Tomi Laamanen , Ann-Kristin Weiser , Georg von Krogh , William Ocasio
{"title":"人工智能在自适应策略创造和实施中的应用:增强策略过程中的注意控制","authors":"Tomi Laamanen , Ann-Kristin Weiser , Georg von Krogh , William Ocasio","doi":"10.1016/j.lrp.2025.102561","DOIUrl":null,"url":null,"abstract":"<div><div>This article focuses on the deployment of proprietary artificial intelligence (AI) systems in strategy creation and implementation processes, with a specific focus on their role in enhancing organizational attentional control. By employing the attention-based view (ABV) as an overarching theoretical framework, we examine how company-specific AI systems trained on proprietary data can support strategy processes. We identify three key contributions of AI in strategy creation and implementation processes: (1) broadening organizational attention to external and internal developments, (2) democratizing strategic processes through improved transparency and inclusivity, and (3) accelerating feedback loops with real-time monitoring of strategy implementation progress. Potential challenges associated with the deployment of AI systems for attentional control are also addressed. The paper concludes by putting forward potential directions for future research.</div></div>","PeriodicalId":18141,"journal":{"name":"Long Range Planning","volume":"58 4","pages":"Article 102561"},"PeriodicalIF":7.4000,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in adaptive strategy creation and implementation: Toward enhanced attentional control in strategy processes\",\"authors\":\"Tomi Laamanen , Ann-Kristin Weiser , Georg von Krogh , William Ocasio\",\"doi\":\"10.1016/j.lrp.2025.102561\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This article focuses on the deployment of proprietary artificial intelligence (AI) systems in strategy creation and implementation processes, with a specific focus on their role in enhancing organizational attentional control. By employing the attention-based view (ABV) as an overarching theoretical framework, we examine how company-specific AI systems trained on proprietary data can support strategy processes. We identify three key contributions of AI in strategy creation and implementation processes: (1) broadening organizational attention to external and internal developments, (2) democratizing strategic processes through improved transparency and inclusivity, and (3) accelerating feedback loops with real-time monitoring of strategy implementation progress. Potential challenges associated with the deployment of AI systems for attentional control are also addressed. The paper concludes by putting forward potential directions for future research.</div></div>\",\"PeriodicalId\":18141,\"journal\":{\"name\":\"Long Range Planning\",\"volume\":\"58 4\",\"pages\":\"Article 102561\"},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2025-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Long Range Planning\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0024630125000640\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Long Range Planning","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0024630125000640","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
Artificial intelligence in adaptive strategy creation and implementation: Toward enhanced attentional control in strategy processes
This article focuses on the deployment of proprietary artificial intelligence (AI) systems in strategy creation and implementation processes, with a specific focus on their role in enhancing organizational attentional control. By employing the attention-based view (ABV) as an overarching theoretical framework, we examine how company-specific AI systems trained on proprietary data can support strategy processes. We identify three key contributions of AI in strategy creation and implementation processes: (1) broadening organizational attention to external and internal developments, (2) democratizing strategic processes through improved transparency and inclusivity, and (3) accelerating feedback loops with real-time monitoring of strategy implementation progress. Potential challenges associated with the deployment of AI systems for attentional control are also addressed. The paper concludes by putting forward potential directions for future research.
期刊介绍:
Long Range Planning (LRP) is an internationally renowned journal specializing in the field of strategic management. Since its establishment in 1968, the journal has consistently published original research, garnering a strong reputation among academics. LRP actively encourages the submission of articles that involve empirical research and theoretical perspectives, including studies that provide critical assessments and analysis of the current state of knowledge in crucial strategic areas. The primary user base of LRP primarily comprises individuals from academic backgrounds, with the journal playing a dual role within this community. Firstly, it serves as a platform for the dissemination of research findings among academic researchers. Secondly, it serves as a channel for the transmission of ideas that can be effectively utilized in educational settings. The articles published in LRP cater to a diverse audience, including practicing managers and students in professional programs. While some articles may focus on practical applications, others may primarily target academic researchers. LRP adopts an inclusive approach to empirical research, accepting studies that draw on various methodologies such as primary survey data, archival data, case studies, and recognized approaches to data collection.