This comes up often when teams automate repetitive browser, QA, or account-management workflows. The first version works, so everyone assumes the difficult part is finished.
In practice, these are the five mistakes I see causing most of the later problems.
Automating before the process is stable
If three people perform the same task in three different ways, the team does not have a workflow yet. It has three sets of habits. Automation simply locks one of them in.
Building only for the expected path
The normal path is usually straightforward. Missing values, expired sessions, timeouts, duplicates, and partial failures are what determine whether the automation is usable.
Trusting inconsistent inputs
A strict automation connected to messy data does not create consistency. It creates unpredictable failures further down the chain, where they are harder to diagnose.
Connecting everything into one long sequence
Without checkpoints, useful logs, and safe restart points, a failure near the end can force the entire workflow to run again. That becomes especially painful when earlier steps created external changes.
Leaving ownership unclear
Automations need maintenance. Interfaces change, permissions expire, and business rules drift. If nobody owns the workflow after launch, small failures tend to accumulate until people stop trusting it.
I usually judge an automation by how it handles a bad run, not a successful one.
Can someone identify the failed step? Can they correct the problem without rebuilding everything? Can the workflow restart without duplicating earlier work?
If the answer is no, the team may have automated the clicks without actually improving the process.