How to calculate AI automation ROI
How do you decide whether an AI project is worth doing before you build it?
Estimate the current cost of the process in hours and errors, subtract the cost to build and run the replacement, and apply an honest discount for adoption. If it is not clearly positive on conservative assumptions, do not build it yet.
Measure what exists first
How many hours a week does this consume, across everyone who touches it? What does an error cost when it happens, and how often does it happen? If you cannot answer these, the project is not ready to scope, and that is useful information rather than a failure.
Count the full cost of the replacement
Build cost is the visible part. Add model and infrastructure cost at realistic volume, the maintenance you will owe in year two, and the time your team spends on review where a human stays in the loop.
Discount for adoption
Assume partial adoption in the first quarter, not full. A system used by half the intended team returns roughly half the value. Modelling full adoption on day one is the most common way these estimates go wrong.
Value that is real but not in the model
Faster response to customers, fewer errors reaching clients, and work senior people stop doing are all genuine. Count them qualitatively and do not convert them into a fabricated number to make a business case look better.
The bottom line
Conservative assumptions, full lifetime cost, and a discount for adoption. If the case only works on optimistic inputs, it does not work.
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