AI Integration New Paths in Technical Service—How AI Can Support Mechanical And Plant Engineering

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The mechanical and plant engineering sector is facing major challenges such as declining orders and a skills shortage. Learn how AI provides solutions and secures competitive advantages.

The machinery and plant engineering sector has been under pressure for some time: declining order intake, high energy costs, and geopolitical uncertainties are weighing on the industry. Additionally, many companies are struggling with rising operating costs and a shortage of skilled workers. Furthermore, products from other countries are improving. The solution: companies must optimize their service processes to better meet customer requirements and differentiate themselves from the competition. Technical service thus becomes a crucial success factor. Those who design it sustainably strengthen their competitiveness and retain customers in the long term.

New Possibilities Through AI-Powered Service

By designing processes more efficiently, precisely, and cost-effectively, artificial intelligence currently offers the greatest potential to elevate technical service to a new level. Certain application areas are particularly in focus—foremost among them is predictive maintenance. Predictive maintenance minimizes unplanned downtimes and optimizes maintenance intervals. This increases machine availability and significantly reduces operating costs.

Advantages of AI-Powered Service Optimization

Additionally, AI supports fault diagnosis by using automated analyses and pattern recognition to identify problems early—often before they occur. This improves service quality and reduces the effort required for manual troubleshooting. Furthermore, AI-driven image recognition systems on AR glasses or tablets enable even more efficient maintenance. They detect defective components and provide the technician with step-by-step repair instructions, including suitable spare part suggestions.

Another area of application is the optimization of resource management. AI-driven systems help to store spare parts as needed, deploy service technicians more effectively, and seamlessly coordinate processes. All this ensures high-quality service despite the shortage of skilled workers, strengthens customer loyalty, and increases competitiveness. Additionally, AI opens up new business models such as pay-per-use, where machines are billed based on usage—a approach that makes the service more flexible and customer-friendly.

Not least, AI improves the personalization of service offerings: by analyzing large data sets, customer needs can be understood more precisely and tailored solutions can be developed that strengthen customer loyalty in the long term. In addition, AI accelerates product development by optimizing design and innovation processes in service.

While some companies are already exploiting these advantages, others still face challenges such as a lack of data strategy, integration hurdles, or insufficient expertise. To realize the full potential of AI in technical service, targeted investments in data management, training, and strategic implementation are essential.

Limits of AI in Technical Service

Despite all the advances, AI also reaches its limits in technical service. Complex problems that require creativity, deep expertise, and innovative problem-solving cannot be automated. Especially in safety-critical decisions, humans remain indispensable. In short, AI is changing the working world, but it will not replace service technicians in the foreseeable future. Instead, it supports repetitive tasks and relieves skilled workers.

In addition, it can enable less qualified employees to reach a higher level of competence. At the same time, the use of AI creates new professions in technical service that require specialized know-how—knowledge that still needs to be developed in many places. In conclusion, AI optimizes many processes overall but cannot fully replace expertise and human judgment.

Challenges in Data Quality, Integration, And Acceptance

Despite its potential, there are hurdles in using AI in machine and plant engineering. A central issue is data quality. Many companies have unstructured or incompletely digitized machine data, which hampers the use of intelligent algorithms. Furthermore, AI must be compatible with existing systems. If the appropriate interfaces are lacking, this poses a challenge for technological integration.

Economic factors also play a role: many companies face the question of whether the required investments in software and infrastructure will pay off in the long term. At the same time, there is a shortage of skilled workers with AI expertise, which slows down implementation. Last but not least, there are often acceptance issues among the workforce due to a lack of knowledge. Some service technicians fear being replaced by automation. Clear AI strategies, the right use cases, and active communication are needed to alleviate fears and successfully integrate AI.

The following practical examples from the circle of KVD member companies show how organizations can successfully establish AI in technical service.

Practical Insight I: AI Optimizes Spare Parts Pricing A company in the packaging industry relies on artificial intelligence for spare parts pricing. The system continuously analyzes market prices, competitive data, and delivery times from various sources to determine optimal spare parts prices. This enables the company to offer fair and competitive prices without jeopardizing its own margins. At the same time, customers benefit from greater transparency. The result: higher customer satisfaction and loyalty, as well as increased revenues in the spare parts business.

Practical Insight II: Augmented Reality Optimizes Maintenance and Fault Diagnosis A leading manufacturer of labeling and packaging technologies uses AI-powered augmented reality to improve maintenance and fault diagnosis. Service technicians scan machine components using a camera and receive an instant visual fault analysis. Through the system, they can also communicate with experts in real-time and access repair instructions. Many issues can thus be resolved remotely. This saves time, reduces travel costs, and accelerates maintenance processes—to the benefit of the customers.

Practical Insight III: AI-Supported Training Enhances Customer Service Many companies also benefit from AI in training service employees. For instance, an AI-powered training platform analyzes speech patterns, facial expressions, and body language and provides individual feedback based on this. Employees receive targeted recommendations to improve their communication skills—a clear advantage for service technicians who need to clearly convey complex technical issues to customers.

Stepwise Integration As the Key to Successful AI Deployment

The practical examples show: AI is worthwhile when integrated purposefully. Essential for added value is a gradual introduction with a clear strategy, technological compatibility, and a high-quality data base. Companies should adjust existing processes to AI-driven operations early and realistically assess the resources and expertise required. A key success factor is employee acceptance. Involving them from the start minimizes resistance and eases implementation. Pilot projects help gather initial experiences and guide investments strategically. Additionally, companies should optimize their data management to supply AI systems with high-quality and structured information. Collaborations with technology partners can offset the lack of know-how and reduce implementation effort.

AI in Mechanical And Plant Engineering: the Future of Technical Service

AI in technical service is no longer optional but necessary to remain competitive. By using AI-driven analysis, companies can better understand customer needs, offer tailored solutions, and unlock new revenue streams. Additionally, AI helps alleviate the shortage of skilled workers in technical service. Automation of repetitive tasks, remote maintenance technologies, and efficient training relieve service staff and increase productivity. Success depends on well-thought-out integration, high-quality data, and workforce acceptance. Companies that address these challenges strategically will benefit from the advantages of intelligent technologies in the long term.

*Carsten Neugrodda is the Managing Director of the Service Association KVD.

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