“Artificial intelligence has now entered the everyday language of industry. But beyond the enthusiasm and expectations, the real question is different: where can it create concrete value in the design of automatic machines?”
Beyond the novelty effect
Over the past two years, artificial intelligence has become the protagonist of conferences, trade fairs, and corporate presentations.
It seems that every new technology must necessarily be defined as “AI-powered”.
In the industrial automation sector, however, change is developing with different timelines and methods compared to other fields.
Machines do not need to make creative decisions.
They need to be reliable, predictable, and repeatable.
For this reason, artificial intelligence is not replacing traditional motion control. It is complementing it, introducing new analysis tools and decision support.
AI does not move axes. It analyzes what happens.
When discussing motion control, it is easy to imagine algorithms directly commanding movement.
In industrial reality, the first impact of artificial intelligence is much less spectacular, but probably much more useful.
Each axis generates an enormous amount of information: cycles executed, movement times, accelerations, current draw, vibrations, temperatures, deviations from nominal parameters.
For years, this data has been used only minimally.
Today it can become a valuable source for understanding the real behavior of the machine.
Artificial intelligence does not replace motion control, but helps interpret what the system is already communicating.
Data without context does not mean knowledge
One of the greatest risks is thinking that collecting more data automatically equals obtaining more information.
This is not the case.
A modern machine can produce thousands of data points every second.
Real value emerges when this data is connected to the production process and transformed into useful indications for those who design, manage, or maintain the system.
Artificial intelligence is effective precisely because it can identify correlations that are difficult to recognize through traditional analysis.
From maintenance to continuous optimization
One of the areas where AI is finding the most concrete applications is predictive maintenance.
Recognizing an anomalous variation in cycle times, motor current draw, or axis behavior can enable scheduling an intervention before machine downtime occurs.
But the potential does not end here.
The same information can be used to optimize motion profiles, reduce energy consumption, improve synchronization between axes, and maintain consistent performance even after millions of cycles.
This is not just about avoiding failures.
It is about continuously improving machine behavior.
The designer remains at the center
There is a misconception that often accompanies the debate on artificial intelligence.
The idea that the algorithm can replace experience.
In the world of industrial automation, exactly the opposite occurs.
Artificial intelligence provides increasingly sophisticated analysis tools, but continues to need the expertise of those who know the process, understand application constraints, and can interpret results.
An algorithm can identify an anomaly.
Determining whether that variation represents a real problem or a normal consequence of the production cycle remains the designer’s responsibility.
The market is changing its approach
The automation market is also experiencing a maturation phase.
After an initial period characterized by announcements and very high expectations, attention is shifting toward concrete applications.
Companies are looking for tools that reduce downtime, improve production efficiency, and simplify system management.
In this context, artificial intelligence stops being an element to showcase and becomes a technology to integrate discreetly, where it can genuinely generate value.
This is probably the most interesting phase of its evolution.
Looking ahead without forgetting the fundamentals
Every innovation brings enthusiasm.
But industrial automation has always followed a precise rule: technologies establish themselves only when they demonstrate solving a real problem.
Artificial intelligence will be no exception.
Good mechanical design, correct sizing, reliable control, and in-depth process knowledge will continue to be fundamental.
AI can amplify these qualities.
It cannot replace them.
Conclusions
In a few years, we will probably talk much less about artificial intelligence and much more about the results it has helped achieve.
Because the real change will not be having machines that “think”.
It will be providing designers with tools capable of better understanding what happens inside the machines they design every day.
