23. Februar 2026

AI in production: opportunities, areas of application and challenges

AI in industry: How artificial intelligence is changing production and how companies can benefit

Industrial production is going through structural change. Global supply chains, increasing cost pressure, shortage of skilled work and volatile markets are forcing companies to rethink their production systems.
Today, AI is taking industrial production to a new level: systems learn from data, recognize patterns and make predictions independently. This creates a new quality of control and optimization.

But what does that mean in practice?

Artificial intelligence in production makes it possible not only to operate machines, but also to understand them, to analyze data and to identify optimization potential. This gives companies transparency that would not be achievable with traditional methods.
The central question is therefore no longer whether AI is relevant in production, but how quickly companies strategically integrate it.

AI in production

What does artificial intelligence actually mean?

Before we talk about AI applications in production, it's worth taking a look at the basics.
The term “artificial intelligence” is frequently used but rarely explained precisely.

Definition of artificial intelligence

Artificial intelligence refers to systems that are able to learn from data, recognize patterns and derive recommendations for action. Algorithms form the basis for this.
In the context of AI in industrial production, this means that machine and process data is continuously evaluated in order to identify optimizations or deviations at an early stage.
For example, an algorithm can identify deviations in vibration behavior.

Machine Learning (ML): Learning from data instead of fixed rules

Machine learning is a sub-term of artificial intelligence. Instead of predefined threshold values, the system itself learns what is “normal” and automatically detects deviations.
In industrial practice, this means:
Machines are developing a digital “behavior profile”
Production processes are modeled based on data
Unusual patterns are automatically recognized

Deep learning: neural networks for complex patterns

Deep learning is an evolution of machine learning and uses multi-layered neural networks. They can recognize highly complex patterns — for example in image data or large sensor data sets.
Typical areas of application of AI in industrial production with deep learning:
Visual quality check
Defect classification
Pattern recognition in high-frequency sensor data
Optimizing complex production processes
Deep learning plays an important role in automated quality control, especially in artificial intelligence production.

Natural language processing (NLP): When machines understand language

NLP enables machines to process and interpret speech. In production, this can be used for:
Automatic analysis of maintenance logs
Digital assistance systems
knowledge databases
Chat-based support systems
Even though NLP is less visible than condition monitoring or predictive maintenance, it is an important part of modern AI applications in production.

Applications of AI in industrial production

AI in industrial production is a key tool for companies that want to make their production more efficient and future-proof. Companies use artificial intelligence in production to monitor, analyze and optimize processes based on data.
The special thing about AI in production is that it not only improves individual machines, but also connects the entire production system together. Production managers get a real-time overview of their systems. Artificial intelligence thus enables production to increase efficiency and serves as a well-founded basis for decision-making.

Automated modelling and data-driven decision making

A central field of application of AI in industrial production is automated modelling. Production processes are digitally mapped and continuously analyzed, allowing companies to optimize their processes. Artificial intelligence in production thus creates a basis for decision-making based on real-time data. Instead of relying on assumptions or experience, production managers can thus derive concrete measures. Companies benefit from improved energy efficiency, reduced material costs and higher overall plant utilization. AI in production is thus transforming classic production planning into a data-driven decision-making process.

Anomaly Detection and Predictive Maintenance

Predictive maintenance is one of the most relevant fields of application of AI in production. The analysis of historical and current machine data makes it possible to identify imminent failures at an early stage. AI in industrial production thus ensures that unplanned downtimes are avoided and that plants are optimally utilized. Artificial intelligence continuously learns from machine data streams and is able to recognize complex patterns that remain hidden from human eyes. Companies that implement AI applications in production increase productivity, reduce maintenance costs and secure a competitive advantage in the long term.

What are the benefits of AI for companies?

The concrete benefits of AI in industrial production can be seen on several levels. Companies not only increase efficiency and reduce costs, but also improve the basis for decision-making, reduce downtimes and secure competitive advantages.

