How to Implement Artificial Intelligence (AI) in MBSE Workflows with EWB Professional and Accuris Thread
As engineering teams evolve toward Model-Based Systems Engineering (MBSE) and digital engineering, a new frontier is emerging: integrating artificial intelligence (AI) into MBSE workflows across the entire product life cycle. The newly introduced AI-Augmented MBSE Maturity Model provides a valuable framework to assess current capabilities and chart a course toward greater automation and decision support.
Model-Based Systems Engineering serves as the primary means for exchanging information, feedback, and requirements within engineering processes, transitioning from traditional document-centric systems to digital models.
To help organizations climb this maturity curve, Engineering Workbench Professional and Accuris Thread deliver the knowledge, automation, and AI-powered tools needed to move from manual to AI-augmented systems engineering.
Understanding the AI-Augmented MBSE Maturity Model for Complex Systems
The AI-Assisted Maturity Model for Model-Based Systems Engineering (ZWF, De Gruyter 2024) defines six levels of increasing AI integration in MBSE:
- Level 0: Manual MBSE – Traditional workflows with no AI assistance.
- Level 1: AI-Supported Engineering – Generic AI capabilities used to support technical workflows
- Level 2: Dedicated Engineering Copilots – AI technologies and copilots support engineers with automation of specific development tasks.
- Level 3: Continuous AI-Integrated Engineering – AI copilots can automate entire development processes.
- Level 4: AI-Driven Engineering – AI assistants work alongside engineers as a part of the team, with human experts outlining and coordinating development.
- Level 5: Autonomous AI Engineering – AI assistants are capable of owning, planning, and executing strategy and tasks across the engineering process start to finish, proactively developing, validating, and evolving models.
Most organizations are working within Level 0 to Level 2. Advancing beyond them requires digital platforms designed for AI-assisted engineering and scalable automation.
The methodologies involved in advancing MBSE maturity include streamlining and automating various processes, which enhances productivity and reduces manual effort.
Advancing MBSE Maturity with Engineering Workbench Professional in Systems Engineering
EWB Professional provides foundational capabilities that help teams transition from manual to semi-automated MBSE:
- Engineering Knowledge Management: EWB centralizes validated engineering standards, regulations, codes, specifications, patents, and technical reference materials, enhancing engineering practices through the digitalization and centralization of traditionally unstructured data.
- AI-Powered Search: Artificial intelligence like machine learning, natural language processing, and semantic search help jumpstart research, development, and analysis of standards and engineering information critical to your systems and projects.
- Automated Engineering Workflows: Built-in tools support compliance management, impact analysis, and change management with automated alerts when standards content changes, and AI-enabled automatic comparisons between revisions of documents.
- Integrate Standards Content into MBSE Tools: Direct connections from standards content into modeling platforms enable seamless integration of technical knowledge into system models.
These capabilities support systems engineering automation and dramatically reduce manual overhead—key characteristics of maturity levels 2 and 3 in the AI-Augmented model.
Unlocking AI-Augmented MBSE with Accuris Thread
Accuris Thread focuses on AI-driven improvements in requirements management, a core pillar of MBSE maturity:
- Automated Requirements Extraction: Identify, define, and automatically extract requirements from documents with zero manual rework.
- Natural Language Understanding: Use natural language processing to automatically tag, classify, and append requirements with metadata using AI-based semantic analysis.
- Intelligent Traceability: Create dynamic, AI-supported digital threads between requirements, models, systems, and other important documentation like audit or compliance reports.
- Integration with MBSE Applications – Directly connect extracted requirements into your other model-based tools and systems.
With these features, Accuris Thread empowers engineering teams to move into AI-augmented MBSE workflows (maturity levels 3 and 4), laying the groundwork for future autonomous systems engineering.
Why Artificial Intelligence in MBSE Matters Now
Incorporating AI in MBSE isn’t a future vision—it’s a present need. The pressure to innovate faster while maintaining quality and compliance is pushing organizations to adopt MBSE tools, AI, and digital threading solutions that automate modeling, simulation, and analysis. Simulations play a critical role in validating system performance and behavior, ensuring that designs meet requirements and function as intended.
Together, EWB Professional and Accuris Thread enable:
- Connected information across complex systems
- Accelerated cycle time
- Reduced risk, reduced rework
- Scalable AI integration aligned with digital engineering goals
- Improved systems engineering traceability and collaboration
These tools leverage AI to improve efficiency in engineering and decision-making, reducing risk and accelerating time to market.
Final Takeaway: Advancing AI in Digital Systems, Modeling, and Design
If you’re exploring how to implement AI in your MBSE strategy, start by benchmarking where you are today using the AI-Augmented MBSE Maturity Model. Then, equip your teams with tools like EWB Professional and Accuris Thread that are built to accelerate progress across that spectrum—from assisted workflows to intelligent automation.
Leveraging AI technologies can significantly enhance Model-Based Systems Engineering (MBSE) by improving efficiency and effectiveness in your systems, processes, models, and designs. Machine learning algorithms can analyze large datasets to optimize analysis, automate requirements identification, and improve modeling and simulation by generating and refining system models based on requirements, performance data, and feedback.
With the right tools in place, engineering organizations can turn MBSE from a manual exercise into an intelligent, adaptive process—one that’s ready for the demands of modern digital engineering and the opportunities of AI.