Before proposing any automation, AI integration, workflow redesign, or open-source migration, I always start with the same foundation: understanding the real context and purpose behind the project.
Most engineering teams jump straight into “what we want to automate,” but the real breakthrough comes from asking why the process is worth automating in the first place.
This category helps uncover motivations, goals, constraints, and the technical environment that drives decision-making.
1. Why do you want to automate this process?
This is the single most important question. Companies often request automation because a process feels inefficient, slow, or outdated — but that’s only the surface.
The real drivers might be:
- reducing engineering hours spent on repetitive tasks,
- increasing throughput of simulations,
- avoiding human errors in pre- or post-processing,
- improving traceability and project consistency,
- or eliminating licensing bottlenecks.
Understanding the true motivation ensures the solution is aligned with actual business and engineering needs, not assumptions.
2. What is the critical goal of this project (both business and technical)?
Every project has a visible goal (“we want to run simulations faster”) and a hidden one (“we need to deliver to the client in half the time”).
This question uncovers both:
- Business goals → ROI, reduced cost, faster delivery, improved competitiveness
- Technical goals → eliminating manual steps, standardizing workflows, enabling HPC scaling, integrating new solvers
When these goals are clearly defined, automation becomes intentional and measurable — not just an IT experiment.
3. What does the current workflow look like, and where are its bottlenecks?
To design automation, I must first understand how engineers work today. Bottlenecks often hide in unexpected places:
- manual geometry preparation,
- repetitive meshing steps,
- manual editing of solver input files,
- copy-pasting results between tools,
- inconsistent file naming and versioning.
Mapping the workflow helps reveal where time is lost, where errors occur, and which steps should be automated first.
4. Which data, files, and tools are essential in your current process?
CAE workflows depend heavily on diverse toolchains and data structures — CAD, meshes, solvers, scripts, spreadsheets, HPC systems.
Understanding the current ecosystem ensures:
- compatibility with existing tools,
- proper handling of file formats,
- seamless integration with solvers,
- respect for existing data management rules,
- and avoidance of unnecessary migrations.
This question prevents building automation that breaks the current toolchain or creates additional complexity.
Summary: Why these questions matter
These four questions build the foundation for any successful automation initiative.
They help uncover real motivations, establish clear goals, expose workflow inefficiencies, and ensure we understand the technical environment before proposing solutions.
Without this clarity, even the best automation or AI project risks missing its targets.
This category ensures we start with precision, not guesswork.
