AI didn’t break our development process, it exposed it.
While building TrekCrumbs, things moved fast, almost uncomfortably fast. Features shipped. Bugs got fixed. Progress felt real.
Early on, we split our data models by type. It felt clean and flexible. Custom fields. Easy extensions. Quick wins.
And for a while, it worked.
Then complexity compounded.
Fields drifted across schemas. Validation logic duplicated. Small changes started breaking unrelated parts of the system. AI kept offering fixes: add a mapping layer, patch the UI, write converters.
All technically correct.
All increasingly expensive.
The problem wasn’t code quality.
It was architecture.
The only real fix was a hard reset. We stepped back, normalized the schemas, and clarified the end-to-end data flow. That week wasn’t about coding at all. It was about deciding what had to stay consistent, what could change, and where the boundaries really were.
Once those decisions were made, AI executed the refactor quickly and cleanly.
Here’s the uncomfortable truth:
The more powerful your implementation engine becomes, the more costly weak planning gets.
AI doesn’t shortcut thinking. It amplifies the consequences of skipping it. When execution was slow, you could afford to figure things out as you went. When execution is fast, ambiguity compounds before you notice.
That’s the shift many leaders miss.
AI didn’t remove the need for planning.
It turned planning into the constraint.
Leadership takeaway
When execution is fast and cheap, planning quality becomes the constraint. Leadership responsibility doesn’t disappear — it concentrates.
Action cues
- Notice where decisions were never clearly made, but still had consequences
- Pay attention to refactors driven by assumptions, not defects
- Watch for momentum masking unresolved intent
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