Fast AI suggestions can create more rework

An AI suggestion can arrive within seconds and still require more human work than the previous workflow. An automation decision depends on the work needed to reach an equivalent result. Model response time covers only part of that path.

Compare the same requests through to the same result

In this fictional calculation, 100 support requests are assigned to the correct specialist team and accepted for handling. Their underlying support problems have not necessarily been solved. Existing manual routing takes two minutes per request: 100 × 2 = 200 minutes.

In the AI workflow, the routing suggestion appears quickly. Each request receives one minute of review. Afterwards, 30 requests need four additional minutes each for correction. Reconciliation and handover add a single 20-minute task for the batch.

Correction time is additional to the review already counted. Both paths end with equivalently routed and accepted requests; the same work is not counted twice. The example produces 40 more minutes of human effort.

The bottleneck can follow the suggestion

If automation delivers new suggestions faster than the team completes them, the review queue grows. A quick initial response can coexist with a longer wait for usable routing. Waiting time and active handling time are different quantities.

The Google SRE Workbook recommends quantifying actual work before making changes. For this pilot, the unresolved requests and the age of the oldest should remain visible alongside active time. A low average model response time says little about them.

Correction is part of the software

The Guidelines for Human-AI Interaction include efficient correction. In the routing interface, staff should see the original request and suggestion together and be able to change an incorrect team directly. If they must copy data, open another system and reconstruct context, that work also belongs in the automation assessment.

The assessment can change when fewer corrections are needed or suggestions are already reliable enough for a low-risk purpose without individual review. The example does not establish a universal requirement to check every AI result manually. Required outcome quality must remain the same in the comparison.

A wider rollout therefore needs justification across the whole workflow. If only time to suggestion falls while correction effort and unresolved requests grow, the error and handover path needs improvement first. Response speed alone cannot support that decision.

Sources

• Google SRE Workbook: Identifying and Recovering from Overload

• Amershi et al.: Guidelines for Human-AI Interaction (CHI 2019)