{"title":"Transitioning to artificial intelligence-based key account management: A critical assessment","authors":"Daniel D. Prior , Javier Marcos-Cuevas","doi":"10.1016/j.indmarman.2025.02.009","DOIUrl":null,"url":null,"abstract":"<div><div>Research suggests that Artificial intelligence (AI) use for sales and marketing activities improves firm performance. Underpinning these AI applications are datasets that reflect large volumes of sales transactions and interactions with a broad range of customers. Conversely, key account relationships involve deep and focused engagements with a small number of strategically important customers at multiple levels, and this has important implications for AI data inputs and uses. Whether AI is appropriate or relevant to key account management (KAM) is currently unclear. In this paper, we critically evaluate AI applications for KAM. The paper highlights the amenability of a firm’s KAM capabilities to AI and evaluates the opportunities and challenges that AI-based KAM offers. The paper also outlines a set of moderating factors likely to affect the impact of AI on KAM and provides a conceptual model to better understand the potentially transformative effects of AI on KAM. The paper concludes with a set of theoretical and managerial implications of AI-based KAM and develops a comprehensive research agenda to contribute to the further exploration of AI-based KAM.</div></div>","PeriodicalId":51345,"journal":{"name":"Industrial Marketing Management","volume":"126 ","pages":"Pages 72-84"},"PeriodicalIF":7.8000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial Marketing Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S001985012500029X","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Research suggests that Artificial intelligence (AI) use for sales and marketing activities improves firm performance. Underpinning these AI applications are datasets that reflect large volumes of sales transactions and interactions with a broad range of customers. Conversely, key account relationships involve deep and focused engagements with a small number of strategically important customers at multiple levels, and this has important implications for AI data inputs and uses. Whether AI is appropriate or relevant to key account management (KAM) is currently unclear. In this paper, we critically evaluate AI applications for KAM. The paper highlights the amenability of a firm’s KAM capabilities to AI and evaluates the opportunities and challenges that AI-based KAM offers. The paper also outlines a set of moderating factors likely to affect the impact of AI on KAM and provides a conceptual model to better understand the potentially transformative effects of AI on KAM. The paper concludes with a set of theoretical and managerial implications of AI-based KAM and develops a comprehensive research agenda to contribute to the further exploration of AI-based KAM.
期刊介绍:
Industrial Marketing Management delivers theoretical, empirical, and case-based research tailored to the requirements of marketing scholars and practitioners engaged in industrial and business-to-business markets. With an editorial review board comprising prominent international scholars and practitioners, the journal ensures a harmonious blend of theory and practical applications in all articles. Scholars from North America, Europe, Australia/New Zealand, Asia, and various global regions contribute the latest findings to enhance the effectiveness and efficiency of industrial markets. This holistic approach keeps readers informed with the most timely data and contemporary insights essential for informed marketing decisions and strategies in global industrial and business-to-business markets.