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The use of machine learning (ML) technologies by companies offers new and exciting opportunities but also poses significant challenges in the design of business models (BM) for value creation and capture. Research that advances the theoretical and empirical development of the value of ML technologies in the enterprise will guide organisations in their investment decisions and subsequent actions in developing and deploying ML technology to create value.
ML is becoming a key technology in digital transformations aimed at boosting productivity and fostering discovery in many industries (Jain, Padmanabhan, Pavlou, & Santanamd. 2018, p. 250). BM challenges and digitisation activities should be analysed in future research to highlight differences across industries (Reim, Yli-Viitala, Arrasvuori, & Parida, 2022, p. 8).). Studies such as the one by Sharma, K., Anand, D., Mishra, K. K., & Harit, S. (2022) analyse the leading role of artificial intelligence (AI) and ML technologies in the so-called “fourth industrial revolution”, or Business 4.0, which is being developed recently. Business 4.0 is characterised by the integration of online technology in the industry at possible levels thanks to the benefits of the interconnectivity that the Internet of Things (IoT) allows, massive access to data and the development of cyber-physical frameworks (Sharma et al., 2022).It is common to see ML technologies used by the world’s most profitable companies, such as Amazon, Apple, Google and Facebook, to personalise their offerings (Al Dakhil & Bayoumi, 2020).
The growth in the use of ML to support or automate organisational processes has been driven by the wide availability of data, extensive advances in telecommunications technologies, the democratisation of mobile technologies, and advances on the Internet of Things (IoT) (Dwivedi, Kumar, & Buyya, 2021) and the wider availability of cloud storage (Jordan & Mitchell, 2015). These changes potentially enable BM that were previously unthinkable (Vetter, Hoffmann, Pumplun, & Buxmann, 2022). However, at the same time, the specific characteristics and capabilities of ML make it difficult to create real business value for organisations from this technology (e.g., Burström, Parida, Lahti, & Wincent, 2021).
ML technology can be used to implement artificial intelligence (AI) by learning patterns in data and then making predictions (Brynjolfsson & Mitchell, 2017; Saura, 2021). Harnessing the potential of ML technology is a challenge for any organisation: it requires enormous resources and differs significantly from building an ML based on conventional technologies (Vetter et al., 2022). While most organisations benefit from data and data analytics by improving specific processes, some BMs use data and data-driven learning as their crucial resource in becoming ML-driven BMs (Vetter et al., 2022). However, not all companies using ML technologies manage to create value or capture much of the value created (Joshi, Austin & Sundaram, 2021). While some organisations that invest in particular uses of ML technology succeed in deploying it in a way that creates unparalleled value, others that introduce similar uses of ML technology fail (Singhal & Kapur, 2021).
As a result, managers ask how best to approach the uses of ML for value creation and capture (Bughin et al., 2017), the answers to which question are crucial for several reasons. They can help managers ensure that their ML technology investment decisions and subsequent actions do create value and increase the firm’s bottom-line performance.