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Engineering & Simulation · Engineering
Generative AI / CAD & Workflow Copilots
Simulation workflow copilot in engineering
Scaling Adjacent medium effect
Core capability
This makes complex engineering software easier and faster to use, reducing friction in routine tasks and lowering the effort required to work across CAD and PLM workflows.
How it works
The user asks for an action in plain language, and the copilot interprets the request, selects the right operation, and executes it in the engineering tool without forcing the user through every manual step.
Application here
An AI assistant helps automate simulation setup and post-processing, making analysis tools easier to use.
Business impact
This can reduce setup time and make simulation workflows more accessible to engineers who are not deep CAE specialists.
Limitations
Automated setup can still introduce subtle errors, and expert review is needed to confirm that the simulation approach is appropriate.
In production
This is already helping engineering teams work faster day to day by reducing tool friction and automating routine software actions.
Research
The frontier is toward AI assistants that can collaborate on larger engineering jobs, split work across subtasks, and support assembly-scale workflows rather than only single commands.
Examples
Transforming Manufacturing Simulation with AI: BSH's journey with Siemens Process Simulate Copilot
Description: BSH tested an AI copilot in Process Simulate for manufacturing simulation. It guided beginners through pick-and-place and screwing operations, reducing optimization time from hours to minutes and providing better sequences than manual methods in some cases. Expert review confirmed outputs.


Eaton Accelerates Product Design with Generative AI
Description: Eaton integrated generative AI into CAD workflows to simulate manufacturability and costs from design inputs. This cut design time by 87% for parts like heat exchangers and gears, enabling faster iterations while embedding analysis early. Engineers reviewed AI outputs for production.


Generative AI in Manufacturing: 6 Use cases + Real-life Examples
Description: Bosch used generative AI for MEMS sensor design optimization, automating topology generation on production data. Tasks taking months reduced to days, accelerating engineering workflows. The toolchain supported simulation-like structural analysis for vehicle components.
https://blogs.sw.siemens.com/tecnomatix/transforming-manufacturing-simulation-with-ai-bsh-journey/https://www.getstellar.ai/blog/revolutionizing-manufacturing-with-ai-real-world-case-studies-across-the-industryhttps://masterofcode.com/blog/generative-ai-in-manufacturing
Sources
Ansys AI — ; Ansys SimAI —
https://www.ansys.com/aihttps://www.ansys.com/products/ai/ansys-simai