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Top1. Introduction
Digital transformation has been among the key topics on the strategic agendas of many companies in the past years, including firms from a variety of sectors, such as consumer goods, manufacturing industries, pharmaceuticals, and the service sector (Amit and Han 2017; Andriole 2017; Belkeziz and Jarir 2020; Deena et al. 2020; Ertl et al. 2020; Loey et al. 2020; Sangwan and Bhatnagar 2020). As a consequence of the significant managerial attention, many companies have started particular strategic initiatives and programs to transform their organizations and to increase the digitalization of their business activities, for example with regard to specific data management strategies for their established businesses (Bughin 2017; Dellermann et al. 2017). While some of these initiatives have focused on enhancing the efficiency of existing processes, some others have been directed at generating new revenues, for example by commercializing digital services (Iansiti and Lakhani 2014; Mancha et al. 2020). On this basis, some firms have achieved their digitalization objectives. In contrast, many others have failed to profit from their initiatives due to substantial implementation challenges (Aikhuele 2018; Remane et al. 2017; Ross et al. 2017).
Now, many firms increasingly rely on artificial intelligence (AI) to complement or to replace their existing activities of digital transformation (Brock and Wangenheim 2019; Garbuio and Lin 2019). For example, these companies comprise well-known multinationals, such as General Electric, Microsoft, Tesla, Amazon, and Apple (Lichtenthaler 2018b). By exploring a wide variety of smart algorithms and advanced analytics tools, these firms attempt to leverage some of the results of their digital transformation initiatives, for example with regard to a better availability of detailed data about their business operations (Haenlein and Kaplan 2019; Kumar et al. 2019). In this regard, the companies rely on many different AI technologies, including speech synthesis, machine learning, and natural language processing, and many other AI algorithms (Finlay 2017; Millstein 2018; Mueller and Massaron 2018). Similar to the digital transformation activities, many AI initiatives focus on the optimization of existing business processes rather than generating new revenues based on particular AI applications. Thus, some of the AI initiatives are strongly related to previous digital transformation programs, which focused on an enhanced data management. In contrast, some other AI activities largely constitute stand-alone projects (Metcalf et al. 2019; Overgoor et al. 2019).
Despite the enormous attention that digitalization and AI have received, however, there is a clear gap in the literature about these topics. Specifically, our knowledge about AI and data management initiatives is surprisingly limited, especially concerning the combination of these strategic programs (Lichtenthaler 2020c; Tambe et al. 2019). Therefore, this conceptual paper attempts to bridge this gap in the literature by developing a conceptual framework for the combination of data management and AI, and it addresses the following question. How do data management and AI initiatives, individually and jointly, contribute to a firm’s digital transformation with regard to ensuring digital readiness and realizing new business opportunities? As such, this paper offers several contributions.