Using AI in your Engineering Workflow: A Step-by-Step Method That Works
- Patrick Law
- Jun 9
- 2 min read
AI adoption in engineering doesn’t require a full system overhaul.
We integrate AI where it makes measurable impact—starting with routine tasks, testing results, and sharing what works. This guide outlines how you can do it in your Engineering Workflow. 1. Start With Repetitive Tasks
Skip theory. Begin with real tasks—e.g weekly reports, documentation, data formatting.
Choose one repetitive task.
Use a tool like ChatGPT to complete it.
Record the process and share it with the team.
Assign one team member to test the process for a week and give feedback.
2. Document and Reuse What Works
Useful AI prompts and workflows shouldn’t be one-offs.
Update the Operations Manual with the new AI-enhanced step.
Save effective prompts and results in the Prompt Library.
Make everything easily accessible and reusable.
3. Select Tools Based on Fit
Ignore trending apps. Focus on tools that fit existing tasks.
Examples: Use ChatGPT for writing your project report.
Assign one team member to explore and test the tool.
Use what improves speed, clarity, or accuracy.
4. Apply the System
We test AI on real engineering work. If it saves time or improves quality, document it.
No top-down mandates. Individual engineers propose, test, and improve workflows.
Prompt Library is regularly updated based on results.
Implementing AI in your engineering organization isn’t a one-time project. It’s a structured, ongoing effort that starts with identifying repetitive tasks, selecting pilot projects, assigning ownership, and continuously refining the process.
Each step from tool selection to prompt documentation—builds on the last. Over time, what starts as a test becomes a standard. Engineers shift their focus from manual repetition to high-value problem-solving. Lessons learned compound. Teams become faster, more accurate, and more consistent.
It’s not about hype. It’s about operational improvement that engineers can see and measure.
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