AI is making engineering faster.
We can research a problem, generate concepts, analyze data, write code, summarize technical information, explore design alternatives, and create documentation in a fraction of the time it took just a few years ago.
That’s exciting. But there’s a question I think we need to be asking more often: Faster at what? Because accelerating engineering work only creates value if we are solving the right problem.
AI Can Give You an Answer. Engineering Judgment Tells You Whether It Makes Sense.
I’ve spent more than 40 years developing products and leading engineering teams, and some of the most expensive engineering mistakes I’ve seen weren’t caused by engineers who couldn’t design. They were caused by starting to design too soon. The requirements weren’t understood. The physics hadn’t been worked through. Manufacturing wasn’t considered early enough. Cost targets weren’t defined. The team optimized a part without understanding the system. Or everyone became so focused on solving the problem in front of them that nobody stopped to ask whether it was the right problem.
AI doesn’t make those issues disappear. In some ways, it can make them more important. If we can generate ten solutions in the time it once took to develop one, we still need to know which solution deserves to move forward. That requires engineering judgment.
What Is Engineering Judgment?
Engineering judgment isn’t just technical knowledge. It’s the ability to take everything you know, and everything you don’t know, and make a good decision within the context of the product.
It means asking questions like: Does this actually meet the requirement? What assumptions are we making? What happens when this goes into production? Can it be assembled? Can we inspect it? What does it cost? Where will it fail? What happens at tolerance extremes? Are we solving a part problem or a system problem?
And one of my favorites: Does this answer make sense?
AI can be extraordinarily useful in helping us investigate those questions. But the engineer still needs to know to ask them.
Context Is Becoming More Valuable, Not Less
As engineering tools become more powerful, I believe context becomes increasingly important. A CAD model doesn’t exist in isolation. Neither does an FEA result, a tolerance, a material selection, a supplier quote, or an AI-generated recommendation.
Every engineering decision exists inside a larger system of requirements, physics, manufacturing processes, cost, schedule, supply chain, customer needs, regulatory requirements, and business objectives.
That’s why at HatchOne we work from Define to Produce. Before committing to a solution, define the problem. Before designing the part, understand the physics. Before optimizing the geometry, understand how it will be manufactured. Before releasing the drawing, understand how someone will build and inspect it.
AI can accelerate every one of those activities. But acceleration without context can simply get you to the wrong answer faster.
The Engineer’s Role Is Changing
I don’t see AI replacing engineering judgment. I see it changing where engineers create value. The value of an engineer will increasingly be less about simply producing an output and more about understanding, evaluating, questioning, integrating, and deciding.
That means engineers need more than software proficiency. They need exposure to manufacturing. They need to understand cost. They need to communicate across disciplines. They need to understand how products move from an idea through design, engineering, prototyping and production. And they need enough fundamental understanding to recognize when a very convincing answer is wrong.
This is also one of the reasons I created The Engineering Exchange. HatchOne works with companies solving real engineering and product-development problems. The Engineering Exchange works from the other direction, helping engineers develop the context, practical skills, mentorship, and judgment that are difficult to learn from a textbook or software tutorial alone. AI makes that mission more relevant, not less.
Faster Engineering Isn’t the Goal
Better products are the goal. AI gives engineering teams an extraordinary new set of tools. Used well, those tools can help us explore more ideas, perform analysis sooner, communicate more effectively, automate low-value work, and make better-informed decisions. But speed by itself isn’t innovation.
The opportunity is to combine the speed and capability of AI with something experienced engineers have always brought to product development: Curiosity. Context. Skepticism. Fundamentals. And judgment.
AI can help us answer the question. Engineering judgment determines whether we asked the right one.
I’ll be carrying this conversation with me to the UK as I attend EDS Coventry. I’m looking forward to talking with engineers, designers, manufacturers, and technology companies about how AI is changing not just the tools we use, but the work of engineering itself.
If you’ll be at EDS Coventry, come find me! I’d love to hear what you’re seeing.