AI in Service Why Networking of AI is More Important than Algorithms

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Many companies place their hopes in AI, but it often fails due to isolated systems and a lack of networking. Why AI doesn't work without continuous processes and what role platforms play in this.

Hardly any technology discussion today can do without AI. Whether it's predictive maintenance, automated service processes or smart spare parts optimization—AI promises great efficiency gains. But a closer look quickly reveals: Most companies are not struggling with a lack of algorithms, but with isolated IT landscapes. Machine and sensor data do exist, but they are scattered across different systems without a meaningful connection. The result is an AI without practical use that remains a theoretical concept.

Many companies invest in digital solutions with AI functionality—but often the results fall short of expectations. Not because AI models are bad, but because they have to work with incomplete or unconnected data. The consequences are machine and maintenance data that reside in separate systems without real-time reconciliation. Additionally, spare parts lists and service protocols are stored as PDFs or Excel files without structured access, and sensor data exists but cannot be linked with other relevant information. In this environment, AI cannot make accurate predictions, automate processes, or provide any added value.

Why AI Needs More than Algorithms

The fundamental problem is not the algorithm, but the lack of data linkage. AI only works when systems collaborate seamlessly. This means that machine and service information must be pooled in a common platform so that all relevant data is available in one place.

In order to enable AI-supported analyses at all, processes must also be synchronized across system boundaries. Data flows must also be standardized so that machines, spare parts catalogs, and maintenance histories do not exist in isolation. AI needs access to consolidated data spaces—only then can it work reliably.

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Practical example: AI in Documentation

A common problem in service is the time-consuming access to technical documentation. In practice, this means: A service technician spends minutes searching for information that could help him, but is buried in confusing PDFs or old Excel lists.

Here, a real AI application with added value is revealed:

• Service technicians enter a question and instantly receive the appropriate information. This allows them to directly access the documentation,

• The technician receives targeted solution suggestions instead of flipping through manuals, thus speeding up troubleshooting,

• By interacting with maintenance histories and spare parts catalogs, AI can utilize linked data sources and thus provide more precise answers.

Concrete Use Cases from Everyday Service

The following use cases demonstrate that AI adds value where it is integrated into existing processes.

• A technician needs to reset a controller. Instead of going through a 300-page manual, he asks the AI directly and gets the exact procedure in seconds,

• A customer is looking for a specific spare part but only knows the old part number. The AI matches the data and immediately provides the current model with a purchase option,

• A service team analyzes recurring faults. Through AI-supported analysis of maintenance histories and error messages, patterns can be recognized and causes can be rectified more quickly.

Why Predictive Maintenance is often Overrated

Predictive maintenance is often touted as the ultimate AI promise: sensors capture machine data in real-time, AI models recognize patterns and predict failures. Yet, in practice, implementation often falls short of expectations. Why?

1. Data basis insufficient: Sensor data alone is not enough. Without context (historical maintenance data, error analyses, component information), the meaningfulness is limited. An AI can only predict a machine failure if it knows all the pieces of the puzzle—from past maintenance to the current part quality. If this information is missing, it remains a guessing game.

2. lack of networking: If sensor data is not connected to service processes, it remains useless. An anomaly detected early is of no help if no one knows which spare part is needed or if a technician is available.

3. Economics: Not every machine needs predictive maintenance. In many cases, a well-thought-out preventive maintenance strategy is sufficient.

Predictive maintenance is not the universal solution—it is only useful when the entire service processes are aligned with it.

How Companies Successfully Use AI in Service

Artificial intelligence only unfolds its full potential when the fundamental processes are already digitized. Companies should therefore first optimize their data and service processes before implementing AI-supported solutions. Because AI does not replace digitization—it builds on it.

Another key success factor is the networking of data sources. As long as information is stored in isolated systems, efficiency gains are absent since important correlations cannot be recognized and utilized. Ultimately, the use of AI also requires the strategic involvement of employees. New technologies change workflows and processes—therefore, it is crucial to create acceptance early and train the teams accordingly. In short: AI only works if it operates with networked processes—not with data silos.

Only when AI, networked systems, and well-prepared employees work together can real progress in service be realized. But what does this look like in practice? Here are some examples of successful AI applications in service:

• Modern AI-powered search functions allow service technicians to find important information in seconds instead of struggling through documents,

• Automated ticket categorization intelligently prioritizes requests, allowing urgent cases to be processed faster.

• Digital spare parts catalogs with intelligent search prevent incorrect orders and facilitate the identification of suitable components.

First Connect Processes, then Use AI Meaningfully

AI can fundamentally transform service—but only if the structures are right. Companies wanting sustainable success with AI in service must first network their data processes and connect systems. Because without a solid foundation, AI remains a theoretical concept—with it, it becomes a real competitive advantage.