Employing AI in Mechanical Engineering: How Companies Are Making It Work

AI has long been an inherent part of our personal and professional lives: facial recognition on smartphones, driver assistance systems, and chatbots are just a few examples of AI applications that have become a regular feature of our everyday existence.  

AI is also leaving its mark on industry – in areas such as predictive maintenance, quality assurance, and wherever data analysis plays a role and intelligent assistance systems relieve people’s workloads.  

The launch of ChatGPT at the end of 2022 raised the profile of the topic of artificial intelligence. Industrial companies have been exploring the potential of AI for years; a representative survey conducted by the digital association Bitkom in 2025 revealed that 82% of industrial companies surveyed held the opinion that AI is crucial for competitiveness. 42% of the study participants stated that they were already using AI, while a further third were planning to do so. The use of AI is particularly widespread in analytics, but it is also used in robotics, machine configuration, and design.

Put simply, AI is the ability of computers to learn, recognize patterns, and make decisions based on them. Artificial intelligence uses technologies such as machine learning, language processing, and computer vision (i.e., the analysis and interpretation of images).

AI tools are not out-of-the-box solutions that solve all problems and fully satisfy the requirements of mechanical engineers. A better way to look at AI is as a toolbox which companies can use to develop individual solutions that provide individuals with the best possible support in their day-to-day work.  

AI comes in many different forms, and the processes it powers can: 

• Run in the background and perform tasks such as processing and cleaning data or
• Interact directly with the user – as is the case, for example, with voice assistants, chatbots, and autonomous systems such as robotics applications. 

Artificial intelligence provides support to industrial companies in many areas – be it in development, production, or service. Practical examples include the use of robots in production, camera-aided quality assurance, and data-based optimization of production planning. Specific AI use cases in mechanical engineering can also be found in aftersales & service as well as technical documentation. Artificial intelligence can support various tasks typical to this area, including: 

• Spare parts identification 
• Predictive maintenance 
• Quality assurance and machine translation of technical documentation 
• Self-service portal for customers
• Chatbots in aftersales support 
• Networking information for service users 

Take a look at the following examples to discover other types of added value that AI can offer aftersales & service and technical writing departments.

Data need to be well prepared and linked up in an intelligent way in order for AI to generate added value for both users and customers. Service organizations produce a wealth of valuable data over time. However, historical data are often only available in the form of scanned PDFs. Digitalizing information can be easy with the help of AI: The tools can learn to selectively extract certain information and identify correlations and elements such as tables, paragraphs, and forms. AI tools therefore offer efficiency advantages over optical character recognition (OCR). At Quanos, for example, such tools are used to simplify manual processes and save users time – one example is the intelligent preparation and provision of data from PDF files in digital form.  

One of the strengths of AI is its ability to classify content at lightning speed. In technical documentation, metadata can be assigned with the help of AI. Artificial intelligence is capable of evaluating large volumes of data in a short space of time.

Technical writers gain an efficiency edge when they use AI-based functions in their writing – for example, when rewording texts that have already been written.

When data are better prepared and enhanced with metadata, this also improves the results obtained by users when working with AI-supported tools. In aftersales & service, information about machines, spare parts, and processes counts among the most important tools available. 

Artificial intelligence helps service technicians to find the data they need to process customer orders, even when faced with a glut of information. AI is capable of delivering the right results even when dealing with typos and can make intelligent, context-relevant suggestions that get technicians to the information they need even faster.  

No matter whether it’s processing an English-language search query, finding spare parts using a photo, or operating with voice commands: AI can make it much easier for service users to access information and significantly increase productivity.

At Quanos, many of the possibilities afforded by AI in mechanical engineering – and especially in technical documentation and aftersales & service – are already available for practical use: 

• Content created with the component content management system ST4 XML can be automatically translated using AI Translator.
• The technical writing team can utilize AI-based functions to support authors during the writing process.
• ST4 AI Jetpack automatically assigns metadata in the content management system, which simplifies the data preparation process.  

Cloud-based Quanos InfoTwin, which links product documentation, spare parts, and service information, also has AI functionalities:

• AI Connector analyzes unstructured supplier data and inventory documentation so it can be tagged with metadata.
• AI Assistant offers a chat interface to facilitate the retrieval of information.
• Visual spare parts identification is available as a separate, partner-supported solution on the Quanos platform and offers a quick, intuitive process for identifying spare parts based on a smartphone photo.

Find out more about the use of AI in Quanos software solutions. 

Mechanical engineering companies eager to use AI in their aftersales & service and technical documentation areas should first define their expectations. Mechanical engineers should take a clear look at: 

• What goal they hope to achieve by using AI 
• Who will be working with it 
• What their planned timescale looks like and what volume of data they need

When setting a goal, always bear in mind that AI solutions do not have the perfect answer to every question. Instead, these solutions deal with probabilities, relevance, and providing support to complex questions that do not have a simple binary answer.  

Mechanical engineers should therefore keep an open mind when setting goals and keep alternatives to AI in mind. When working with learning algorithms, it’s also important to consider whether the AI should be trained for a specific process or if a self-learning system is the better solution. 

The target group plays a key role. AI photo recognition for service technicians is worthless if the AI has been trained on disassembled components and does not correctly identify installed or soiled parts in use.  

In technical writing, it is important that the content continues to be created by humans, even if AI functions are able to support the work. Appropriate processes are required to establish a suitable way of working with AI. 

There is no universal answer to these questions; the length of time and amount of data required to implement AI applications in a company are dependent on the intended use. Machine learning is required for complex AI models like ChatGPT: using a large volume of data, the AI learns to recognize patterns, generates answers, and continuously improves its performance during training. 

