Tasks went from 8 hours to 1, yet the sprint ran dry: how to measure AI productivity honestly
rseroter · x · 2026-10-28
The author's team shows the AI productivity paradox: an integration script dropped from 8 hours to 1, discovery from a week to 3 days — yet in Sprint 69 engineers finished four days early with almost nothing ready for the next sprint.
Industry data agrees: Workday's survey of 3,200 respondents found 85% save 1–7 hours/week, but nearly 40% of time savings are lost to rework and only 14% consistently get clear positive net outcomes. An NBER paper surveying 6,000 executives found 69% of firms use AI, with nine in ten reporting no measurable impact on employment or productivity.
Takeaway: measuring only task time overstates gains — measure the wait time of the overall process. The author proposes five numbers product teams can use to measure AI productivity without fooling themselves.
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