In today’s fast moving construction and design world, automation is everywhere. It promises faster results, reduced manual tasks, and potential cost savings, and in many ways, it delivers. Whether it’s converting 2D drawings into 3D models or auto, tracing old PDF or CAD plans, automated tools can make things easier.
But is faster always better? And does it mean more accuracy for the Industry?
At Modelo Tech Studio, we’ve seen both sides of the coin. Automation can be a helpful tool, but when it’s treated as a replacement for trained human eyes, things can go sideways fast, specially in something as nuanced and detail heavy skill sets as BIM and 3D modelling.
The Upside of Automation (Yes, It Has One)
Let’s give credit where it’s due! Automation isn’t the enemy. When used thoughtfully, it can seriously improve workflows:
Speed: Automated tracing or model generation can reduce turnaround time during virtual construction.
Repetitive Task Relief: It’s advantageous for handling the boring bits and pieces that don’t require a lot of thinking.
Scalability: Large volumes of data or drawings? No problem, automation can chew through them quickly.
Used well, these tools can be powerful allies, but there’s a caveat: speed and scale don’t mean much if the output needs redoing.
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Dynamo script extracting excavation volume based on a 3D model and existing scanned-to-bim topography
Why Construction Isn’t Like Manufacturing
Automation works beautifully in manufacturing. That’s because it’s a world built on repeatability: same parts, same process, same results, every time and, under continued and permanent controlled environments.
Construction? Not so much.
Every project is unique, every drawing set comes with quirks. Even two buildings with the same footprint can vary dramatically in terms of structure, sequencing, or intent.
That’s the challenge: you’re applying a standardized tool to a field full of exceptions. And machines, no matter how clever, aren’t great with exceptions. They follow patterns, they don’t ask questions, they don’t interpret context, and they definitely don’t make judgment calls.
Dynamo script placing rebars in concrete
Where Things Start to Break
This is where we often get called in, after an automated process has churned out a model that looks okay… until it doesn’t.
No Context: A machine doesn’t know that a wall slicing through a window is a problem, it’s just connecting lines.
No Architectural Intent: It won’t question whether a slab overlapping a stairwell makes sense, or whether a mechanical room is actually accessible.
Replicates Bad Inputs: If the source files are messy or outdated, automation won’t clean them up, it’ll just copy the mess.
Surface, Level Accuracy: Models might look clean at a glance, but often come loaded with incorrect levels, misalignments, or phantom geometry.
And with AI entering the mix, these issues are evolving. AI can do more, but it still depends on pattern recognition. It’s getting smarter, sure, but it’s not thoughtful. It doesn’t understand the model, and it can’t step back and ask, “Does this make sense in the real world?”
That’s where people still come in.
Humans Aren’t Perfect, But They’re Better at Judging
Let’s be real: people make mistakes too. Nobody’s flawless.
But here’s the difference, people can reflect. They can spot inconsistencies, think critically, and course correct. Judgment is something humans develop over time. Machines can replicate actions, but they don’t learn the way we do. They don’t improve from experience in the same way. And they don’t carry accountability.
We’ve seen countless models that were generated quickly, only to fall apart in the next phase of the project. Then comes the backtracking: rework, delays, frustration, things that could’ve been avoided with a bit more care and experience upfront.
Why We Still Trust the Human Eye
A seasoned modeller isn’t just checking for mistakes, they’re building with intent. They’re constantly asking:
Does this make sense structurally?
Is this detail constructible?
How will this hold up during coordination?
What’s the logic behind this design choice?
And when the source drawings are vague or conflicting (which happens often), humans are the ones who can fill in the blanks, not by guessing, but by applying their experience and understanding of how things are built.
Dynamo script placing clamps on unistruts holding conduits
Bottom Line: It’s About Balance
We’re not anti-automation. We use it ourselves, but we see it as just one piece of a larger puzzle. Tools are only as good as the people using them, and when it comes to BIM modelling, “close enough” just isn’t good enough.
If you want models that hold up during coordination, clash detection, and real world construction, you need more than automation. You need insight, judgment and experience.
That’s what we bring at Modelo Tech Studio. Because the best models don’t just look good on screen, they make things easier in the field. And that starts with having the right people on the job.
Curious to dive deeper into BIM practices? Check out our related blog posts for more insights and strategies to elevate your projects!
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