Benefits and Added Value of AI in Mechanical and Plant Engineering

According to the VDMA, two out of three AI projects fail. The causes often lie in an unclear project approach, a lack of resources, and the mistaken expectation that AI works like a traditional IT project with fixed start and end dates. AI, however, requires continuous maintenance, training, and fine-tuning. Companies must be prepared to commit fully to the process. Half-measures rarely lead to success.

AI can only deliver meaningful results if it is fed high-quality, realistic, and up-to-date data. In industrial settings in particular, it is crucial that AI works with real-world condition data. This could include, for example, photos of used, dirty spare parts rather than idealized new parts. This data is often not freely available and must be extracted from internal systems such as the ticket system or CAD archive.

Companies should explore specific use cases early on and assess whether they have the necessary data infrastructure in place. AI can only realize its full potential if there is a genuine need for it and the organization is willing to commit to the project. Employees must be involved from the very beginning—not as potential “losers,” but as co-creators of new processes.

3.1 Planning Runs in the ERP System

One particularly effective area of application is the processing of planning runs. Up to 90% of the tasks are repetitive and can be automated using AI. This saves time, reduces errors, and improves the quality of planning. Employees can focus on complex decisions while AI handles routine tasks.

3.2 Spare Parts Identification in Service

AI can assist service technicians and customers in identifying spare parts. Even heavily worn or dirty parts are reliably detected. This reduces complaints, saves on return shipping costs, and increases customer satisfaction.

3.3 Automatic Tender Detection

An example: Using AI, a German company was able to identify and win a tender in Romania. An opportunity that would previously have gone unnoticed due to language and geographic barriers. The AI continuously scanned multiple platforms and identified relevant tenders.

3.4 Cost Analysis in Procurement

AI can analyze the manufacturing costs of assemblies, including material, processing, and labor costs. This provides a sound basis for decision-making regarding international procurement. Companies can compare prices, weigh risks, and make strategic purchasing decisions.

3.5 Quality Assurance

In manufacturing, AI can be used for automated quality control. It identifies typical defects and enables 100% inspection without the need to manually inspect every part. This saves time and increases production reliability.

AI projects differ fundamentally from traditional IT implementations. They are not a one-off deployment with a clearly defined go-live date, but an ongoing development process. Initial results often fall short of expectations, models may not yet deliver optimal quality, or they may encounter organizational constraints. This is precisely the point at which many projects fail.

Successful organizations accept that AI works iteratively: models need to be trained, reviewed, refined, and integrated into real-world processes. At the same time, conditions such as data quality, process logic, and user behavior are constantly evolving. AI systems must be able to adapt to these changes.

Those who judge AI too quickly or abandon a project after an initial setback forgo long-term potential. Sustainable success emerges where companies treat AI as a strategic capability and commit time, attention, and resources to it. Only then can real value gradually unfold.

For companies with limited resources, starting out with specialized service providers can be a practical approach. Tools such as the COSMO AI Pathfinder (https://www.cosmoconsult.com/de/loesungen/ki-strategie/cosmo-ai-pathfinder) help identify suitable use cases and enable a resource‑efficient project launch.

AI is not a panacea. Caution is particularly warranted when dealing with unstructured processes or complex contractual issues. In such cases, AI should be used only as a support tool. The less structured data is available, the greater the risk of inaccurate results.

AI offers enormous potential in the mechanical and plant engineering sector: from increased efficiency and cost reductions to new business opportunities. However, a strategic, data-driven, and sustained project approach is crucial. By involving employees early on, defining specific use cases, and selecting the right partners, companies can implement AI successfully and sustainably.