mlsanityData to 14 Oct 2026

Shifting Work Patterns with Generative AI

arXiv 2504.11436 ↗First posted 15 Apr 2025econ.GN cs.LG q-fin.ECcited by 5

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Abstract (arXiv)

We present evidence from a field experiment across 66 firms and 7,137 knowledge workers. Workers were randomly selected to access a generative AI tool integrated into applications they already used at work for email, meetings, and writing. In the second half of the 6-month experiment, the 80% of treated workers who used this tool spent two fewer hours on email each week and reduced their time working outside of regular hours. Apart from these individual time savings, we do not detect shifts in the quantity or composition of workers' tasks resulting from individual-level AI provision.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 2 of its 19 references that are in the map. First posted on arXiv 15 Apr 2025.

Builds on

2 of 19 references.

References · 2

  1. 2022A Test for Evaluating Performance in Human-Computer Systemscited by 8
  2. 2017Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in Indiacited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.