In the after-sales department of a mechanical engineering company, the phone rings, and a customer on the other end says: "The strip feed jams when the machine ramps up." No part number, no technical term, just a symptom. The first-level support employee must formulate the right question from this: Is it about roller contact pressure, strip thickness, or torque referencing? This translation is the real work, and it is the point where industrial service stalls every single day.

In conversations with mechanical engineering companies, we see this pattern time and again. First-level support is viewed internally as a cost center, even though 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, compared to 15 to 25 percent for the sale of new machinery. Service is where money is made and the next order is initiated. And that is precisely where the customer is left waiting on hold.

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

Three friction points slowing down first-level support

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

The first is the expert gap. Customers describe their issue in everyday language, while the company's knowledge is codified in technical terminology. "The stainless steel part" refers to a specific material grade; "the small valve" is a solenoid valve with a part number. A gap opens up between what the customer says and what is listed in the product catalog and ERP—a gap currently bridged by an experienced human asking follow-up questions.

The second friction point is knowledge leaving the company. Experience in mechanical engineering resides in just a few minds, and those individuals are retiring. The DIHK Skills Shortage Report 2025/26 cites the shortage of skilled workers as the greatest business risk for more than half of companies. Where knowledge is only passed on verbally, a game of telephone emerges. And with every departure, the available information becomes thinner.

The third friction point is media disruption. The answer to a customer inquiry is rarely in one single place. It is scattered across a PDF data sheet, a spare parts list, inventory levels in the ERP, and a note in the CRM. An analysis by Atlassian estimates that roughly a quarter of working time is spent searching for information. In service, that represents the time the customer spends waiting.

What changes when first-level support is automated

An AI chatbot shifts the translation work from the human to the system without sacrificing technical depth. This succeeds when three elements come together.

• The bot anchors its answers to verified sources. Information comes from an approved data sheet, the ERP, or a test report, but not from the unconstrained language model. This keeps it reliable in a technical environment where incorrect information proves costly. How this source grounding works is detailed in the article on hallucinations in AI chatbots.

• The bot translates between symptom and technical term. Behind "the strip feed jams," it identifies the technical attributes that narrow down the selection and guides the customer to the appropriate part using targeted follow-up questions. This guided consultation bridges the dialogue with the structure of the catalog, as described in the article on digital product advisors.

• The bot accesses actual transactional data. Only integration with ERP and CRM turns general information into concrete details: availability, price, delivery time, compatibility with the installed machine. How a chatbot connects to SAP, Salesforce, and HubSpot is demonstrated in the article on CRM and ERP integration.

What remains goes to the domain experts. Complex cases, complaints, and discretionary decisions are routed to service technicians via an organized handoff, complete with the conversation history. The Agent Desk keeps the context visible at a glance and enables the next steps.

An example from fastening technology

What this looks like in practice is illustrated by Böllhoff. Since 2019, the fastening technology specialist has relied on a chatbot from Mercury.ai to manage a complex B2B portfolio and overcome typical media disruptions in service. Customer inquiries are handled seamlessly, and the dialogue generates qualified leads daily that are routed directly to sales via the Agent Desk. Johanna Neumann, Head of Marketing Europe, oversees the project, whose next step is multilingual capability for an international customer base. The detailed case study is available under Böllhoff.

First-level support carries the customer relationship

Industrial after-sales determines repeat purchases and recommendations—and it carries the highest margin in the company. Automating it means delegating repetitive tasks to a system that understands symptom-based language, grounds answers in verified knowledge, and accesses real-time data. Experienced personnel remain where they are needed most: addressing complex cases and supporting customers who need critical decisions.

For machinery manufacturers with broad portfolios and distributed locations, this is the way to unite accessibility with technical expertise. A foundational framework for this is provided in the guide to automating customer service with AI.

Looking to relieve your after-sales team? Talk to us or explore our customer service solution.

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 Skills Shortage Report 2025/26: More than half of companies view the shortage of skilled workers as the greatest business risk.

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