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 SodiusWillert AI Modeling Assistant (SAM) for IBM Rhapsody, 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.

SAM provides comprehensive capabilities across key MBSE activities (some of which will be demonstrated), including:

• interpreting requirements documents and automatically generating corresponding models,
• analyzing existing architectures to summarize their purpose and identify issues,
• producing professional-grade documenting models and explanations,
• accelerating team onboarding through worked examples and educational support,
• reverse engineering of existing code into structured models, while also conducting thorough model and code reviews,
• performing requirements coverage analysis to identify gaps and conducts impact analysis for planned changes, helping teams understand cascading effects across complex systems,
• activating automations and leveraging existing models and documents for contextual understanding, making it adaptable to diverse engineering environments.

This demonstration illustrates how, by integrating AI into modeling, we can dramatically reduce 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.