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This study analyzes a staggered rollout of a generative AI conversational assistant to 5,179 customer support agents. Tool access raises productivity by 14% on average, with larger gains for novice and low-skill workers. It also improves customer sentiment, supports employee retention, and offers suggestive evidence of worker learning.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/generative-ai-at-work-working-paper-31161-research-findings/296699/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/generative-ai-at-work-working-paper-31161-research-findings/296699.png","ImageObject",300,407,{"name":92,"@type":93},"Gelato","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-08","2026-09-18",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What workplace change does the study focus on?","Question",{"text":113,"@type":114},"It examines the staggered deployment of a generative AI-based conversational assistant for customer support agents.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does AI access affect productivity?",{"text":118,"@type":114},"Access increases productivity, measured as issues resolved per hour, by 14% on average.",{"name":120,"@type":111,"acceptedAnswer":121},"Are the benefits uniform across workers?",{"text":122,"@type":114},"No. The study finds minimal impact on experienced and highly skilled workers, with stronger improvements for novice and low-skilled agents.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},296699,1789735713,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145},19241457091524,"https://us-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","NBER WORKING PAPER SERIES  \nGENERATIVE AI AT WORK  \nErik Brynjolfsson  \nDanielle Li  \nLindsey R. Raymond  \nWorking Paper 31161  \n[http://www.nber.org/papers/w31161](http://www.nber.org/papers/w31161)  \nNATIONAL BUREAU OF ECONOMIC RESEARCH  \n1050 Massachusetts Avenue  \nCambridge, MA 02138  \nApril 2023, revised November 2023  \nWe are grateful to Daron Acemoglu, David Autor, Amittai Axelrod, Eleanor Dillon, Zayd Enam, Luis Garicano, Alex Frankel, Sam Manning, Sendhil Mullainathan, Emma Pierson, Scott Stern, Ashesh Rambachan, John Van Reenen, Raffaella Sadun, Kathryn Shaw, Christopher Stanton, Sebastian Thrun, and various seminar participants for helpful comments and suggestions. We thank Max Feng for providing excellent research assistance and the Stanford Digital Economy Lab for funding. The content is solely the responsibility of the authors and does not necessarily represent the official views of Stanford University, MIT, or the National Bureau of Economic Research.  \nAt least one co-author has disclosed additional relationships of potential relevance for this research. Further information is available online at [http://www.nber.org/papers/w31161](http://www.nber.org/papers/w31161)  \n[NBER working papers are circulated for discussion and comment purposes. They have not been](NBER working papers are circulated for discussion and comment purposes. They have not been)[ ](NBER working papers are circulated for discussion and comment purposes. They have not been)[peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies](peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies)[ ](peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies)[official NBER publications.](official NBER publications.)  \n© 2023 by Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.  \nGenerative AI at Work  \nErik Brynjolfsson, Danielle Li, and Lindsey R. Raymond NBER Working Paper No. 31161  \nApril 2023, revised November 2023 JEL No. D8,J24,M15,M51,O33  \nABSTRACT  \nNew AI tools have the potential to change the way workers perform and learn, but little is known about their impacts on the job. In this paper, we study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents. Access to the tool increases productivity, as measured by issues resolved per hour, by 14% on average, including a 34% improvement for novice and low-skilled workers but with minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve. In addition, we find that AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning. Our results suggest that access to generative AI can increase productivity, with large heterogeneity in effects across workers.  \nErik Brynjolfsson  \nStanford Digital Economy Laboratory 353 Jane Stanford Way, Office 136 Stanford, CA 94305  \nand NBER [erik.brynjolfsson@gmail.com](erik.brynjolfsson@gmail.com)  \nDanielle Li  \nMIT Sloan School of Management 100 Main St, E62-484  \nCambridge, MA 02142 and NBER[d_li@mit.edu](d_li@mit.edu)  \nLindsey R. Raymond  \nMIT Sloan School of Management 100 Main Street  \nE62-489  \nCambridge, MA 02142  \n[lindsey.r.raymond@gmail.com](lindsey.r.raymond@gmail.com)  \nThe emergence of generative artificial intelligence (AI) has attracted significant attention, but few studies have examined its economic impact. While various generative AI tools have performed well in laboratory settings, excitement about their potential has been tempered by concerns that these tools may be less effective in real-w","cbCaisa4Os6uF6wr","https://ap.wps.com/l/cbCaisa4Os6uF6wr","pdf",1003328,67,"English","# Abstract\n## Productivity effects of generative AI assistance\n## Heterogeneity by worker experience and skill\n## Impacts on customer sentiment and retention\n## Evidence on learning and best-practice dissemination","[{\"question\":\"What workplace change does the study focus on?\",\"answer\":\"It examines the staggered deployment of a generative AI-based conversational assistant for customer support agents.\"},{\"question\":\"How does AI access affect productivity?\",\"answer\":\"Access increases productivity, measured as issues resolved per hour, by 14% on average.\"},{\"question\":\"Are the benefits uniform across workers?\",\"answer\":\"No. The study finds minimal impact on experienced and highly skilled workers, with stronger improvements for novice and low-skilled agents.\"}]","Generative AI at Work - Working Paper 31161 - Research findings | PDF",169]