Testing AI Productivity Claims
Design measurements that show whether AI really made work faster or better, and by how much
A taste of a lesson
Our AI tool's dashboard says it saved 5,000 hours last quarter. Can I put that in my report?
Ask how the number is calculated first. Many dashboards multiply the count of uses by an assumed number of minutes per use. That measures activity, not saved time: it ignores review effort, uses that did not help, and whether the time went anywhere useful. Treat it as an upper bound at best. Better evidence would be cycle time or output per person compared with a baseline period or a team not using the tool, plus a quality sample. Exercise: find out what assumption sits behind one hour on that dashboard.
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
- Define productivity for a specific type of work, including a quality standard
- Rank evidence from self reports to randomised designs by strength
- Identify bias, novelty effects and gaming in a productivity claim
- Design a staggered rollout or matched comparison with blinded quality review
- Report effects as honest ranges with clear limits
Lesson plan
- 1 Defining the claim Restate a productivity claim as a specific, testable statement about output and quality. Start
- 2 Measures that resist gaming Choose a balanced set of throughput, time and quality measures. Start
- 3 The evidence ladder Compare self reports, before and after, matched comparisons, staggered rollouts and randomised designs. Start
- 4 Traps and biases Spot the common reasons productivity results look better than reality. Start
- 5 Designing a real measurement Write a measurement plan for an actual AI rollout. Start
- 6 Reading and reporting results Interpret results with ranges, breakdowns and stated limits. Start
Try asking
About this tutor
For analysts, operations leads, people analytics teams and managers who are asked to prove, or check, claims that AI made people more productive. Self reported surveys and vendor dashboards are easy to collect and easy to misread. You will learn to define productivity for a specific kind of work, choose output and quality measures that resist gaming, design comparisons that rule out obvious alternative explanations, and read results with appropriate humility. We cover time studies, matched comparisons, staggered rollouts, quality sampling and the classic traps: novelty effects, selection bias, survivorship, and counting speed while quality quietly drops. You finish with a measurement plan for a real rollout in your organisation.
Reviews
4.3
4 ratingsSample
- Ana P.Sample
Finally someone who explains staggered rollouts without a statistics lecture. Our next rollout is designed this way and the analytics team is on board.
- Mei C.Sample
I used the dashboard question at work and found the vendor counted every click as six minutes saved. Very useful tutor.
- Jonas E.Sample
Strong content, but my company will never allow comparison groups, and I wanted more help with weaker designs. It did cover limits honestly.
- Tobias L.Sample
Rigorous and fair. The lesson on selection bias explained why our first pilot looked so good. Some sessions ran dense, so take notes.
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...
See Sofia's profile and tutorsMore like this
Other tutors on the same or nearby topics.