Generative Artificial Intelligence and Sales Productivity: Field Experiments in Online Retail

Abstract

We quantify the short-term impact of generative artificial intelligence (GenAI) on sales performance through a series of large-scale randomized field experiments involving millions of users and products at a leading crossborder online retail platform. Over 2023–2024, the platform integrated GenAI into seven business workflows spanning customer service, consumer-product matching, advertising, and seller services. We find that GenAI adoption increases sales in most workflows, with effects ranging from no detectable impact to 16.3% depending on GenAI’s marginal contribution relative to baseline firm practices. Across the four GenAI applications with positive sales effects, the implied annual incremental value is roughly $5 per consumer—an economically meaningful impact given the retailer’s scale and the early stage of GenAI adoption. The gains operate primarily through higher conversion rates rather than larger cart values, consistent with GenAI improving the shopping experience by reducing search, information, communication, and personalization frictions. Importantly, these effects are not associated with worse postpurchase outcomes as product return rates and customer ratings do not deteriorate. Finally, we document substantial demand-side heterogeneity, with larger gains for less experienced consumers. Our findings provide novel, large-scale causal evidence on how GenAI shapes sales productivity in online retail, highlighting both its immediate value and broader potential. This paper was accepted by Jean-Pierre Dube, marketing. Funding: L. Fang acknowledges financial support from the National Natural Science Foundation of China [Grants 72501258 and 72192803], the Major Special Project in Philosophy and Social Sciences of the Ministry of Education of China [Grant 2026JZDZ020], the National Social Science Fund of China [Grant 22&ZD081]. Z. Yuan acknowledges financial support from the National Key Research and Development Project [Grant 2024YFB3312900], the National Natural Science Foundation of China [Grants 72141305 and 72203202], the National Social Science Fund of China [Grants 26ZDA030 and 25&ZD149], and the Fundamental Research Funds for the Central Universities. K. Zhang, D. Donati, and M. Sarvary have no funding to report. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2026.00623 .

The authors' abstract, as published at the source. Management Science, 2026 · DOI ↗