IBM announces new AI automations for Model Based Systems Engineering and other innovations for Engineering Lifecycle Management

IBM Engineering AI Hub is designed to automate repetitive tasks, increase clarity, and accelerate decision-making for engineering building software-intensive systems, often including hardware, electronics. Version 1.1 introduces a new agent and enhancements that expand how teams can apply AI to systems engineering, requirements and workflow management.

A key addition to Engineering AI Hub 1.1 is the Model-Based Systems Engineering (MBSE) use-case discovery agent, built to interpret natural-language requirements and create corresponding model elements in Rhapsody Systems Engineering and Rhapsody 10 (SysML v2 and SysML v1, respectively). This capability helps reduce early modeling effort, lower the cognitive load for MBSE practitioners, and improve digital continuity through traceability between requirements and system models. It’s the first example we are delivering of the many possible AI-powered automations for MBSE practitioners.

Engineering AI Hub 1.1 includes refined experiences for requirement quality analysis, work item synopsis, and administrative controls that allow teams to configure which Large Language Models (LLMs) best fit their organizational needs.

These updates are aimed to help engineers access information faster, improve requirements clarity, streamline reviews and accelerate modeling workflows, while keeping human experts in full control of every AI-suggested output.

Newly-released IBM ELM 7.2 delivers a set of usability and performance improvements designed to help engineering teams work efficiently while supporting the end-to-end traceability needed for compliance and complex systems development. ELM provides the engineering lifecycle foundation for requirements, workflow and test management, configuration management and reporting, as well as systems and software modeling—now with optimizations that help teams operate at greater scale.

Improvements to help engineers collaborate faster and make easier use of system-of-record engineering data in reports, dashboards and generated documents include:

• Refreshed editing experience for work items and test artifacts, and new threaded commenting on IBM Engineering Rhapsody model elements directly in the browser. 
• New reporting capabilities, including expanded pre-defined and custom historical metrics, improved report viewing, and enhanced traceability for requirement collections, help engineering leaders gain clarity across programs. Publishing updates, including cleaner Excel output and image/table handling, make it easier to produce audit-ready deliverables.

Improvements in planning and software development include:

• More efficient planning, making use custom attributes in Quick Planner.
• More productive workflows for developers managing source code in ELM, making use a new VS Code extension for Engineering Workflow Management (EWM), including the ability to use this extension with Project Bob and other agentic AI systems.

Systems Engineering plays an increasingly critical role in the development of software-defined, multi-disciplinary systems. In addition to the MBSE use case discovery agent, IBM Rhapsody Systems Engineering 1.6 brings new capabilities for methods & tools teams, and OEM and ISV partners to customize and extend Rhapsody Systems Engineering for their user communities. Additional user experience optimizations are designed to reduce effort and cognitive load for practitioners.

IBM is innovating to help engineering teams innovate faster. For more information about these new releases, book a live demo with the IBM Engineering Lifecycle Management experts or see the resources below.

Explore Engineering AI Hub 1.1

Discover Engineering Lifecycle Management 7.2

See Rhapsody Systems Engineering v1.6

IBM Engineering Rhapsody 10.0.2 profile for MBSE use case discovery agent