Abstract

The use of Artificial Intelligence (AI) in organizations has grown tremendously. With its ability to avail vast expertise, Artificial Intelligence has transformed the dynamics of different decision situations making it faster and more accurate (Meyer et al. 2014). Increasingly, Artificial Intelligence is affecting the workplace by performing creative jobs that were previously reserved for humans. The absence of the human factor working side-by-side with these technologies is labeled as artificial intelligence. To name a few, the New York Times has experienced the “AI reporter”, and American Express relies on Artificial Intelligence to detect fraud in real time. Other organizational solutions requires the collaboration between humans and artificial intelligence and therefore labeled as “augmented intelligence”. For instance. IBM Chef Watson became a sous-chef in the kitchen helping human to develop recipes on food combinations. Based on business needs, two paradigms have evolved with distinct views on the relation between human and artificial intelligence. The first paradigm was introduced by McCarthy and called “the Good Old Fashioned AI” (GOFAI) (McCarthy, 2007). It is also known as the rationalistic approach and can be defined as: 1) making computers smart, 2) making models of human intelligence, and 3) building machines that simulate human intelligent behavior. The paradigm is about developing a Human Level - Artificial Intelligence’. The second paradigm is prone for a ‘man-computer symbiosis’ (Licklider, 1960). It has many labels such as “design approach”, and ‘Intelligence Augmentation’ or ‘Augmented Intelligence’. Designed to enhancing human intelligence, augmented intelligence assists in accumulating strategic knowledge that guide expert actions and help gain insights to make better decision (Meyer, et al. 2014). The aim of this exploratory research is to categorize organizational problems with the associated decision/solution taking into consideration the type of artificial intelligence adopted: an automated solution using AI or an augmented intelligence with human-computer collaboration. To this end, we plan on conducting an exploratory qualitative research targeting managers at organizations that depends on AI in its operations. We will develop semi-structured in-depth interview guide that shed the light on the participants’ personal experiences given their specific roles in the types of artificial intelligence implementations at their respective organizations. Specifically, we will ask the participants about problems the organizations face, and the type of artificial intelligence implemented to address this problem. Collecting the characteristics of the problem will help develop a scheme for the level of complexity, and the appropriate solution of AI or automated intelligence decisions. This research will help develop a taxonomy of the problems (e.g., complexity) vs decisions/solutions using artificial intelligence or augmented intelligence in organizational settings.

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Types of Artificial Intelligence and Decision Making in Organizations

The use of Artificial Intelligence (AI) in organizations has grown tremendously. With its ability to avail vast expertise, Artificial Intelligence has transformed the dynamics of different decision situations making it faster and more accurate (Meyer et al. 2014). Increasingly, Artificial Intelligence is affecting the workplace by performing creative jobs that were previously reserved for humans. The absence of the human factor working side-by-side with these technologies is labeled as artificial intelligence. To name a few, the New York Times has experienced the “AI reporter”, and American Express relies on Artificial Intelligence to detect fraud in real time. Other organizational solutions requires the collaboration between humans and artificial intelligence and therefore labeled as “augmented intelligence”. For instance. IBM Chef Watson became a sous-chef in the kitchen helping human to develop recipes on food combinations. Based on business needs, two paradigms have evolved with distinct views on the relation between human and artificial intelligence. The first paradigm was introduced by McCarthy and called “the Good Old Fashioned AI” (GOFAI) (McCarthy, 2007). It is also known as the rationalistic approach and can be defined as: 1) making computers smart, 2) making models of human intelligence, and 3) building machines that simulate human intelligent behavior. The paradigm is about developing a Human Level - Artificial Intelligence’. The second paradigm is prone for a ‘man-computer symbiosis’ (Licklider, 1960). It has many labels such as “design approach”, and ‘Intelligence Augmentation’ or ‘Augmented Intelligence’. Designed to enhancing human intelligence, augmented intelligence assists in accumulating strategic knowledge that guide expert actions and help gain insights to make better decision (Meyer, et al. 2014). The aim of this exploratory research is to categorize organizational problems with the associated decision/solution taking into consideration the type of artificial intelligence adopted: an automated solution using AI or an augmented intelligence with human-computer collaboration. To this end, we plan on conducting an exploratory qualitative research targeting managers at organizations that depends on AI in its operations. We will develop semi-structured in-depth interview guide that shed the light on the participants’ personal experiences given their specific roles in the types of artificial intelligence implementations at their respective organizations. Specifically, we will ask the participants about problems the organizations face, and the type of artificial intelligence implemented to address this problem. Collecting the characteristics of the problem will help develop a scheme for the level of complexity, and the appropriate solution of AI or automated intelligence decisions. This research will help develop a taxonomy of the problems (e.g., complexity) vs decisions/solutions using artificial intelligence or augmented intelligence in organizational settings.