In the after-sales department of a machine manufacturer, the phone rings and a customer on the other end says: "The strip infeed is jamming when starting up the machine." No part number, no technical term, just a symptom. The first-level employee must formulate the correct question from this: Is it about the roller contact pressure, the strip thickness, or the torque referencing? This translation is the actual work, and it is the point at which industrial service comes to a standstill every day.

In discussions with machine manufacturers, we see this pattern time and again. First-level support is seen internally as a cost center, yet the customer relationship in the most lucrative part of the business depends on it. According to an analysis by BCG, after-sales services in the industrial sector achieve gross margins of 30 to 50 percent, while the sale of new machines lies between 15 and 25 percent. Service is where the money is made and where the next order is prepared. And that is exactly where the customer is left waiting in the queue.

In after-sales, the speed with which someone finds the right spare part decides the next order.

Three points of friction slowing down first-level support

Anyone observing industrial service sees the same three points of friction, regardless of the industry or product.

The first is the expert gap. Customers describe their problem in everyday language, while the company's knowledge is stored in technical terms. "The stainless part" means a specific material grade, "the small valve" is a solenoid valve with a part number. A gap yawns between what the customer says and what is in the product catalog and ERP, which is currently bridged by an experienced person asking follow-up questions.

The second point of friction is the knowledge leaving the company. Experience in mechanical engineering resides in just a few minds, and those minds are retiring. The DIHK Skilled Labour Report 2025/26 cites the shortage of skilled workers as the greatest business risk for more than half of all companies. Where knowledge is only passed on verbally, a Chinese whispers effect occurs. And with every departure, the information becomes thinner.

The third point of friction is the media break. The answer to a customer's question is rarely in one place. It is distributed across a datasheet as a PDF, a spare parts list, the inventory in the ERP, and a note in the CRM. An analysis by Atlassian estimates that around a quarter of working time is spent searching for information. In service, this is the time the customer spends waiting.

What changes when the first level is automated

An AI chatbot shifts the translation work from humans to the system without sacrificing technical expertise. This succeeds when three things come together.

• The bot anchors its answers to verified sources. The information comes from a checked datasheet, the ERP, or a test report, but not from the free-form language model. This ensures it remains reliable in a technical environment where an incorrect statement is costly. How this source anchoring works is explained in the article on hallucinations in AI chatbots.

• The bot translates between symptom and technical term. Behind "the strip infeed is jamming," it recognizes the technical attributes that narrow down the selection and guides the customer to the right part with targeted follow-up questions. This guided consultation combines dialogue with the structure of the catalog, as described in the article on the digital product advisor.

• The bot accesses real transaction data. Only the integration into ERP and CRM turns a general answer into a concrete one: availability, price, delivery time, compatibility for the installed machine. How a chatbot connects to SAP, Salesforce, and HubSpot is shown in the article on CRM and ERP integration.

What is left over goes to the technical experts. Complex cases, complaints, or decisions requiring discretion land with the service technician via an organized hand-off, complete with chat history. The Agent Desk keeps the context at a glance and enables the next steps.

An example from fastening technology

The case of Böllhoff shows what this looks like in practice in the industrial sector. The fastening technology specialist has been relying on a chatbot from Mercury.ai since 2019 to manage a complex B2B portfolio and the usual media breaks in service. Customer inquiries run seamlessly without media breaks, and qualified leads are generated daily from the dialogue, landing with sales via the Agent Desk. Johanna Neumann, Head of Marketing Europe, is guiding the project, the next step of which is multilingualism for the international customer base. The detailed case study is available under Böllhoff.

The first level carries the customer relationship

Industrial after-sales decides on repeat purchases and recommendations, and it carries the best margin in the company. Automating it means handing repetition over to a system that understands symptom language, anchors answers to verified knowledge, and knows the real data. The experienced human remains where they are needed: on tricky cases and with customers who need a decision.

For machine manufacturers with a broad portfolio and distributed locations, this is the way to bring availability and technical expertise together. A foundation for this is provided by the guide to automating customer service with AI.

Would you like to relieve your after-sales team? Speak with us or view the customer service solution.

About the author: Mirco Schmidt is Chief Revenue Officer at Mercury.ai. He has more than ten years of experience in international and leadership positions, including at the Volkswagen Group, Club Med, and the EQS Group, and holds a degree in Marketing Management from the FH des Mittelstands in Bielefeld. His focus areas are project management, marketing and sales, and communications.

Sources

• BCG (2025): Aftermarket Services Drive Growth and Higher Margins for Industrial Manufacturers. After-sales gross margins of 30 to 50 percent compared to 15 to 25 percent in the new machinery business.

• DIHK-Fachkräftereport 2025/26: More than half of companies see the shortage of skilled workers as the greatest business risk.

• Atlassian (2025): State of Teams. Around a quarter of working time is spent searching for information.