Examining the limitations of AI in business and the need for human insights using Interpretive Structural Modelling

Abstract

The integration of Artificial Intelligence (AI) in business settings is rapidly increasing, yet significant limitations hinder its effective use and adoption. Understanding these limitations and their interrelationships is crucial for enhancing AI implementation. Despite growing research, there is a lack of a comprehensive model that systematically identifies and elucidates the factors influencing AI limitations in business environments. This study employs Interpretive Structural Modeling (ISM), combined with MICMAC analysis and an extensive literature review, to develop such a model. We identified 15 key factors and analyzed their driving and dependence powers to understand their interrelationships. Most factors, such as contextual understanding, transparency, intuition, emotional intelligence, ethics, bias, tacit knowledge, creativity, credibility, and reliability, were found to be autonomous. Accountability and privacy emerged as the strongest driving forces, while trust and adaptability exhibited the highest dependence and lowest driving power. This research offers a comprehensive understanding of AI limitations and their interrelationships, providing valuable insights for managers and businesses. The findings can aid in making more informed decisions about AI implementation and in developing strategies to mitigate these limitations. Furthermore, the study emphasizes the importance of combining AI with human insight to overcome these challenges. However, using the ISM technique could involve subjective judgment from the experts.

Keywords

• Artificial intelligence
• Limitations
• Business
• Interpretive structural modeling
• Interrelationships
• Open innovation