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What you are probably seeing
Your team calculates time saved by automation but ignores the volume of late-night reviews caused by unhandled model exceptions. The queue of flagged errors grows faster than the developers can clear it.
You notice that automated tools confidently move data to version control, but the final step of resolving edge cases remains unowned. No single person is accountable for watching the queue and handling the breaks.
Why it happens
Most teams treat automation as a speed improvement rather than a change in responsibility. They focus on the technical integration of AI agents without defining who manages the failures.
The cost of automation is not the software but the human attention required to handle exceptions. When the exception handling process is not owned by a specific person, the burden falls on everyone.
What changed recently
Step by step
1. Identify a specific team member who will own the review of automated model exceptions.
2. Map out the critical dependencies and external links that the AI agent will interact with.
3. Define clear criteria for when the AI should pause and hand off to a human reviewer.
4. Create a documented workflow for the owner to handle the flagged items in the queue.
5. Test the new process with a small set of financial models before scaling it up.
Ways to go further
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