Quick fixes rarely fail. They accumulate and eventually make systems harder to understand and change.
Most teams don’t start using Airtable because they’re shopping for tools. They start using it because something needs to move faster, and the usual ways of doing it feel slow, heavy, or out of reach.
That impulse feels even stronger now. People are used to shaping their own workflows, fixing things as they go, and expecting systems to keep up. Automation and AI have only amplified that expectation. Airtable slips into that space easily, because it lets the people doing the work actually change how the work gets done.
At first, everything feels light. You make changes quickly, add fields when you need them, clean up small annoyances as they come up. The system bends with the work, and for a while that feels not just efficient, but right.
What’s harder to notice is when that feeling starts to shift. Nothing breaks, and there’s no clear moment where things go wrong. You just pause a little longer before making changes you would have edited without thinking a few months earlier. You double-check, hesitate, and find yourself working around the system instead of changing it directly.
That’s usually when the system has started carrying more weight than anyone ever meant it to.
It usually shows up in a Slack thread that was supposed to be quick.
Someone asks if it’s okay to change a field, or remove an automation that’s been there for a while. The kind of thing that should take five minutes. Instead, the conversation slows down.
“Wait — what else uses that?”
“I think there’s an automation tied to it.”
“Yeah, but I’m not totally sure what happens if we change it.”
So nobody does.
Not because anyone thinks it’s a bad idea, but because nobody wants to be the one who breaks something they don’t fully understand anymore... It feels safer to leave it alone than to find out what it’s actually holding together.
So the change doesn’t happen. Or it happens somewhere else:
A workaround gets added, another automation appears downstream, and the original one stays exactly where it is, not because it’s right, but because too many things now seem to rely on it.
At that point, an automation stops being a shortcut and starts becoming something people plan around.
Quick fixes don’t usually fail outright. They succeed quietly.
Each one solves a local problem, often under real pressure. Over time, though, they change how the system is understood. Logic moves out of shared structure and into scattered automations. Behavior becomes encoded in conditions instead of decisions.
The base still works, but fewer people can explain why it works the way it does. Understanding becomes partial. Confidence becomes uneven. The system becomes something you navigate carefully rather than shape deliberately.
Once quick fixes are in place, removing them feels riskier than adding new ones.
Undoing an automation means revisiting assumptions and asking questions no one is fully sure how to answer anymore. It means slowing down work that’s already moving.
So teams keep building on top of what exists, because it’s the least disruptive option available at the moment. Over time, the system accumulates history faster than clarity.
Quick fixes aren’t a sign that a team is doing something wrong. They’re a sign that a system is being asked to adapt faster than its design is being revisited.
When they start to pile up, it’s usually not a tooling issue. It’s a signal that the system has reached a point where continuing to patch it is easier than deciding what it should become.
That decision doesn’t need to be made immediately. But it does need to be made deliberately, because once a system is relied on this way, the cost of avoiding it only goes up.
That’s the moment worth paying attention to.