For organizations developing complex aerospace and defense systems, model-based systems engineering (MBSE) can provide a way to bring requirements, architecture, interfaces, behavior and other engineering information into one connected environment. But adopting MBSE is not simply a matter of selecting a tool and starting to build models. Organizations also need a strategy for implementation, a clear understanding of what they want to achieve, and tools that engineers can work with effectively.
Benjamin W. Stirgwolt, who served as Director of Model-Based Systems Engineering at Belcan, worked with aerospace and defense customers across the engineering lifecycle. Since being introduced to MBSE in 2017, he focused on helping organizations understand how models can support engineering work and how tools such as the CATIA Magic portfolio can make that approach more accessible.
For Ben, the value of MBSE comes from having connected engineering information in one place. The next step is making that information practical to use and increasingly, using AI to help engineers get more value from it.
A connected model changes how engineers find information
One of the central ideas behind MBSE for Ben is straightforward: engineers should know where to go when they need information about a system.
Complex engineering programs generate large amounts of information about requirements, interfaces, architecture and system behavior. With MBSE, these elements can be represented within a model, with relationships connecting the different pieces.
Instead of searching across disconnected sources to answer a question, engineers can query the model.
That becomes particularly important as a system evolves. Requirements change, new functionality is introduced, and engineering teams have to rework existing designs. A connected model provides a way to understand how those changes affect the architecture and other parts of the system.
As Ben explains, the model provides a place where teams can understand not only what the system looks like, but also how its different elements relate to one another.
For organizations working on complex systems, that connected information is a key part of the MBSE value proposition.
Starting MBSE with a roadmap, not the technology
For customers that are interested in MBSE, one of the first problems Ben encounters is often not technical. Organizations may know they need MBSE, have seen demonstrations, and understand that the approach can provide value — but still have no clear idea where to begin.
His recommendation is to start with implementation planning.
Before creating models or moving toward advanced capabilities, organizations need to establish what they are trying to accomplish, define goals, determine reasonable timelines and understand the budget required for the initiative.
This matters because MBSE implementation is a progression. Ben points out that organizations can be tempted to jump directly toward advanced simulations or other capabilities that look impressive in demonstrations. But those activities may come much later in an effective implementation roadmap.
The starting point is more fundamental: define the objectives, establish the business case and determine what successful adoption should look like.
That approach also gives organizations a way to connect their MBSE investment to concrete business and engineering goals rather than treating the technology as an isolated initiative.
Why the CATIA Magic user experience matters
Belcan and many of its customers use the CATIA Magic portfolio for MBSE. For Ben, one of the distinguishing characteristics of the tools is their focus on usability.
He first encountered CATIA Magic when he was introduced to MBSE in 2017 and describes the portfolio as having grown significantly in popularity since then. His emphasis, however, is not simply on adoption numbers. It is on how engineers experience the software.
Ben highlights the intuitive organization and user interface of CATIA Magic. In his view, putting the user at the center of the experience can address one of the practical obstacles to MBSE adoption: the perception that modeling tools are too difficult or complex.
This is particularly relevant when organizations are trying to bring more engineers into an MBSE workflow. If the software itself creates unnecessary friction, teams may spend their energy learning the tool instead of applying systems engineering practices.
Ben also points to the direction of the 2026 version of CATIA Magic, describing its focus on user experience as an important part of reducing those barriers.
For him, usability is therefore not simply a software preference. It can influence whether engineers are willing and able to adopt model-based ways of working.
CATIA Magic as a productivity environment
The benefit Ben hears most clearly from customers is productivity.
That productivity comes from using the model as a central source of engineering information. Once the model contains the relevant elements and their relationships, engineers have a consistent place to look for answers.
A question about an interface can lead back to the model. So can a question about requirements or system behavior.
This changes the role of the model from being simply something engineers create into something they actively use during engineering work.
The productivity gain is therefore not described as a single feature of CATIA Magic. Instead, it comes from the combination of model-based working and a tool that allows engineers to access and work with that information.
For programs where engineers regularly need to navigate large amounts of interconnected system information, having a recognized source of truth can reduce the time spent searching for answers and redirect that effort toward engineering decisions.