Generally speaking, it won’t be possible to attain a critical volume of data for special processes in mechanical and plant engineering in particular. But even without machine learning, artificial intelligence can support companies in mechanical engineering – this is where other AI processes come into play. Optimal results are far more dependent on data quality than data quantity.  

Content created with the ST4 content management system is ideal as it contains a large amount of information thanks to the assigned metadata. When used in combination with a large language model (LLM), it enables new business cases such as chatbot documentation. 

Quality is also the factor that determines the timeframe when introducing AI. Many technologies for standardized processes deliver acceptable results after a short time. However, the higher the quality requirements, the longer the implementation will take. Companies should therefore be prepared for longer-term processes with many iterations. 

Machine manufacturers need to lay the right groundwork if they want their aftersales & service operations to benefit from the positive aspects of artificial intelligence. The following actions are essential for companies taking the step into the new age of AI.

In order to successfully start using AI, machine constructors first need to check if their processes are already geared up to it or if optimizations are needed. If, for example, your spare parts management workflows are not yet sufficiently interconnected and digitalized, your initial focus should be on creating end-to-end data flows.

Maturity level models like acatech’s Industrie 4.0 Maturity Index help businesses with this. Using this model, you can assess not only your internal processes, but also your corporate culture in terms of digitalization. The results show whether you are already using the data you have available effectively. It also forms the basis for a digital roadmap with the ultimate goal (or destination) being your company becoming an agile organization.

Alongside the processes and mindset, existing IT systems and data also need to be checked in terms of their AI maturity, as an accurate database is required for AI models to make full use of their strengths. Machine manufacturers and operating companies therefore have to thoroughly analyze what data they have, where they come from, and how they can be used in conjunction with AI.

In this respect it is important that the data be collected seamlessly and correctly, and processed in compliance with GDPR requirements. Data protection and data security must have the highest priority – especially if you want to enrich your data with public information, such as market data on spare parts pricing.

A data protection officer can help you with this. Companies in the EU can also use the Compliance Checker to find out whether their planned use of AI is affected by the EU’s AI Act and whether they need to meet particular specifications.

The next step in getting you on track to introducing AI is selecting a specific use case for your company. When assessing which AI application you should consider for your needs, others factors also play a role in addition to the maturity of your processes and IT systems and the legal conditions.

For example, you need to perform a cost-benefit analysis to systematically assess both the investment costs as well as risks and opportunities of your AI project. What’s more, you should answer the following questions so that you can plan and implement the AI use case as effectively as possible:

• Does the application match your company goals?
• Which specific advantages should the application bring?
• Which needs of which stakeholders must be considered?
• Are you in a position to respond flexibly to changes and make ongoing improvements during the project?
• Should you develop the required AI expertise internally or purchase this from an external party?
• Is the AI solution that you want to use ready for the market and available, and how can you customize it to your specific requirements or retrain it?
• Do you have all the necessary resources – i.e., the knowledge, time, and budget – to successfully implement the AI project?

Once you’ve clarified all these questions and found an appropriate use case for AI, there’s nothing more standing in the way of introducing it. Change management often proves to be a useful tool for the next steps, which are a big change for many companies.

Introducing AI in a mechanical engineering company represents a profound change and requires employees to be engaged and the organization to be transformed. Effective change management will help master these tasks and will make the transition to the age of AI as smooth as possible.

An important part of the change process is transparent communication. Change management methods should be used to make the advantages of using AI clear to every individual. This will encourage acceptance and engagement among employees.

A key aspect of this is defining a vision. Change managers must also involve all those affected from the start to listen to them and find out:

• What people’s expectations are of the AI implementation
• What fears they have that need to be overcome
• What training and qualifications the stakeholders need to be able to use AI

Tools like Lewin’s three-stage model or Kotter’s eight-step model can help those responsible take a structured approach to establishing a change like introducing AI in service departments.
 

The easiest way to make sure your AI projects run smoothly is by commissioning the support of experienced partners like Quanos. We develop software solutions for aftersales & service teams in mechanical engineering companies that can have artificial intelligence added to them, allowing them to form the basis for new business models in sales and service.

The Quanos experts have the following tips for businesses that want to integrate AI into their processes:

1. Managing expectations: AI can do a lot, but certainly not everything. You should see the technology as an aid for your service team and communicate the solution you’re implementing as such.
2. Added value: Define and communicate the specific benefits of the AI application clearly and transparently. To ensure the solution offers added value to its users in practice, it needs to be continuously improved based on feedback from users.
3. Monitoring: AI is not assigned the task of independently controlling machines and equipment, but instead supporting people with operating and maintaining these assets in the best way possible. In order for people to trust the algorithm, AI decisions must be comprehensible. What’s more, monitoring by real people and ethical considerations are indispensable.

You can find these and other recommendations for your AI project in the latest KVD SERVICERADAR “KI im Service”. If you already have specific ideas about using AI in your organization and would like to speak to an expert, don’t hesitate to get in touch.

AI essentially fulfills two tasks in mechanical engineering – firstly, it can enhance the intelligence of systems and secondly, it can support users in their work. Studies show that although AI is becoming increasingly relevant in mechanical engineering, many companies are still looking for the right approach to the technology. 

It’s important that companies avoid being put off at this point and instead focus on getting to grips with AI now. As with all trends, it makes sense to have healthy expectations and gain experience by trialing the possibilities of the technology. Companies should accept artificial intelligence for what it is: a tool that is intended to support people in their day-to-day work, rather than replacing them.