How to identify Over Engineering
You will find one of the clearest signs of over-engineering in the words on the screen.
The labels, buttons, and error messages in an application reveal whether the system was built for the developer or for the person using it. When the language is wrong, users pay what I call a linguistic tax: the extra cognitive effort required to constantly translate machine logic into human intent every time they interact with the software.
Three examples I keep running into:
The “Delete” lie: Users almost never want to delete an order. They want to cancel it, or archive it, or reverse it. However, the button says “Delete” because that’s the underlying database action. Even then, nothing is deleted, and only a soft delete happens - the record gets flagged, not removed. So, the label is wrong in both directions: it doesn’t describe what the user wants, and it doesn’t describe what the system actually does. The user clicks “Delete,” expects the record to disappear, and then finds it still sitting in a report three days later. That confusion was designed into the system.
“Load” vs. “Show more”: “Load more items” is a description of what the database is doing. The user doesn’t care about data retrieval. They only want to see more items, and “Show more” captures their intent properly. It’s a small difference in phrasing and a large difference in who the software is built for.
Abstract product names: “Azure” is the color of a cloudless sky, which is an odd name for a cloud computing platform. “GraphQL” sounds like it involves graphs to anyone who hasn’t already learned otherwise. These names create an entry barrier by signaling that the product was named by the people who built it, not the people who need it.
All of these intentional design decisions accumulate and add a small friction cost that compounds across thousands of interactions. The linguistic friction leads to a state where users are "drowning in information while starving for wisdom.” When your users have to mentally translate your interface before they can use it, you’ve built something that works against them, instead of for them.
Why Over Engineering Happens
Over-engineering starts with a simple mistake: the sequence gets reversed. Teams start with the tool instead of the problem, and everything that follows gets shaped around the wrong starting point.
Tools come first, needs come second: Developers reach for frameworks they want to use, not what the problem actually calls for. The right sequence is Need first, then Problem, then Solution and Tools come last. When that sequence flips, the software ends up built around the technology, not around the person who has to use it every day.
Elaborate architecture for simple problems: Teams build complex systems when a much simpler solution would have served the need just as well. The architecture keeps growing because building something impressive is easier to justify than admitting a simple solution would work.
Speed gets measured, direction does not: Teams track velocity with lines of code per hour and story points per sprint. But nobody measures whether the team is building the right thing. You can be moving fast and heading the wrong way, and the metrics will not tell you.
How do you fix Over Engineering?
Kathy Sierra (famous game developer and programmer) had a principle I think about constantly: Our job is to make the user feel like a "badass”.
The product is just a tool. If the tool makes the user feel confused, slow, or dependent on a manual, you’ve failed, regardless of how sophisticated the underlying code is.
Over-engineering flips this principle. It makes the developer the hero of the story: look at this elegant abstraction, look at this architecture built for ten thousand concurrent users we don’t have, look at how many patterns I implemented. All of this is invisible to the users. The user only sees a button that says “Delete” when they mean “Cancel.”
The best systems are the ones where the user accomplishes what they need without thinking about the system at all. Technology gets out of the way, and humans are able to work without any friction and translation.
Final Words
Every unnecessary abstraction layer, every machine-facing label, every architecture decision made for technical elegance rather than user outcome are all barriers between the person and their goal. As mentioned earlier, they’re the engine revving in the driveway.
If your team is building something that users need a manual to understand, the problem probably isn’t training. It’s a design. Improving’s product delivery and UX team works with engineering organizations to close exactly this gap, starting from user intent and working backward to the architecture, not the other way around.
For any comments or suggestions on this article, find me on LinkedIn.
