What’s Trending in Model-Based Systems Engineering for 2026?

Model-based systems engineering (MBSE) has been around for a while, but it continues to gain ground in engineering projects to shorten development cycles and reduces errors. MathWorks has been delivering MBSE tools, and I talked with Becky Petteys, Systems Engineering Segment Manager, about trends she sees for this year.

What limitations of document-based systems engineering are driving adoption of system-level models?

Traditional document-based systems engineering has difficulty handling the complexity of modern systems that combine software, electronics, and networked components. Most system information is captured in static documents such as Word, Excel, and PowerPoint. While these tools document requirements and designs, they do not represent the relationships between system elements very well.

Another limitation is the lack of an authoritative source of truth. Information is spread across many documents owned by different teams such as software, safety, and system architecture, which can quickly fall out of sync.

Because the information is written in natural language, it can also be interpreted differently by different groups. This often leads teams to develop components independently, with integration problems only appearing later in the development cycle.

System-level models address these issues by providing a shared representation of the system architecture. Models make dependencies and interfaces explicit and allow teams to analyze and simulate system behavior earlier in development.

For example, Gulfstream Aerospace adopted a systems engineering approach called eSAM built on System Composer. Their engineers use it to create functional and logical architecture models for aircraft electronic systems, replacing a largely document-based approach. This allows them to automate previously manual tasks and analyze their system for possible issues earlier in the process.

How do high-fidelity digital twins expose system-level issues earlier in development?

High-fidelity digital twins help expose system-level issues earlier by moving beyond static representations to executable models of the system.

Instead of relying only on diagrams or descriptive models, engineers can simulate how a system behaves over time within a physics-based environment. This makes it possible to observe interactions across components and disciplines, which helps uncover issues such as interface mismatches, unexpected dependencies, or performance limitations before physical hardware is built.

Digital twins facilitate a "shift-left" strategy, where teams can evaluate requirements, architecture, and system behavior much earlier in the development process. Engineers can run virtual tests, analyze edge cases, and inject faults into the model to study how the system responds. This supports the “shift-left” approach to verification and validation, where potential problems are identified and addressed in simulation rather than during the late-stage integration or physical testing.

In this example here, high-fidelity digital twins extend this MBSE approach by turning system models into executable simulations, enabling engineers to observe real system behavior and interactions across components early in development. This allows for earlier verification and validation and helps teams identify interface issues, dependencies, and performance limitations during the design phase.

Where is model-based systems engineering seeing the fastest adoption, and what technical pressures are driving it?

MBSE is seeing the fastest adoption in industries where system complexity is increasing rapidly due to software, connectivity, and autonomy. As products become more software-intensive and operate as part of larger systems of systems, traditional document-based approaches make it difficult to manage interactions across mechanical, electrical, and software domains.

These pressures are especially visible in sectors such as aerospace, automotive, defense, energy, and healthcare. For example, the shift toward software-defined vehicles and service-oriented architectures in the automotive industry requires development approaches that can manage complex communication patterns, frequent software updates, and tight integration between subsystems.

Similar challenges appear in aerospace and energy systems, where safety requirements, regulatory constraints, and large-scale infrastructure make early system-level analysis increasingly important.

MBSE helps address these challenges by allowing engineers to represent system architectures, analyze interactions across domains, and evaluate system behavior earlier in development.

For example, Siemens Energy uses Simulink and MBSE methods for project line engineering. This approach allows their teams to manage complexity in grid technologies by combining standardized architectures with project-specific customization, helping engineers reuse designs while adapting them to different deployment requirements.

How is AI being used within model-based systems engineering workflows today?

AI is starting to play a practical role in MBSE by helping engineers manage complexity and automate routine work.

AI tools can analyze requirements documents and automatically generate early system or behavior models. This helps engineers quickly create a structured starting point for design, bridging the gap between stakeholder needs and technical architecture.

AI generates simplified versions of high-fidelity simulations that preserve essential system dynamics while running faster. These reduced-order models (ROMs) allow engineers to include detailed subsystem behavior in full-system simulations without excessive computational cost.

Large language models (LLMs) and AI algorithms assist with drafting initial requirements, performing model consistency checks, and generating documentation. By offloading these routine tasks to AI, engineers can focus on higher-value design and analysis work.

AI can help identify edge cases, suggest parametric studies, and evaluate system behavior early in the development cycle. This supports a “shift-left” approach, uncovering potential issues before physical prototypes are built and reducing the risk of late-stage integration problems.

What role does data science play in managing complexity in modern systems engineering?

Data science plays a key role in managing complexity in modern systems engineering by enabling engineers to analyze the vast amounts of data generated by simulations, tests, and operational systems. Techniques such as clustering, outlier detection, and probabilistic reasoning allow teams to explore large system state spaces, identify undesirable behaviors, and quantify risk in ways that manual review or traditional spreadsheets cannot.

Data science also helps correlate diverse parameters to assess overall system health and predict potential failures early. By combining simulated, experimental, and operational data with design models, engineers gain a deeper understanding of complex system behavior and can evaluate alternatives more rigorously.

What are the main technical and organizational challenges in adopting model-based systems engineering?

Adopting MBSE presents both organizational and technical challenges. On the organizational side, the biggest hurdle is changing long-standing habits and culture. Many companies rely on document-centric processes, and shifting to a model-based approach requires a mindset change across engineering teams. Coordination between disciplines such as systems engineering, software, and user experience is critical to designing complex, integrated systems but often difficult to achieve.

Technically, MBSE adoption is complicated by fragmentation across engineering tools and data. System models are often stored in proprietary formats, making cross-tool data exchange challenging and dependent on custom integrations. Maintaining traceability across these disconnected artifacts can require significant manual effort.

Emerging standards like SysML v2 aim to enable data-centric, interoperable workflows. But fully achieving seamless collaboration across the digital ecosystem remains a work in progress.

What measurable benefits do teams see when MBSE is combined with AI and data science?

Combining MBSE with AI and data science allows engineering teams to evaluate more design alternatives and make decisions earlier in the development process. One key benefit is the ability to explore a much larger design space.

AI-assisted analysis and simulation can evaluate significantly more design variants than traditional approaches. This helps engineers better understand tradeoffs related to performance, safety, and reliability.

Another measurable benefit is the automation of routine engineering tasks. System models combined with data-analysis tools can streamline activities such as data entry, report generation, and safety analysis workflows. This reduces manual effort and allows engineers to focus more on system architecture and design decisions.

The integration of models, data, and analytics also helps maintain a digital thread across requirements, design, and verification, reducing the likelihood of late-stage integration issues and shortening development cycles.