A research on feasibility of artificial intelligence performing as chief executive officer

The main purpose of this research is to examine the feasibility of artificial intelligence as chief executive officer (CEO) in organizations. A classic grounded theory methodology was followed throughout the whole research process. Face-to-face interviews were conducted with 27 participants selected according to "theoretical sampling" and consisted of academicians from the fields of artificial intelligence (AI), philosophy, and management; experts and artists performing in the field of AI, and professionals from the business world as executives, and entrepreneurs. Semi-structured and unstructured interview methods were used in collecting qualitative data. In accordance with the classic grounded theory methodology; codding, analysis, and data collecting stages were processed jointly and simultaneously. The data collected from participants selected by theoretical sampling analyzed by constant comparative analysis. At the first stage, open codding applied to the data. Soon after the core category emerged titled as "hard problems", the process of relating the other categories with the core category -selective codding- started. As the categories achieved theoretical saturation, data collecting processed closed and as a result a theory describing the relationship among theoretical categories was constructed. The theory, describes the evolution process of narrow AI to general AI and the possible scenarios after then, was titled as "The Vizier-Shah Theory". The theory consisted of six theoretical categories: "narrow AI", "hard problems", "debates", "solutions", "AI as a CEO" and "scenarios".