The AI chatbot is supposed to uncover new sales opportunities but fails due to lack of access to CRM data. This scenario shows: Generative AI only delivers real value when it can access multiple systems. An open standard as a translator transforms isolated AI solutions into intelligent partners.
The unstoppable wave of isolated AI agents presents challenges for companies. As the introductory example shows, AI agents need access to company data to truly create value. Yet systems such as CRM, DMS, or ticketing tools often come with their own AI chatbots. To implement AI strategies at scale, a central bridge is needed that enables reusable access to system data.
The Solution: MCP as a Common Language Standard
This bridge is called Model Context Protocol (MCP). This model overcomes the new AI silos and acts as a translator between AI agents and IT systems. Its true value lies in its ability not only to read data but also to perform targeted actions within the systems. This can range from creating an order, updating a customer record, to scheduling an appointment. MCP forms the unified communication layer that reliably makes this possible. The principle is simple and effective: the AI states what it wants; MCP translates it and ensures the system understands and correctly executes the request. Existing software does not need to be modified, which protects investments and makes AI strategy future-proof.
The Benefit: Orchestration Instead of AI Agent Chaos
This is how the vision of a connected AI landscape becomes reality. Instead of many isolated agents, an intelligent ecosystem emerges. This results in key strengths that make MCP the key to connected AI:
Start Small, Grow Sustainably:
With MCP you can start small by connecting two systems and one AI application. The architecture is designed to grow step by step with your requirements. New systems and AIs can be added modularly.
Free Choice of AI Model:
MCP decouples system integration from the choice of language model. You can use different AI models such as those from OpenAI, Anthropic, or even Open Source, without having to reintegrate the connected systems. This independence is a strategic advantage in a rapidly evolving market.
Central Control and Security:
In a world of autonomously acting AI agents that directly interact with your systems, central control is essential. MCP gives you authority over which AI can access which data and perform which actions. This creates transparency, compliance, and security.
Future-Proof Through Standardization:
MCP is open source, allowing extensions and technological adaptation to remain flexible and license-free in the long term. Major providers such as Microsoft or Atlassian are already beginning to adopt the standard.
Success Comes from People and Technology
MCP solves the technical integration problem and makes ideas finally achievable. However, the real transformation only begins when employees trust the new possibilities, actively use them, and adapt their way of working accordingly. Only when people understand AI applications and integrate them into their daily work do real benefits emerge, such as time savings, new business ideas, and more joy at work.
Interview:
The Model Context Protocol (MCP) bridges the gap between AI systems and business applications. Mirco Stoffel, Head of AI Business Consulting at Axians IT Services, explains how the new AI applications can become successful with employees.
What technical requirements must be met so that existing IT systems become MCP-compatible without changes to the software itself?
Mirco Stoffel: As long as the system has an accessible interface – such as an API – no changes to the software are necessary to make an IT system MCP-compatible. It should be noted that each MCP server must either be developed in-house or an already available one – for example, provided by the vendor – can be used.
How does MCP handle domain-specific requirements – such as industry-specific data formats or highly individual business processes?
MCP addresses domain-specific requirements through individually adaptable MCP servers that function as adapters between the system and AI agents. Companies can, for example, map industry-specific data formats without the overarching AI agent needing domain-specific knowledge.
Critical voices warn of security risks related to agent-based AI. The often extensive system permissions that AI agents have could easily be misused. How can MCP be implemented securely?
MCP enables security standards such as authentication, encryption, or monitoring. In addition, tools can be designed so that they only perform specific actions or require user approval for sensitive operations.
What concrete measures do you recommend for companies to strengthen employee trust in AI applications – especially when the systems make autonomous decisions?
We recommend that companies establish open and honest communication and actively promote skill development among employees. Another important tool is the implementation of a “human-in-the-loop” strategy, firmly anchored in AI governance. This measure precisely defines when and how employees can review, validate, or manually override autonomous AI decisions, ensuring they remain in control and do not feel at the mercy of the technology.
How can employees be meaningfully involved in the design of AI-supported processes to foster acceptance and reduce resistance?
First, companies should conduct process workshops with directly affected employees. In these, existing workflows are jointly analyzed, bottlenecks identified, and ideas gathered on where AI could provide support. This is followed by the co-design phase, in which interdisciplinary teams – consisting of subject matter experts and AI specialists – jointly design the new AI-supported target process. Visual tools such as process maps or user journeys are used to make the future collaboration between humans and AI tangible.
In your view, which skills and roles will be particularly important for employees in a system landscape interconnected by MCP and AI?
In the area of GenAI, the role of knowledge engineers and symbolic AI concepts is becoming increasingly important. Knowledge must be structured in such a way that AI systems can use it effectively and make traceable decisions. In addition, data literacy – i.e., understanding data quality and interpretation – as well as critical thinking and an awareness of AI ethics will be essential to responsibly evaluate and use the results of AI applications.
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About Axians
Axians, the ICT brand of VINCI Energies, supports private and public companies, network operators, and service providers in modernizing their digital infrastructures and solutions. Whether business applications or data analytics, corporate networks, shared workspace, data center and cloud service infrastructures, telecommunications infrastructures, or internet security – Axians is a specialist in all current information and communication technologies! Through consulting, planning, integration, and a broad range of services, Axians unlocks the full value of these technologies in the form of customized solutions for customers.
2024: €3.7 billion revenue // 16,600 employees // 38 countries
www.axians.com
In Switzerland, 1,113 employees at 32 locations work under the ICT brand Axians of VINCI Energies.
www.axians.ch
About VINCI Energies
In a changing world, VINCI Energies accelerates the ecological transition by actively shaping two profound transformations: digitalization and the energy transition. As a market-oriented integrator of customized, cross-technology solutions, we support our customers in implementing technologies, from planning through realization and operation to maintenance. With our 2,100 regionally anchored, agile, and innovative business units, we are involved in our customers’ energy-related decisions, infrastructures, and processes, ensuring greater reliability, efficiency, and sustainability every day. VINCI Energies strives for a holistic performance that respects the environment, creates value, and promotes our solidarity commitment.
2024: €20.4 billion revenue // 102,600 employees // 2,100 business units // 61 countries
www.vinci-energies.com