Time Savings With Review Included

From minutes to money

Labour savings are volume times minutes saved per item, divided by 60, times the loaded rate. Using the task-level minutes (after review) instead of the naive "the AI does it all" assumption often cuts the estimate substantially. Present both numbers if stakeholders have heard the optimistic one, and explain the difference.

Savings that will actually show up

Realistic benefits account for review time, error effects and how freed time is used.

Naive versus task-level savings, run

I ran this with plain Python 3. All figures belong to one worked example (an invoice-processing team) with invented but internally consistent numbers; prices are placeholders. Assuming the whole 6.5 minutes disappears would claim $23,400 a month. Using the task-level 2.69 minutes remaining gives $13,716, so the naive claim is 1.7 times too high.

volume = 12000; rate = 18.0
before_min, after_min = 6.5, 2.69            # from the task decomposition
optimistic = volume * before_min / 60 * rate
realistic = volume * (before_min - after_min) / 60 * rate
print(f"naive claim 'automates the job'   : ${optimistic:,.0f} per month saved")
print(f"task-level estimate incl. review   : ${realistic:,.0f} per month saved")
print(f"overstatement of the naive claim   : {optimistic / realistic:.1f}x")

Output:

naive claim 'automates the job'   : $23,400 per month saved
task-level estimate incl. review   : $13,716 per month saved
overstatement of the naive claim   : 1.7x

Show the gap openly

Explaining why the realistic number is lower builds more trust than presenting only a big number.

त्वरित जाँच: Why is the task-level estimate lower than the naive one?

• Review and unautomated steps still take time
• Task-level estimates ignore volume
• Naive estimates count review twice
• Loaded rates are lower