What you are probably seeing
The dashboard looks quiet because the automation runs without human input, but error logs are accumulating without anyone reviewing them.
The two-minute check-in is skipped regularly, allowing small failures to grow into broken workflows that no one notices until a customer complains.
Why it happens
Shifting tasks from doing to watching changes the failure mode from visible mistakes to silent errors that require active monitoring to catch.
Privacy-focused choices like Murena or self-hosting focus on data location but leave the operational duty of exception handling unchanged.
What changed recently
Step by step
1. Define a specific two-minute time block each day to review the automation exception queue.
2. Create a simple log sheet to record any exceptions found and their resolution time.
3. Assign a single person as the owner for the exception queue to prevent gaps in accountability.
4. Set a threshold for error frequency that triggers a review of the automation rules.
5. Test the monitoring process by intentionally introducing a small error to ensure it is caught and logged.
Ways to go further
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Other problems readers raise
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Why is my AI review backlog so hard to manage?