Estimating AI Costs and Returns Honestly
Build cost and benefit estimates with ranges and assumptions you can defend in a budget meeting
A taste of a lesson
If AI saves each of our 20 staff an hour a day, can I count that as 20 salaries' worth of hours saved?
Not yet. Three questions first. Is an hour a day measured, or a hope? Have you subtracted the time people spend checking AI output? And will the freed time be used for something valuable, or will it quietly disappear into the day? Saved minutes become money only when they avoid a hire, reduce overtime or go to work you can name. Try this: write the hour as a range, say 10 to 40 minutes, and tell me what the team would do with it.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Build a benefit model from volume, time, share helped and review effort
- List every running cost, including staff time and evaluation
- Present low, expected and high scenarios with a sensitivity check
- State a testable break even condition for a pilot to confirm
- Separate cash benefits from real but unpriced ones
Lesson plan
- 1 The decision behind the estimate Clarify what the estimate must decide and how precise it needs to be. Start
- 2 Building the benefit model Express benefits as a short chain of explicit, checkable assumptions. Start
- 3 Counting every cost List setup and running costs over at least two years. Start
- 4 Scenarios and sensitivity Show how the result changes when key assumptions move. Start
- 5 Break even and the pilot link Turn the estimate into a condition a pilot can confirm or reject. Start
- 6 Presenting it honestly Write a one page summary that shows numbers, assumptions and limits together. Start
Try asking
About this tutor
For managers, analysts and finance partners asked to justify an AI project with numbers. Rather than a single impressive return figure, you will learn to build an estimate from a small set of explicit assumptions: volume, time per task, share of work AI can help with, review time, error costs and full running costs. We practise ranges, sensitivity checks and break even thinking, and talk about benefits that are real but hard to price. Each lesson works on your own case. You leave with a spreadsheet structure, a short narrative explaining what the numbers depend on, and a plan to replace guesses with measured data during a pilot.
Reviews
4.0
3 ratingsSample
- Ravi M.Sample
Solid method, though slow going for someone who already does business cases daily. The part on unpriced benefits was the most useful bit for me.
- Oluwaseun B.Sample
The sensitivity check showed that one assumption, the share of tickets AI can help with, drove everything. Now that is the thing our pilot measures.
- Claire D.Sample
Clear and a bit humbling. My first estimate collapsed once review time went in. I would have liked a downloadable sheet, but building it myself helped.
About the teacher
Helps leaders choose AI work that pays for itself and drop the projects that do not
9 tutors 353 lessons taught Sample
I teach managers and executives how to make sound decisions about AI without needing to become engineers. My background is in strategy and operations work inside companies, where I spent a lot of time turning vague ambitions into projects with a budget, an owner and a way to tell if they worked. I teach with real decisions: a use case...
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