Model Context Protocol transforms archives into information sources for AI

When AI initiatives fail, it is not so much because of the models themselves, but rather due to insufficient data quality and a lack of context.

A key problem is that AI models often work in isolation from the systems in which relevant company knowledge actually resides - such as SAP, collaboration platforms or archive systems. 

This is precisely where a rethink is currently taking place: The standardized, secure and governance-compliant integration of artificial intelligence into existing information landscapes is becoming increasingly important. The Model Context Protocol (MCP) plays a key role here.

From the integration problem to a clear architecture

Until now, the integration of artificial intelligence into company systems has usually taken place via individual interfaces or proprietary connectors. This led to a classic N×M problem: many data sources meet many AI applications - with a correspondingly high level of complexity, inconsistent security concepts and rising costs.

MCP offers a structural solution for this. Instead of numerous point-to-point connections, it establishes a standardized link between AI clients and data or tool providers. MCP servers provide functions and data in a controlled and standardized manner. 

The result: a clearer architecture, better reusability and a significantly higher governance capability.

Archives as strategic context providers

At the same time, the role of archive systems is changing fundamentally. They are no longer just passive storage, but are developing into central sources of information for AI applications. In many companies, archives are the only place where documents are stored in an audit-proof manner, enriched with structured metadata and clearly assigned to business objects. In addition, access logs provide valuable information about usage patterns. Such signals are crucial for modern AI systems, as not only content but also its context, relevance and timeliness play a role.

Dynamic authorizations instead of static assumptions

As soon as archive data is integrated into AI processes, the focus shifts to authorization management. Two misconceptions persist: firstly, that AI requires comprehensive access to all data, and secondly, that authorizations are applied only once. In reality, roles, responsibilities and project assignments are dynamic. Static authorization models therefore quickly lead to inconsistencies. What is needed is an architecture that continuously obtains access rights from the leading systems and keeps them up to date - a function that kgs combines with MCP. Another advantage of the kgs solution for MCP is the use of additional context information such as metadata, document classes, status attributes or access frequencies. This opens up new possibilities, for example for anomaly detection or context-based analyses. Even though chatbots are often used as an introduction to AI projects, they often lead to isolated shadow solutions. An approach with a clear division of tasks is more sustainable: archives provide content and reliable evidence, leading systems such as SAP control authorizations and process context and AI components analyse and consolidate the information. 

Conclusion

The decisive progress lies less in increasingly powerful models than in the underlying architecture. Companies should rely on integrated, governance-capable context architectures such as MCP. Only the ability to integrate AI securely, standardized and closely with existing corporate knowledge makes its use truly effective. (Source: KGS)