The uncertainty and increasing competitiveness of markets, also linked to the progressive spread of digital technologies, make it necessary to implement flexible business management policies that can ensure business continuity (Marques & Ferreira, 2009). The digital age has led to the spread of different technologies (Schwab, 2016) that have changed the habits of consumers and companies (Jovanović et al., 2018; Kaartemo & Helkkula, 2018). Industry 4.0, Artificial Intelligence (AI), Big Data, Internet of Things, cloud databases, social networks, blockchain and fintech applications are considered the engine of the fourth industrial revolution (Schwab, 2016). Among these, Artificial intelligence is considered one of the most promising digital technologies, having already brought important benefits in different business sectors (Serafini & Garcez, 2016). AI represents a complex of “intelligent” systems “created to use data, analysis and observations to perform certain tasks without the need to be programmed to do so” (Antonescu, 2018). Indeed, the definitions of AI are multiple, having also been defined as “an activity dedicated to making machines intelligent. Intelligence is that quality that allows an entity to function appropriately and with foresight in its environment” (Nilsson, 2010). In fact, the origins of the concept of “artificial intelligence” date back to 1956 when John McCarthy, together with a research group, assumed that every aspect of learning and, more generally, of intelligence, could be replicated by a machine. The goal was therefore to create a machine capable of thinking and acting like a human being (McCorduck, 2009). AI deals with the “study of mental faculties through the use of computational models” (Charniak & McDermott, 1985) and “calculation processes that make it possible to perceive, reason and act” (Salin & Winston, 1992). AI has also been defined as the set of scientific studies aimed at investigating the ways in which computers can think, do, interact and act in many fields just like humans (Rich, 1985). For example, among the first AI applications we find the intelligent assistant developed by Apple Inc., “Siri,” which performs a series of functions and commands through the recognition of the human voice (Dirican, 2015). Learning, adaptation, generalization, inductive and deductive reasoning and human-like communication in a natural language configure the characteristic features of this technology (Kasabov, 2018). What has led to an ever-increasing attention to AI technology is “machine learning,” a branch of AI that uses algorithms to analyze the flows of data and information available to companies, also known as Big Data, in order to use them to build a valuable competitive advantage (Pappas et al., 2018). Another approach distinct from machine learning is represented by deep learning (DP) which has the ability to identify the desired information, process it through learning by creating new information useful for making future predictions, for example with reference to what the user will need, thus creating a new business model (Lee & Park, 2018). To this end, the business model must be built around digital technologies and AI is one of the tools available to companies to implement innovative business models (Parida et al., 2019). The choice of many companies to adopt new digital technologies is justified by the advantages linked to their implementation, for example in terms of process automation, optimization of production times and reduction of costs, errors and risks (Grubic & Jennions, 2018), promoting the use of resources and more efficient business models (Neligan, 2018). In order to achieve the above, it is necessary to first implement changes in the objectives of business management, formulating a digital business strategy (Bharadwaj et al., 2013). Subsequently, it is necessary to innovate the entire business model, making a high initial investment (Lundvall et al., 2002) to support structural, organizational and cultural changes (Ruessmann et al., 2015; Parida et al., 2019). Given the importance of digital technologies in promoting and developing more efficient and sustainable business models, this work intends to focus on artificial intelligence, considered one of the most promising technologies (Serafini & Garcez, 2016) to examine the state of the art on the topic, answering the following research question: RQ: What are the issues that animate the scientific debate on the application of artificial intelligence to corporate business models? To achieve this goal we intend to conduct a systematic literature review so as to provide evidence of the ways in which AI is able to make changes in corporate business models. In particular, we aim to explore the main issues that have animated the debate in the literature between artificial intelligence and business models. The results show that researchers have mainly focused on the following three strands of research: (1) the benefits related to the application of artificial intelligence to business models; (2) the steps leading to the successful application of AI; (3) how the sector influences the application of AI. The work is structured as follows: Sect. 2 describes the research method; Sect. 3 illustrates the results of the literature review related to the study of AI applied to business models; Sect. 4 contains the conclusions of the work.
