AI Technology in After Sales - 4 Use Cases

A service technician is called to a defective machine and needs to repair it as quickly as possible. But which is the correct spare part he needs to replace? In addition to experience and personal knowledge, AI-based technologies can provide support here. This is made possible, for example, by spare part recognition via photo: the service technician takes a photo of the removed part with his smartphone or tablet and sends the photo to a service. This service accesses a cloud-based, neural, trained network that was created based on all relevant spare parts information from the manufacturer of the defective machine. The service retrieves relevant results for the spare part with details of the probability of a hit.

The results are displayed directly in the digital spare parts catalog or service information system. By clicking on one of the results, the service technician jumps to the corresponding part in the spare parts catalog. Here he receives further information on the part, which is directly linked to relevant sections from the technical documentation and to the ordering option.

A typical use case of AI technologies and machine learning in service and after-sales is predictive maintenance. The basis for predictive maintenance is the permanent monitoring and evaluation of machine and process data. Real-time analyses in combination with big data are used to determine the current status of machines, systems and devices in operation. In combination with other parameters and information, algorithms are used to predict future maintenance requirements and the optimal time for performing them. Ideally, maintenance is carried out before a malfunction occurs, but only if it is actually necessary.

Whether predictive maintenance is the right maintenance strategy for a company depends on various factors. You can find more information about this in our blog post "Predictive Maintenance - Definition, Requirements and Advantages".

Less complex, but no less interesting, is the use of a digital customer portal with an intelligent chatbot that provides support in after-sales and service. The AI-supported chatbot or intelligent virtual assistant does not serve as a replacement, but rather as a supplement to "human" customer service. Common areas of use are processing routine inquiries, bridging the waiting time until a “real” service employee is available, or querying required data.

Advantages of AI-powered chatbots and intelligent virtual assistants:

• 24/7 availability: regardless of service hours, holidays, and weekends
• High processing speed and efficiency
• Relief of human customer service: time-consuming but simple routine tasks are taken over by chatbots

Like all AI-powered technologies, a chatbot only gets smarter with time and ongoing training. Nevertheless, it can already provide support at the beginning of the deployment if it uses a decision tree to answer standard textual questions.

According to the German B2B E-Business Report on the topic of "13 levers for a convincing after-sales service", 98% of the companies surveyed are pursuing the goal of networking different systems and software. The status quo often looks like this: Product information is distributed across different systems that are not networked and do not exchange data. Silo thinking is also widespread in the organization: The engineering department does not talk to after-sales service, and technical documentation also works in isolation to the greatest possible extent. As a result, service technicians, service hotline staff and customers themselves need to know which systems contain which information and how they can access it.

However, it can be so much easier: with a service information system that brings together mechanical, electronic, and pneumatic service information. In addition, the system intelligently integrates, links and networks other content, such as product information, technical documentation, supplier documentation, photos, or media assets. The networking and linking of the information can be done based on AI technologies with the help of semantics and knowledge graphs, which build a relationship network of the information.