Automation in CAE is never just about tools or technology — it’s about people, workflows, habits, and organizational readiness.
The second category focuses on the human and operational side of engineering work.
Understanding who will use the system, how they currently work, and what constraints they operate under is key to designing solutions that engineers actually adopt.
5. Who will be using the solution, and what is their skill level?
A tool designed for senior developers will look very different from a tool designed for mechanical engineers with minimal scripting experience.
This question reveals:
- expected complexity of the user interface,
- acceptable level of automation,
- whether training will be required,
- and how intuitive the workflow must be.
Good automation adapts to the people using it, not the other way around.
6. Do your engineers work manually, semi-automatically, or with scripts today?
This determines the automation maturity level of the organization.
Some teams still perform almost everything manually; others already use Python, Bash, or in-house tools.
Understanding this helps design solutions that:
- fit into existing habits,
- avoid overwhelming users,
- build on what already works,
- and improve rather than disrupt current workflows.
It also prevents reinventing tools the team might already have.
7. Which standards, regulations, or internal procedures do we need to respect?
ngineering teams operate under strict requirements — ISO standards, safety procedures, simulation guidelines, validation rules, and traceability expectations.
Ignoring these can result in a solution that is technically impressive but unusable in real production.
This question ensures automation supports compliance instead of breaking it.
8. Do you have internal hardware, licensing, or environment limitations?
Automation can be blocked by real-world constraints such as:
- limited HPC capacity,
- overloaded GPUs,
- license restrictions on commercial solvers,
- strict IT policies,
- limited cloud access,
- or weak workstation performance.
Identifying constraints early allows us to design solutions that are feasible, scalable, and cost-effective.
Summary: Why these questions matter
This category ensures that automation is not built in a vacuum.
It aligns solutions with the organization’s skill level, ensures compliance with standards, and respects hardware and operational limitations.
Ignoring these questions leads to tools that look good in demos but fail in real engineering environments.
These answers ensure adoption, usability, and long-term success.