Model-Based Systems Engineering (MBSE) has become essential for managing complex system development, yet the steep learning curve and time-intensive nature of modeling tools like IBM Rhapsody often limit their effectiveness. This presentation introduces the pros and cons of applying artificial intelligence to Model-Based Systems and Software Engineering (MBSE). The presentation also includes a demonstration of its practical application using Sodius Willert AI Modelling Assistant (SAM), a novel Model Context Protocol (MCP) server that bridges artificial intelligence with IBM Rhapsody, fundamentally transforming how engineers interact with such a modeling tool.

SAM enables AI assistants like Claude, Anthropic's AI assistant, but also all others like ChatGPT or even your own, to directly read, write, and manipulate Rhapsody models, creating unprecedented possibilities for automation and intelligent assistance in systems and software engineering workflows. By leveraging natural language interactions, engineers can now delegate complex modeling tasks that previously required extensive manual effort and deep tool expertise.

The demonstration showcases SAM's comprehensive capabilities:

• SAM can interpret requirements documents and automatically generate corresponding models.
• Analyze existing architectures to summarize their purpose and identify issues.
• Document models with professional-grade explanations.
• It excels at educational support, providing worked examples in Rhapsody to accelerate team onboarding.
• For legacy systems, SAM performs reverse engineering of existing code into structured models, while also conducting thorough model and code reviews.

Critical to modern agile development, SAM performs requirements coverage analysis to identify gaps and conducts impact analysis for planned changes, helping teams understand cascading effects across complex systems. The tool can activate automations and leverage existing models and documents for contextual understanding, making it adaptable to diverse engineering environments.

This demonstration illustrates how AI-augmented modeling dramatically reduces the time and expertise barriers in MBSE, enabling engineers to focus on design decisions rather than tool mechanics, ultimately accelerating development cycles and improving model quality across the systems engineering domain.