work, and interact precisely with its environment—making it far more effective in handling parts of your value chain. MCP will therefore play a key role in task automation and optimization in Switzerland and internationally in the coming months and years.
Edana: strategic digital partner in Switzerland
We support companies and organizations in their digital transformation
How MCP Works (For Technical Readers)
MCP relies on exchanging JSON messages between the AI agent and business connectors, orchestrated by a lightweight broker:
- Initial Request
The user or application sends a question or trigger to the AI agent. - Context Analysis
The agent, equipped with an appropriate prompt, wraps the request in an MCP envelope (with metadata about the user, application, permissions). - Broker & Connectors
The MCP broker reads the envelope, identifies required connectors (CRM, ERP, document store, etc.), and issues REST or gRPC API calls per a simple, extensible specification. - Data Retrieval & Aggregation
Connectors return structured fragments (JSON, XML, protobuf), which the broker assembles into a single, rich context. - AI Model Invocation
The AI agent receives the full request and context, then queries the model (hosted locally, in your private cloud, or via an API such as OpenAI) to generate the response or next actions. - Execution & Feedback
For action steps (ticket creation, email dispatch, etc.), the broker relays commands to target systems and can return an execution log for auditing.
This workflow is completely vendor-agnostic: you can host an open-source speech-to-text model in-house for call center interactions, or use the OpenAI API for NLP, depending on business context and cost or time constraints.
Challenges & Best Practices for Successful MCP Deployment
To guide technical and business teams through concrete implementation of the protocol while anticipating key pitfalls, we recommend following these steps:
1. Define Your Functional Scope
- Map priority use cases (customer support, maintenance, BI…)
- Identify target systems (CRM, ERP, SCADA…) and access constraints (authentication, throughput, latency)
2. Governance & Security
- Establish fine-grained access policies: which agents can query which data, under what conditions
- Implement continuous MCP call auditing (centralized logs, anomaly alerts)
3. Technical Pilot & Rapid Prototyping
- Start with a PoC on a simple case (e.g., CRM-connected FAQ assistant)
- Measure end-to-end latency and functional enrichment delivered by MCP
4. Industrialization & Scaling
- Deploy a resilient MCP broker (high availability, load balancing)
- Version and test business adapters (unit/integration tests)
5. Continuous Monitoring & Optimization
- Dashboards tracking:
- Number of MCP calls per day
- Average response time
- Error or integration-failure rate
- Collect user feedback (internal NPS) to refine and prioritize new connectors
Edana’s Approach: Flexible Solutions
Edana combines the best of open source, third-party APIs, existing tool integration, and custom development to address each business context.
We naturally favor open standards and open-source building blocks to limit costs, avoid vendor lock-in, and optimize total cost of ownership. However, when time-to-market, budget, or complexity constraints demand it, we integrate proven solutions: hosting an open-source speech-to-text model for call centers, leveraging the OpenAI API for rapid NLP understanding, or coupling with a third-party computer-vision service… With MCP, these elements mesh seamlessly into your ecosystem without adding technical debt.
Our methodology applies a variety of technology approaches tailored to maximize ROI and ensure robustness and longevity of your solutions.
As ecosystem architects, we prioritize security, scalability, and sustainability across all your AI agent platforms. We factor in your CSR commitments and corporate strategy to deliver responsible, high-performance AI aligned with your values and specific business needs—accelerating your digital transformation without compromising on quality or data control.
Ready to automate your business processes without sacrificing quality—in fact, improving it? Not sure where to start? Our experts are here to discuss your challenges and guide you end-to-end.