Bringing AI into the CATIA Magic workflow
Ben sees AI as a natural next step for MBSE tools such as CATIA Magic.
Belcan has been exploring how AI could be applied in this space, with the goal of assisting systems engineers rather than replacing them. The emphasis is on using AI to accelerate activities that currently require significant manual effort.
One example is requirements analysis. An AI assistant could help review requirements and identify whether they are written in a way that aligns with expected requirements practices. It could also help identify potential gaps, such as missing requirements or architectural elements.
Another opportunity is model interrogation. Instead of manually performing certain queries or analyses, engineers could use AI to interact with the information contained in the model and obtain metrics or other useful insights more quickly.
This creates a different role for AI within MBSE. Rather than replacing the model or the engineer, AI can become an additional interface through which engineers work with the model.
For Ben, that is where the combination of AI and MBSE becomes particularly interesting: using automation and natural-language interaction to increase the productivity already enabled by model-based engineering.
From model creation to faster engineering decisions
The broader opportunity Ben describes is not simply about creating models faster. It is about shortening the path from engineering information to engineering action.
A connected MBSE model gives teams a structured representation of the system. CATIA Magic provides the environment in which that information can be developed and managed. AI could add another layer by helping engineers review, query and analyze the information more quickly.
That combination could support organizations trying to move products through development faster while controlling costs.
The important distinction is that AI remains an assistant in this workflow. Ben specifically frames its role around supporting the systems engineer reviewing information, identifying potential gaps, generating metrics and accelerating model queries.
In this view, the future of MBSE is not about removing the engineer from the process. It is about reducing the amount of time engineers spend on tasks that can be assisted by technology, leaving more time for engineering judgment and decision-making.
Note that Dassault Systemes is rapidly expanding out of the box enterprise level AI capabilities to above mentioned and other use case.
A practical path toward the next generation of MBSE
Ben’s perspective brings together three different parts of the MBSE challenge.
First, organizations need a clear implementation strategy rather than jumping directly into advanced capabilities. Second, engineers need tools that are approachable enough to support adoption at scale. Third, emerging technologies such as AI can extend what engineers are able to do with the information already contained in their models.
CATIA Magic sits at the center of that workflow for Belcan and many of its customers. Its role is not only to provide a modeling environment, but to give engineers a practical place to create, connect and work with system information.
As AI becomes increasingly integrated into engineering tools, the opportunity is to build on that foundation making it easier to interact with models, identify information gaps and extract useful insights without adding unnecessary manual work.
Conclusion
For Ben, successful MBSE starts with a clear implementation strategy and continues with tools that engineers can actually use. CATIA Magic provides the modeling environment, while AI offers new ways to accelerate analysis and interaction with the model.
The direction is clear: make connected engineering information easier to use, and give engineers more time to focus on engineering decisions.
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FAQ
- Why does Ben consider MBSE important?
He sees MBSE as a way to bring requirements, interfaces, architecture, behavior and other system information into a connected model. This gives engineers a central place to query information and understand how changes affect the system.
- What does Ben recommend organizations do before implementing MBSE?
He recommends establishing a roadmap first. Organizations should define their objectives, set goals, determine realistic timelines and understand the budget and business proposition before moving into more advanced MBSE activities.
- Why does Ben value the CATIA Magic portfolio?
He highlights its user experience, including its intuitive interface and organization. He believes focusing on usability can help remove barriers for engineers who might otherwise perceive MBSE tools as too complex.
- How does CATIA Magic contribute to engineering productivity?
According to Ben, productivity comes from having a connected model that engineers can use as a source of truth. Instead of searching across different sources, engineers can return to the model for information about requirements, interfaces and system behavior.
- How could AI support MBSE in CATIA Magic?
Ben sees AI as an assistant for systems engineers. Potential applications he identifies include reviewing requirements, identifying missing requirements or architectural elements, generating metrics and querying the model more quickly.
Saulius Pavalkis is a CATIA CYBER SYSTEMS Industry Process Expert Director, within the CATIA Cyber Systems EE&F AMERICA organization.