AI in Mechanical Engineering: This Is How Companies Approach It
In the blog post "AI in Mechanical Engineering: Why Companies Have Nothing to Fear", we looked at the benefits that artificial intelligence brings to mechanical engineering and the role that humans play. In this article, you can find out which questions companies in the mechanical engineering sector should ask themselves if they want to use AI and how they approach it.
3 questions to ask before you start using AI in your company
Companies that want to use AI in mechanical engineering in the areas of aftersales & service and technical documentation should first define their expectations. Mechanical engineering companies should be clear:
- What goal they want to achieve with the use of AI
- Who should work with it
- How the AI should be trained
#1 What goal do you want to achieve with the use of AI?
When setting goals, it must be clear that AI is not a solution that provides perfect answers to all questions. Rather, it is about probabilities, relevance, and getting support for complex questions that cannot be answered with 0 and 1.
Machine builders should therefore keep an open mind when setting goals and also keep alternatives to the use of AI in mind: for example, the optimization of user guidance or a particularly intelligent process design. With learning algorithms, it is also important to ask whether the AI should be trained for a specific process or whether a self-learning system is the better solution.
#2 Who should work with AI?
The target group plays a central role. An AI photo recognition for service technicians is worthless if the AI is trained on exposed components and does not correctly recognize installed or dirty parts in practical use.
In technical writing, on the other hand, it is important that content creation should still not take place without humans. Security-relevant sections in particular should not be created by AI without a human-in-the-loop. Appropriate processes must be created for a suitable way of working with AI.
#3 How much data and time do you need for AI adoption?
There is no universal answer to the question of how much time and data are necessary to implement AI applications in the company. Both depend on the intended use. For example, to develop autonomous learning AI and neural networks like ChatGPT, machine learning is necessary: Using a large amount of data, the AI learns to recognize patterns, make decisions on its own, and make better and better predictions over time.
It is usually impossible to obtain a critical mass of data, particularly in the case of special processes in mechanical and plant engineering. But even without machine learning, artificial intelligence can support companies in mechanical engineering. For optimal results, the quality of the data is more important than the quantity.
Content created with ST4 is ideal for this: The structured content in ST4 usually contains much more information (e.g. product mapping) than just plain text. This means, for example, that a large language model (LLM) could be "trained" on content from technical writing to better understand its own machines, which would enable a number of new business cases (e.g., chatbot documentation).
Quality is also the factor that determines the timeframe of an AI implementation. Many technologies for standardized processes deliver passable results after a short time. But the higher the quality requirements, the longer the implementation takes. Companies should therefore be more prepared for long-term processes with many iterations.
General rule is: involve users at an early stage
In the end, it's all about the users and their needs. Whether special AI tools are required or standard solutions are sufficient can — depending on the application — only be answered by the service technician or technical writer. To ensure that the solution can actually support him in his work and save him and the company time, the user should be involved at an early stage.
Conclusion on AI in mechanical engineering
AI essentially fulfills two tasks in mechanical engineering: On the one hand, it can make systems more intelligent, and on the other, it can support users in their work. Studies show that although AI is becoming increasingly relevant in mechanical engineering, many companies are still searching for the right approach to the technology.
The important thing for companies is not to be deterred, but to get to grips with AI at an early stage. As with all trends, it makes sense to have a healthy expectation and ideally to gain experience by testing 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, not replace them.
Here you will find handy tips for practical implementation in mechanical engineering.
