Application of Artificial Intelligence in Strategic Marketing Decision-Making Processes

The purpose of this study is to explore the perceptions of subject matter experts (SME) about the use of artificial intelligence for stock trading. AI is already used to analyze large and various amounts of data to support decision-making of future predictions for many organizations today. Looking ahead, AI has the potential to disrupt and be a game changer for the stock trading market. The theoretical foundation used will be the Efficient Market Hypothesis. This theory helps us conceptualize the market that AI is developed to predict. The research design was a multiple case study where we identify themes from the data collected from interviews of SME and case studies to gather a better understanding of this phenomena. The findings of this study revealed the following themes about the use of AI for stock trading 1) Efficiency, 2) Data processes, 3) Trust, 4) Other Limitations, and 5) Human Supportive. This concluded that AI is efficient and used as a supportive tool for stock trading. A limitation to this study lies in the sampling methodology. These results may be helpful to scholars by providing more insightful information regarding AI in stock trading and to practitioners by bridging the gap between theory and real-market applications