Embracing AI in Model Based Systems Engineering (MBSE) – Envisioning the Future

Model-Based Systems Engineering (MBSE) has already transformed the way complex systems are conceived, designed, and validated. As industries move toward digital engineering and connected ecosystems, the integration of Artificial Intelligence (AI) into MBSE represents a potential next leap forward. AI technologies have the capacity to support human decision-making, help automate repetitive engineering tasks, and enhance traceability and consistency across the digital thread. By leveraging AI-driven insights, engineers may be able to more rapidly explore design trade-offs, model potential system behavior under varying conditions, and work toward optimizing performance with greater precision.

Looking ahead, MBSE may evolve into a more intelligent, adaptive environment where AI functions as a collaborative partner rather than simply a tool. Natural language processing could enable engineers to interact with models conversationally, potentially reducing barriers to adoption for non-technical stakeholders. Machine learning models trained on historical data might offer design suggestions, highlight potential risks earlier, and support faster validation cycles. In addition, AI integration could help foster collaboration across disciplines by providing context-aware recommendations aligned with enterprise goals and relevant industry guidelines.

The fusion of AI with MBSE is not only about efficiency; it is about exploring new ways to approach engineering challenges. It presents opportunities for innovation, adaptability, and resilience in designing next-generation systems—whether in aerospace, automotive, healthcare, or digital infrastructure. This session will explore potential use cases, the evolving role of engineers, and the ethical considerations that arise with AI-augmented design practices. Together, we will examine a forward-looking vision for how MBSE and AI might converge to create a more intelligent, adaptive engineering ecosystem.

Key takeaways:

• AI integration in MBSE has the potential to support greater automation, traceability, and decision support across the digital thread.
  

• Future MBSE environments may incorporate natural language interaction and predictive analytics to help foster collaboration and increase accessibility.
  

• Engineers’ roles could evolve from primarily creating models to supervising and validating models with the assistance of AI-driven insights.
  

• The synergy of AI and MBSE may create opportunities to accelerate innovation, identify potential risks earlier, and support the development of more adaptable system designs.