Guide
Measuring AI return without fooling yourself
Updated
The benefit half of an AI business case is usually weaker than the cost half, and it fails in a predictable way.
Saved time is not saved money
If a task takes twenty minutes instead of an hour and the person simply has an easier day, the business has spent money and gained nothing measurable. The saving becomes real when the time is redeployed to something valuable or when the headcount requirement genuinely changes.
Decide which of those two you are claiming, before the project, and say so out loud. Projects that skip this step cannot be evaluated afterwards.
Measure the before
You cannot show an improvement you never baselined. Time the task, count the errors, measure the cycle time, before anything is installed.
This takes days and it is the difference between a defensible result and an anecdote.
Beware the quality trap
Faster and worse is easy. Any benefit measure should be paired with a quality measure, or the project optimises the thing you counted at the expense of the thing you wanted.
Decide what a wrong output costs and how you would detect one, because that determines how much checking the process needs and therefore how much time is really saved.
Count internal time honestly
Your own people working on the project are not free. Cost their days at the same rate you would pay externally, or the business case is comparing a real number with an imaginary one.
This single correction reverses a surprising number of enthusiastic business cases.