Artificial intelligence is entering design faster than expected just a few years ago. It is no longer just about generating text, searching for information, or simple automation.
In the CATIA environment, AI is moving directly into the product development process.
It can work with design context, assist in modifying designs, utilize corporate know-how, or automate some activities that designers currently perform manually.
However, this does not mean it should replace the designer. Rather, it changes the division of labor between humans and technology.
From AI assistant to AI collaborator
Most people today envision a typical AI assistant as a tool to which they pose a question and receive an answer.
Industrial AI goes further. Dassault Systèmes in CATIA works with the concept of Virtual Companions, virtual collaborators who understand the designer’s intent, work with technical context, and assist in specific tasks during development.
It is not just a question of:“How do I solve this problem?”
AI can also help ensure that specific tasks are actually carried out.
According to Dassault Systèmes, it is based on industrial knowledge, technical principles, and so-called Industry World Models. The goal is for AI not to operate in isolation from the reality of the product but to understand the environment in which the designer works.
LEO and AURA: two different types of AI collaborators
CATIA introduces two specialized Virtual Companions.
LEO operates as an expert focused on engineering and design. It helps understand existing designs, navigate complex product structures, work with technical knowledge, and accelerate design changes.
AURA focuses primarily on company knowledge. Its task is to help teams search for, reuse, and apply existing know-how across projects.
This second area can be particularly interesting for companies.
What happens to the know-how of an experienced designer?
Manufacturing companies possess vast amounts of knowledge that are not stored solely in drawings or CAD models.
They reside in people’s minds.
An experienced designer knows why a certain detail changed five years ago. They know which solutions have not worked in the past. They are familiar with design rules, internal standards, and procedures that have developed over the years within the company.
However, this knowledge is difficult to transfer.
Industrial AI opens up the possibility of working with it more systematically – capturing it, making it accessible to other teams, and reusing it in new projects.
AI does not have to start from scratch every time. It can work with what the company has already solved.
Generative Experiences: AI begins to create directly
The next step is Generative Experiences.
Here, it is no longer just about searching for information or making recommendations.
According to Dassault Systèmes, AI can, for example, create editable mechanical parts from multimodal inputs, predict mechanical interfaces, create assemblies, generate design rules, or convert technical documentation into structured system models.
The result is not intended as an isolated “AI design.” Generative functions are part of the engineering workflow itself and must respect corporate standards and quality requirements.
Why adding AI to CAD is not enough
This is where the difference lies between general generative AI and Industrial AI. A designer does not just need a tool that can create some geometry. They need a solution that understands what this geometry means in the context of the entire product.
How does it relate to other components?
What requirements must it meet?
What happens if we change it?
How will the change affect other disciplines?
And can the new solution be verified before the prototype is manufactured?
That is why Dassault Systèmes connects Industrial AI with the Virtual Twin environment and the 3DEXPERIENCE platform. Modeling, systems engineering, simulation, and product data create the context in which AI can evaluate the situation and support decision-making.
The greatest benefit of AI may not be in generation
At first glance, the most attractive features are when AI creates something on its own. However, from the perspective of an industrial company, something else may hold even greater value: the ability to repeatedly utilize technical knowledge and automate processes that the company already knows.
Designers still deal with many routine tasks. They search for existing solutions, check structures, repeat similar design procedures, or find out why a colleague designed a certain part of the product in a specific way several years ago.
If AI takes over some of this work, an experienced person can spend more time where their know-how has the greatest value – in technical decision-making, solving new problems, and innovations.
Does this mean fewer designers?
Not in the way that Industrial AI is described by Dassault Systèmes. The principle is not to remove humans from development but to enhance their capabilities.
Critical technical decisions remain under the control of experts. AI helps automate activities, utilize existing knowledge, and prepare decision-making materials more quickly.
Therefore, the result may not be “AI instead of the designer.”
A much more accurate description is a designer who has another digital expert alongside them.
What does this mean for manufacturing companies
Industrial AI will not make sense for every company and every process. Its greatest potential lies where similar design activities are repeated, where a large number of variants arise, where the product is technically complex, or where the company needs to better utilize its long-term built know-how.
And this raises an important question for companies currently addressing AI: Do we even have the data and technical know-how prepared for AI to work with?
Without structured design data, managed knowledge, and product context, even a very capable AI will not know what the right solution is for a specific company.
Therefore, the future of engineering will likely not only be about more powerful AI. It will be about connecting human experiences, industrial know-how, quality data, virtual environments, and artificial intelligence.
Source
This article is based on material from Dassault Systèmes Industrial AI in CATIA Transforms Engineering.
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