Managing artificial intelligence across functions for enhanced retail firm performance
研究了零售企业如何通过跨职能整合人工智能应用来提升绩效，发现客户服务和网络安全等职能的协同组合能带来效率与创新的双重提升。
Abstract Firms are increasingly deploying AI across business functions, yet the core challenge lies not in adoption itself, but in integrating these applications to achieve coherent, firm‐level outcomes. Despite growing interest, prior research has largely overlooked the dynamic interdependencies, unpredictable interactions, and organization‐wide effects that shape how AI‐enabled functions collectively drive performance. Addressing this gap, we draw on a business process perspective and dynamic capability theory to develop a multi‐level framework of AI integration at the task, function, and firm levels. Using a sequential mixed‐methods design, we first construct a validated measurement instrument based on grounded analysis of 6,519 AI‐related documents from 37 retailers. We then apply fuzzy‐set qualitative comparative analysis (fsQCA) to survey data from 140 executives to identify configurations of AI‐enabled functions associated with efficiency, innovation, or both. The results show that superior performance stems not from isolated AI uses but from synergistic combinations—particularly those involving customer service and cybersecurity—interacting with other functions. These configurations appear to give rise to emergent capabilities such as adaptive learning, predictive analytics, and uncertainty mitigation, enabling firms to reconcile exploitation and exploration. This study offers a dynamic, process‐based view of AI capability and provides strategic guidance for designing ambidextrous AI portfolios in retail.