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The exception rate stayed low. The human queue grew tenfold.

At a 1% exception rate:

10,000 cases need 100 reviews.

100,000 cases need 1,000 reviews.

Example: one review per exception, over the same period.

At the larger volume, the business needs capacity for 900 more reviews, before correction or recovery is counted.

The receiving team experiences hours and deadlines.

Sending work to a queue gives the receiving team more to do. At the higher volume, the same people may face older work and less time to judge each case.

Estimate how many hours the reviews need and when they arrive. Include other workflows using the same team and the surge that a shared source failure could cause.

More staff, better case context, fewer types of enquiry or a process change may make expansion workable. Plan the human response as part of the expansion.

Specify the responsibility being added.

More volume is one change. New actions, different customers, greater financial consequences, and less human confirmation are others.

Answering a payment-status question and correcting a payment require different abilities. Test the added responsibility, including difficult cases such as conflicting records.

Compare results, delays, unfinished work, recovery, human effort and total cost. Allow time for delayed results; a short test may miss uncommon failures.

Keep the option to reduce responsibility.

The business decides what responsibility to grant. Put the necessary controls and human support in place before AI takes on the extra work.

If sources become unreliable or the receiving team cannot keep up, reduce or pause the affected work. Accepted cases, pending confirmation, and recovery still need owners during that change.

Take this question into the next planning meeting: What is the absolute exception workload at the volume we intend to reach?

Then check whether the receiving team can handle that work in time.

See how shared capacity becomes an enterprise decision.