How AI is helping GM build better vehicle software, faster

Artificial intelligence is rapidly reshaping software development, but vehicle programs demand more than fast output. In a software-defined vehicle, speed only matters when the resulting software remains readable, testable, maintainable, and reviewable inside a tightly controlled engineering process.

GM teams used that lens in a broader research effort across the software development life cycle. The question was not whether AI could generate code, but where it could reduce friction without weakening the discipline production software requires.

What Happened

Across very different kinds of engineering work, the same pattern emerged: AI is most useful when it works inside a structured engineering loop and produces bounded first drafts that engineers can verify. The V-model is a useful way to view that work.

• Their approach uses structured prompting and explicit compliance checking patterns to flag potential violations, but human engineers retain final approval authority.

• GM’s research found that even cleaned-up embedded C functions can violate MISRA rules, break logic, or fail to compile in context.

• AI-generated code cannot be assumed to meet MISRA standards without engineer review.

Key Details

Across that life cycle, GM’s work kept returning to the same question: where can AI remove friction while keeping engineering judgment firmly in control? Automotive software is a promising domain for AI because the work is complex, contains recurring engineering tasks, and.

• In each case, AI produces bounded first drafts that engineers review and approve.

• Specific use cases include real-time MISRA compliance guidance via an Intelligent Virtual Advisor (IVA), refactoring embedded C code, translating legacy Perl scripts to Python, and generating test artifacts from requirements.

• GM is applying large language model (LLM)-based methods across the software development life cycle to reduce engineering friction.

Why It Matters

Teams manage large codebases, specialized toolchains, demanding quality expectations, and software that evolves as vehicle capabilities expand. Research has found that automotive software complexity has grown faster than productivity [1].

• In automotive development, that is the combination that makes AI not just interesting, but durable and worth adopting.

• It can serve as a practical layer of assistance that helps experienced teams move faster while keeping human judgment, technical evidence, and final responsibility firmly in place.

What Reports Say

Coverage of the story so far points to:

• Continued reporting by SD Times as more details emerge