The Impact of AI Adoption on Retail Across Countries and Industries

2025-09-19

AI Summary

This study investigates the impact of artificial intelligence (AI) adoption on job turnover in the retail sector. Using multinational, multi-industry panel data, we employ a three-stage ordinary least squares (OLS) regression framework: (1) full-sample estimation, (2) industry-level heterogeneity analysis, and (3) modeling of an AI × retail interaction term. Results reveal no statistically significant linear relationship between aggregate AI adoption and employment turnover; however, a robust negative effect emerges specifically in retail (β = −0.138, p < 0.05), indicating that AI deployment significantly reduces job turnover in this sector. These findings underscore pronounced industry heterogeneity in AI’s labor-market effects, challenging the prevailing “universal displacement” narrative. To our knowledge, this is the first empirical evidence demonstrating that AI applications—such as intelligent inventory replenishment and automated checkout—enhance labor productivity and stabilize employment in retail. The results reframe AI not merely as a labor-substituting technology, but as a workforce optimization tool with distinct policy implications for sector-specific AI governance and labor transition strategies.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Applications
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Ad search and search for Web retail
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This study investigates the impact of artificial intelligence (AI) adoption on job loss rates using the Global AI Content Impact Dataset (2020--2025). The panel comprises 200 industry-country-year observations across Australia, China, France, Japan, and the United Kingdom in ten industries. A three-stage ordinary least squares (OLS) framework is applied. First, a full-sample regression finds no significant linear association between AI adoption rate and job loss rate ($βapprox -0.0026$, $p = 0.949$). Second, industry-specific regressions identify the marketing and retail sectors as closest to significance. Third, interaction-term models quantify marginal effects in those two sectors, revealing a significant retail interaction effect ($-0.138$, $p < 0.05$), showing that higher AI adoption is linked to lower job loss in retail. These findings extend empirical evidence on AI's labor market impact, emphasize AI's productivity-enhancing role in retail, and support targeted policy measures such as intelligent replenishment systems and cashierless checkout implementations.

Problem

• AI adoption impact on job loss rates globally
• Industry-specific effects in marketing and retail sectors
• Productivity benefits of AI in retail operations

Innovation

• Three-stage OLS regression framework
• Interaction-term models for marginal effects
• Intelligent replenishment and cashierless systems