5.3 Industrial Applications The role of AI in Industrial Applications is yet to be fully comprehended and broadly adapted in companies as managers still struggle with identifying and providing the organizational, cultural and technology enablers (Chen, 2017; Johnson, 2019). Within this topic, we have found that papers report opportunities of AI Industrial Applications in several sectors: medical sciences (Jiang et al., 2017; Szolovits, 2019) and specifically either in diseases cure such as in cardiology (Johnson et al., 2018) and radiology (Hosny et al., 2018), in neuroscience (Hassabis et al., 2017), in preventing epidemic diffusion such as the recent COVID-19 as a tool to protect healthcare workers and curb dissemination (McCall, 2020); in the chemical industry (Venkatasubramanian, 2019) of pharmacy (Hessler and Baringhaus, 2018); in social sciences such as in politics (Hudson, 2019), in marketing (Kumar et. al., 2019), and in finance (Faccia et al., 2019). Furthermore, AI enables op

, 2010), in the definition of price strategies (Chou et al., 2015), in product development and scheduling (Metaxiotis and Psarras, 2003), in the management of services in markets and simulations (Li and Li, 2010) and finally web intelligence and e B2B commerce (Li, 2007; Zhong et al., 2007). Interesting opportunities might be activated by AI in integrating the financial accounting cycle (Faccia et al., 2019) and industrial marketing (Martnez-Lpez and Casillas, 2013).

This is the preprint version of the article published on February 7th, 2021 in Technology Analysis & Strategic Management. DOI: Taylor & Francis. Andrea Sestino & Andrea De Mauro (2021) Leveraging Artificial Intelligence in Business: Implications, Applications and Methods, Technology Analysis & Strategic Management, DOI: 10.1080/09537325.2021.1883583

Furthermore, our literature review made apparent that AI applications can highly benefit from modern IoT devices, in data collection, in the transmission of results deriving from AI algorithms, in supporting industrial applications by bringing AI into physical objects (Arsnio et al., 2014). The maximum contribution is thus shown by Industrial Internet of Things IIoT, when IoT is integrated into the production process with the result of precise data analysis and from connected equipment, operating technology, places, and people or providing smart devices in manufacturing (Vermesan et al., 2017). Data collection derived from IIoT is useful for AIbased analysis which can serve in turn the same devices from which it was collected. Therefore, when combined with operational technology monitoring devices, IIoT helps regulate and monitor industrial systems in an integrated manner, monitor events or changes in structural conditions, ensure cost savings, reduced time, better quality, and incre