Introduction

Since its inception in the 1950s, the progress of artificial intelligence (AI) technologies culminated in the development of a particular subfield, machine learning, to such level that nowadays it dramatically reshapes the business practices across industries. The term “machine learning” refers to algorithmic systems that automatically and progressively improve their performance on a specific task with experience (Samuel, 1959). These systems turned out to be particularly useful for generating accurate statistical predictions from available data (Agrawal et al., 2018), thus providing the essential input for managerial decision-making.

The work of managers that determines the course of society comprises essentially decision-making tasks, such as defining agendas, setting goals, designing actions, and evaluating and choosing from possible alternatives (Simon et al., 1987). All these cognitive tasks are based on processing the available information, and this is where the predictions and help of machine learning systems can substantially change the practices and resulting effectiveness and efficiency of the economic activity. Even though one might argue that in addition to making decisions the managers must also ensure their implementation, we will show that in this realm the AI technologies are also going to have a profound, transformational impact.

Today's machine learning technologies embedded in computer systems with ever-increasing power allow gaining nuanced insights from existing data, spotting hidden patterns, and generating meaningful predictions that get better as more data become available. The usefulness of machine learning is thus augmented by the availability of data to learn from (Siegel, 2016). Consequently, these machine learning technologies become particularly useful when combined with big data technologies (i.e., collecting large volumes of unstructured data about social phenomena (McAfee et al., 2012), yielding unprecedented insights that can be used to assist managerial decision-making. As a result, AI technologies have the potential to drastically reshape business operations and to provide multiple new opportunities, thus having a profound impact on management practice with respect to firm-level microeconomic mechanisms of value creation and capture, that is, organizational business models (Biloshapka et al., 2016; Osiyevskyy and Zargarzadeh, 2015).

To date, the impact of AI on the effectiveness and efficiency of organizational business models remains poorly understood. We hope this chapter will throw light on the subject and open avenues for further research.