هوش مصنوعی و یادگیری ماشین به عنوان ابزارهای کسب و کار
Abstract
1- Artificial intelligence and machine learning in business
2- Components of an AI solution
3- Characteristics and effects of AI and ML
4- Identifying the value-destruction potential of AI
5- Implementing the framework
6- Implications for managers
References
Abstract
Artificial intelligence (AI) and machine learning (ML) may save money and improve the efficiency of business processes, but these technologies can also destroy business value, sometimes with grave consequences. The inability to identify and manage that risk can lead some managers to delay the adoption of these technologies and thus prevent them from realizing their potential. This article proposes a new framework by which to map the components of an AI solution and to identify and manage the value-destruction potential of AI and ML for businesses. We show how the defining characteristics of AI and ML can threaten the integrity of the AI system’s inputs, processes, and outcomes. We then draw from the concepts of value-creation content and value-creation process to show how these risks may hinder value creation or even result in value destruction. Finally, we illustrate the application of our framework with an example of the deployment of an AIpowered chatbot in customer service, and we discuss how to remedy the problems that arise.
Artificial intelligence and machine learning in business
As we enter the fourth industrial revolution (Schwab, 2016), artificial intelligence (AI) and machine-learning (ML) technologies are driving business automation in more and more areas, from calculating optimal transport loads to shortlisting loan applicants without human input. These technologies promise to be more costeffective than humans (Castelli, Manzoni, & Popovic, 2016), but they can also be problematic. For instance, automatic trading algorithms have created flash crashes in the U.S. stock market (Varol, Ferrara, Davis, Menczer, & Flammini, 2017), and one of Uber’s self-driving vehicles hit and killed a pedestrian (Levin & Wong, 2018).
Surveys show that managers are delaying the adoption of AI and ML because they are unsure how these technologies can help their firms (Bughin, Chui, & McCarthy, 2017). This article aims to empower decision makers to identify problems so that they can manage risks and be confident in their investments. Specifically, we propose a framework that considers the various components of an AI solution, their fundamental characteristics, and how these may result in the destruction of value for the business