Price the AI mistake before you count the savings

Every AI business case starts with time saved. Ten minutes down to two. Six hours a day handed back to the team. The number is big, the slide looks good, and the project gets approved.

Then the first wrong answer goes to a customer, and someone spends an afternoon fixing it. That afternoon was never in the business case.

The savings are easy to count because they happen every time. The mistakes are easy to forget because they happen sometimes. Both belong in the same sum.

Errors are a cost, not an exception

A human doing this task makes mistakes too. The question is not whether the AI is perfect. It is whether its mistakes, added up, cost more than the time it saves.

That needs five numbers. You can get all four in an afternoon.

The five numbers

Volume. How many times does this task happen in a month?
Time today. How long does one take a person, start to finish?
Time with AI. How long does one take when the AI drafts and a person checks? Count the checking.
Cost of a miss. How long does it take to notice and fix one wrong result, including the rework and any apology?
How often it is wrong. Take it from a test on your own past cases, not from a vendor’s claim. If you have not run one, test the tool on your own work first.

Worked through, with round numbers

Suppose a team handles 600 requests a month. Each takes 10 minutes today. With AI drafting and a person checking, each takes 2 minutes.

Today: 600 × 10 = 6,000 minutes, which is 100 hours.
With AI: 600 × 2 = 1,200 minutes, which is 20 hours.
Time saved before mistakes: 80 hours.

Now the mistakes. Say 3 in every 100 drafts get through the check with an error. That is 18 a month. Say each one takes an hour to find and fix.

Cost of mistakes: 18 × 1 hour = 18 hours.
Net saving: 80 − 18 = 62 hours a month.

Still a good project. Now change one thing. If a miss costs a full day, 8 hours, the mistakes cost 18 × 8 = 144 hours, and the workflow loses 64 hours a month. Nothing else changed. Only the price of a miss did.

These numbers are examples. Replace every one with your own.

Find the break-even and stress it

Do the sum a second way: how expensive can a miss get before the project stops paying? Here, the saving of 80 hours divided by 18 misses is about 4.4 hours each. Any miss that costs more than that turns the project negative.

Then ask whether your worst realistic miss is above or below that line. If a wrong output can lose a client, it is far above it, and that action should never run on its own. If it costs a quick correction, it is far below.

Let the price of a miss set the design

This one number tells you where to put people, and where not to.

Cheap misses (a wrong tag, an internal note): let the AI act, and sample a slice for quality.
Costly misses (anything a customer sees, anything that moves money): have a person approve first, and make the approval screen easy to read.
Ruinous misses (legal, safety, large payments): keep a person as the decision-maker, and let the AI prepare only.

That is the same logic behind where to put the human in an AI workflow, now with a number attached.

Re-run it after the first month

The error rate you estimated is a guess. After a month of real use, count the misses, count the time they took, and redo the sum. If the net has dropped, you know which number moved.

A project that survives its own mistakes is one you can trust. One that only works when nothing goes wrong is a demo.

If you would like the sum done properly on one of your workflows, a Process / ROI Assessment measures the current process and gives a verdict before you build anything.