Estimation question

How much time is spent waiting per day for a real human in the Automated Systems in the US?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Complex multi-domain estimation requiring the candidate to define scope and decompose an ambiguous, broad prompt into countable categories.

Interpret the ambiguous phrase as total daily hours Americans spend waiting for automated systems to hand off to a human, e.g. phone support holds, chatbot escalations, kiosk assistance waits.
Pick the two or three largest categories to estimate rather than trying to cover every automated system: customer service hold times and self-checkout assistance waits.
Estimate customer service: number of US adults contacting support per day times average hold time.
Estimate self-checkout: number of self-checkout transactions per day times the fraction needing staff help times average wait per incident.
Sum the categories into total person-hours per day and sanity-check the order of magnitude.

Explicitly scopes an intentionally vague prompt to two or three concrete, estimable categories rather than trying to be exhaustive.
Uses labeled, round assumptions at each step so the logic is auditable.
States the final number as an order of magnitude, millions of person-hours per day, with a caveat that scope choice drives the answer.

Trying to enumerate every possible automated system and never converging on a number.
Not stating which categories were included versus excluded, making the final number undefendable.

Which category do you think dominates the total, and why?
How would reducing this wait time create business value for a company like Amazon or Google?