Shifting Work Patterns with Generative AI

Fuente: arXiv
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Autores principales: Dillon, Eleanor Wiske, Jaffe, Sonia, Immorlica, Nicole, Stanton, Christopher T.
Formato: Preprint
Publicado: 2025
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author Dillon, Eleanor Wiske
Jaffe, Sonia
Immorlica, Nicole
Stanton, Christopher T.
author_facet Dillon, Eleanor Wiske
Jaffe, Sonia
Immorlica, Nicole
Stanton, Christopher T.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2504_11436
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Shifting Work Patterns with Generative AI
Dillon, Eleanor Wiske
Jaffe, Sonia
Immorlica, Nicole
Stanton, Christopher T.
General Economics
Economics
Machine Learning
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.
title Shifting Work Patterns with Generative AI
topic General Economics
Economics
Machine Learning
url https://arxiv.org/abs/2504.11436