From Manual Mesh Files to AI-Powered Engineering: What 20 Years in Engineering Has Taught Me
Blog Post 1 of 3: The Engineering Transformation
Twenty years ago, I began my engineering career by manually creating mesh files for fluid analysis and attempting to identify the variables that affected flow optimization for drill bit design. Last week, I focused on a development proposal and vendor selection process that previously required traveling to multiple trade shows and coordinating a large team to write comprehensive proposals. What once took months of industry networking and collaborative writing can now be accomplished in weeks with AI-assisted research, proposal generation, and vendor analysis.
Meanwhile, friends in the industry tell me how generative code tools have revolutionized their development cycles—what used to be weeks of coding is now days of AI-assisted development and human refinement.
The difference? Intelligent use of AI as a force multiplier, not a replacement.
The Reality Check
As an engineering leader who has worked across oil and gas, DoD, surveillance, and consumer goods and services, I've witnessed AI's evolution from an interesting concept to an indispensable tool that's fundamentally changing how we approach engineering challenges.
The most successful engineers I work with today understand a crucial truth: AI doesn't replace engineering thinking—it amplifies it.
Where AI is Making the Biggest Impact
The engineers who thrive are those who use AI to:
Accelerate iterative design cycles – What used to take weeks of modeling and simulation now happens in days, allowing for more thorough exploration of design spaces.
Enhance pattern recognition – AI helps identify subtle correlations in complex systems that human analysis might miss, leading to more robust designs and faster troubleshooting.
Automate routine analysis – Freeing engineers to focus on creative problem-solving, system architecture, and innovation rather than repetitive calculations.
Improve documentation and knowledge transfer – AI-assisted documentation ensures institutional knowledge is captured and accessible, critical for complex long-term projects.
The Transformation in Practice
In fluid dynamics alone, the change has been remarkable:
Mesh generation that took days now happens in hours
Parameter optimization across dozens of variables happens automatically
Flow analysis identifies optimal configurations faster than ever
Design iterations happen at speeds that enable true innovation
But here's what I've learned: the magic isn't in the AI itself—it's in how experienced engineers use it to push the boundaries of what's possible.
Why This Matters for Engineering Leaders
Organizations that successfully integrate AI into their engineering practice are seeing significant competitive advantages:
Faster time-to-market – Accelerated design cycles mean getting innovative solutions to customers sooner
Higher quality outcomes – AI-assisted analysis catches potential issues earlier in the development process
More innovative solutions – Engineers freed from routine tasks can focus on creative problem-solving and breakthrough innovations
Better resource utilization – Teams can tackle more complex projects with the same headcount
The Key Insight
After 20 years in this field, I've learned that the best engineering outcomes come from thoughtful integration of AI tools with human expertise. The engineers and leaders who understand this distinction—and act accordingly—will define the future of our profession.
Tomorrow I'll share where human engineering judgment remains absolutely critical, and why that's actually great news for engineering leaders.
What's been your experience with AI integration in your engineering work? Are you seeing similar acceleration in your field?
#Engineering #AI #Innovation #Leadership #Technology #EngineeringLeadership
Brian Adams is an engineering leader and author who has worked across oil and gas, DoD, surveillance, and consumer goods industries. Connect for more insights on engineering leadership and innovation.