Increase efficiency and reduce costs
in production

AI in production enables a systematic
Optimization of processes and use of resources.
This allows companies to significantly reduce energy consumption, use of materials, downtime costs and maintenance costs.

Reduce unplanned downtime

AI in production detects potential failures at an early stage. As a result, plant availability increases, unplanned production interruptions are minimized and expensive downtimes and repairs are avoided.

Better decisions through transparent data models

AI in industrial production provides production managers with clear analyses. This enables data-based decisions that improve processes, reduce risks and provide sound support for strategic measures.

Competitive advantages through intelligent production

AI in production increases quality, flexibility and reaction speed in production processes. AI applications in production are thus becoming a differentiating feature and ensure competitiveness in the long term.

Is artificial intelligence only for large companies?

Contrary to current assumptions, AI in industrial production is not exclusively reserved for large companies. Small and medium-sized companies can also benefit from the advantages of artificial intelligence in production.

AI applications for small and medium-sized enterprises (SMEs)

Modern AI solutions can be implemented step by step. Retrofitsolutions make it possible, for example, to make existing machines simple retrofit. AI applications in production can thus be specifically adapted to the requirements of SMEs.

Scalability: From pilot project to company-wide solution

A key advantage of AI in industrial production is its scalability. Successful pilot projects can easily be extended to other production lines and locations. Artificial intelligence in production grows with the company's requirements and can be integrated step by step into the entire production process.
That's how it works

Checking the data basis:

Our potential analysis automatically checks whether your historical machine data is suitable and complete.

Quick installation:

We receive your data securely via digital interfaces and support you with sensor upgrades if required.

Real-Time Data Visualization & Analysis:

Your data is visualized and analyzed in real time on our dashboard.

Predictive maintenance:

If there are anomalies, you will automatically receive notifications to initiate proactive maintenance measures.

Continuous model optimization:

Our algorithms are constantly evolving by learning from the collected data & your feedback.

Successful customer examples from industry

Large machine plant in the energy sector
Gas storage & compressor
Monitoring of the entire system, in particular the fast-rotating, sensitive turbines.
Illustration of a complex coating system
Inline coating machine
Monitoring of the pumps required for the water cycle in the coating plant.
Section of a machine for processing animal feed
Milling machines for animal feed
Monitoring of storage temperatures and drive performance for reliable & efficient production.
Conveyor belts in port operations
Conveyor belts in
Port Authority

Continuous monitoring of motors, gearboxes and bearing blocks to prevent bearing damage and belt ruptures at an early stage.
Manufacture of bicycles
Gearmotors in bicycle production
Condition monitoring of drives and gears to avoid downtimes in conveyor technology and production interruptions.
Crunchy plant in food production
Crunchy plant in food production
Monitoring of agitators, mixers and conveyors to ensure product quality and continuous production

Challenges of implementing AI in production

The introduction of AI applications in production is complex and requires more than just technology.  Organizational and technical requirements must be met.

Organizational requirements

For AI to be successfully implemented in industrial production, companies must create acceptance. AI applications in production require a rethink, not only technically but also culturally. Only when employees understand how artificial intelligence supports their work can the full potential be exploited.

Technical requirements

High-quality data, reliable sensors, powerful IT systems and interfaces are the basis for AI in production. Without a clean data base, AI applications cannot be effective in production.
Of course, we support you with the implementation.

AI in production: Vision of the future or already in practice?

AI in industrial production is no longer a vision of the future. Predictive maintenance is a reality. Companies that have implemented artificial intelligence in their production benefit from cost reductions, higher plant availability and improved predictability. AI applications in production are therefore a strategic tool of the present day.

Conclusion

AI in production is fundamentally changing industrial value creation. Artificial intelligence ensures data-based decisions, less downtime and optimized processes. AI applications in production are scalable, economically feasible and suitable for both large companies and SMEs. AI in industrial production is not a temporary trend, but the basis for competitive production systems nowadays.
Companies that rely on AI in production are securing long-term efficiency and quality advantages.
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