LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study

📰 ArXiv cs.AI

LLM-powered workflow optimization improves multidisciplinary software development in the automotive industry

advanced Published 25 Mar 2026

Action Steps

Identify inefficiencies in current workflow
Implement LLM-powered tools to automate coding tasks and facilitate collaboration
Develop a shared view of domain knowledge and implementation among team members
Monitor and refine the optimized workflow

Who Needs to Know This

Developers, domain experts, and project managers on multidisciplinary software development teams benefit from LLM-powered workflow optimization as it streamlines collaboration and improves efficiency

Key Insight

💡 LLM-powered workflow optimization can bridge the gap between domain experts and developers, improving collaboration and efficiency

Key Takeaways

LLM-powered workflow optimization improves multidisciplinary software development in the automotive industry

Full Article

Title: LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study

Abstract:
arXiv:2603.21439v2 Announce Type: replace-cross Abstract: Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets. Today, even with AI coding assistants like GitHub Copilot, this process remains inefficient; individual coding tasks are semi-automated, but the workflow connecting domain knowledge to implementation is not. Developers and experts still lack a shared view, resulting in repeated coord

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