AI (artificial intelligence)

AI is increasingly finding its way into field service management and promises to make many processes smarter and more efficient. In practice, AI in FSM covers various areas of application: automated planning, predictive maintenance, chatbots & self-service, image recognition, and data analysis, to name just a few.

AI is currently gaining ground in areas such as scheduling, data analysis, and self-service in field service. This means, for example:

• Resource planning & route optimization: AI algorithms can help find the best technician for a job (taking into account skills, availability, location) and plan the best route. Even highly experienced dispatchers reach their limits in complex scenarios.
• AI can check millions of possible combinations in seconds and, for example, provide a suggestion that minimizes travel times and maximizes service levels. This allows more jobs to be completed per day or improves punctuality. Some FSM systems already integrate optimization engines based on AI/ML.
• Predictive maintenance: AI can use sensor data or historical fault reports to predict when a failure is likely to occur. Instead of waiting for rigid intervals, an AI model learns, for example, that machine X is highly likely to suffer a bearing failure after ~1200 operating hours. The system could automatically generate a service order before the failure occurs. This greatly increases plant availability.
• Assistance systems & self-service: Chatbots or virtual assistants, often AI-driven, can handle simple customer inquiries or provide assistance to technicians in the field. For example, a chatbot in the customer portal that decides whether to create a ticket or provide the customer with self-help information based on a description of the error. Or an AI-supported knowledge management tool that offers the technician similar cases and their solutions from the knowledge database based on the current order.
• Image recognition and AR: AI can analyze images and videos, such as recognizing a photo of a defective system (computer vision) and drawing conclusions from it. In connection with FSM, this can be seen, for example, in damage detection (the AI recognizes components and suggests replacement parts).
• Process optimization: Machine learning can be used to identify hidden patterns in service data. Example: AI analysis of all service reports could show that a certain spare part has to be replaced unusually often after a short time. Or it may turn out that certain combinations of skills within the team lead to faster solutions, which can be taken into account in resource planning. Such statistics and patterns would be almost impossible to discover using conventional means.

The integration of AI offers customers added value: they can make their service more proactive, faster, and more cost-efficient.

Important: AI is not a replacement for people in service, but a tool. The experience of service managers and technicians remains central. AI provides suggestions or analyses that are evaluated by humans. In addition, AI must be fed with sufficiently good data. Clean master data and complete documentation in the FSM are essential for this.

Data protection and acceptance are further aspects: Not every customer wants AI to evaluate their machine data in the cloud, for example, and not every employee blindly trusts a computer suggestion. AI is therefore being introduced gradually and transparency is provided about its decisions.

Vision for the future: In a few years, much of the dispatching work could be prepared by AI systems. The dispatcher primarily monitors the suggestions and only intervenes in special cases. Spare part requirements could be generated automatically from predictions and ordered in good time.

Initial successes show that AI brings real benefits to field service: more efficient processes, less downtime, and higher customer satisfaction thanks to proactive problem solving.