The Application of Artificial Intelligence to Business Models: A Systematic Literature Review
Simona Ranaldo
;Vittorio Dell'Atti;Mario Turco
2021-01-01
Abstract
The uncertainty and increasing competitiveness of markets, also linked to the progressive spread of digital technologies, make it necessary to implement flexible business management policies that can ensure business continuity (Marques & Ferreira, 2009). The digital age has led to the spread of different technologies (Schwab, 2016) that have changed the habits of consumers and companies (Jovanović et al., 2018; Kaartemo & Helkkula, 2018). Industry 4.0, Artificial Intelligence (AI), Big Data, Internet of Things, cloud databases, social networks, blockchain and fintech applications are considered the engine of the fourth industrial revolution (Schwab, 2016). Among these, Artificial intelligence is considered one of the most promising digital technologies, having already brought important benefits in different business sectors (Serafini & Garcez, 2016). AI represents a complex of “intelligent” systems “created to use data, analysis and observations to perform certain tasks without the need to be programmed to do so” (Antonescu, 2018). Indeed, the definitions of AI are multiple, having also been defined as “an activity dedicated to making machines intelligent. Intelligence is that quality that allows an entity to function appropriately and with foresight in its environment” (Nilsson, 2010). In fact, the origins of the concept of “artificial intelligence” date back to 1956 when John McCarthy, together with a research group, assumed that every aspect of learning and, more generally, of intelligence, could be replicated by a machine. The goal was therefore to create a machine capable of thinking and acting like a human being (McCorduck, 2009). AI deals with the “study of mental faculties through the use of computational models” (Charniak & McDermott, 1985) and “calculation processes that make it possible to perceive, reason and act” (Salin & Winston, 1992). AI has also been defined as the set of scientific studies aimed at investigating the ways in which computers can think, do, interact and act in many fields just like humans (Rich, 1985). For example, among the first AI applications we find the intelligent assistant developed by Apple Inc., “Siri,” which performs a series of functions and commands through the recognition of the human voice (Dirican, 2015). Learning, adaptation, generalization, inductive and deductive reasoning and human-like communication in a natural language configure the characteristic features of this technology (Kasabov, 2018). What has led to an ever-increasing attention to AI technology is “machine learning,” a branch of AI that uses algorithms to analyze the flows of data and information available to companies, also known as Big Data, in order to use them to build a valuable competitive advantage (Pappas et al., 2018). Another approach distinct from machine learning is represented by deep learning (DP) which has the ability to identify the desired information, process it through learning by creating new information useful for making future predictions, for example with reference to what the user will need, thus creating a new business model (Lee & Park, 2018). To this end, the business model must be built around digital technologies and AI is one of the tools available to companies to implement innovative business models (Parida et al., 2019). The choice of many companies to adopt new digital technologies is justified by the advantages linked to their implementation, for example in terms of process automation, optimization of production times and reduction of costs, errors and risks (Grubic & Jennions, 2018), promoting the use of resources and more efficient business models (Neligan, 2018). In order to achieve the above, it is necessary to first implement changes in the objectives of business management, formulating a digital business strategy (Bharadwaj et al., 2013). Subsequently, it is necessary to innovate the entire business model, making a high initial investment (Lundvall et al., 2002) to support structural, organizational and cultural changes (Ruessmann et al., 2015; Parida et al., 2019). Given the importance of digital technologies in promoting and developing more efficient and sustainable business models, this work intends to focus on artificial intelligence, considered one of the most promising technologies (Serafini & Garcez, 2016) to examine the state of the art on the topic, answering the following research question: RQ: What are the issues that animate the scientific debate on the application of artificial intelligence to corporate business models? To achieve this goal we intend to conduct a systematic literature review so as to provide evidence of the ways in which AI is able to make changes in corporate business models. In particular, we aim to explore the main issues that have animated the debate in the literature between artificial intelligence and business models. The results show that researchers have mainly focused on the following three strands of research: (1) the benefits related to the application of artificial intelligence to business models; (2) the steps leading to the successful application of AI; (3) how the sector influences the application of AI. The work is structured as follows: Sect. 2 describes the research method; Sect. 3 illustrates the results of the literature review related to the study of AI applied to business models; Sect. 4 contains the conclusions of the work.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
