Decision-Making Scenarios Utilizing Artificial Intelligence in Organizations

Making correct decisions is one of the key factors determining the success of organizations. Efforts have always been made to improve the decision-making process within organizations. With the rapid growth of the artificial intelligence market, organizations are increasingly leveraging this technology to optimize their decision-making processes. Amid this trend, there are significant hopes and concerns about the future of decision-making supported by machine intelligence in organizations. This research aims to outline scenarios for the future of decision-making using AI.

This study adopts a futures research approach and utilizes the "Global Business Network" method to develop scenarios. Initially, data derived from the literature and semi-structured interviews with experts were coded using thematic analysis and classified into seventeen categories. Subsequently, through cross-impact analysis and expert evaluations, two drivers were selected based on their importance and uncertainty to construct the scenarios. Finally, four scenarios were developed based on these two main drivers.

The research identified seventeen drivers influencing machine intelligence-based decision-making in organizations. Using cross-impact analysis, two critical uncertainties—AI’s decision-making ability compared to humans and the extent of AI adoption in decision-making—were chosen. Based on these two axes, four scenarios were outlined: Marginalized Human , The Mirage of Machine Intelligence, Humans as Decision Masters, and Missed Opportunities. Each scenario depicts a different future regarding the role of humans and AI in organizational decision-making. The intermediate scenario illustrates the coexistence of AI and humans.

Due to economic constraints and sanctions, the likelihood of the "Missed Opportunities" scenario occurring in Iran is high. Limited investment in AI and the lack of prioritization for its development may push the country away from even a constructive coexistence between humans and machine intelligence. To avoid this outcome, it is recommended that organizations initiate small pilot projects, utilize open-source tools and cloud services to reduce costs, and control technological risks. Continuous employee training and collaboration with universities and research centers can also enhance skills and reduce external dependency. Finally, monitoring return on investment and adopting agile methodologies will pave the way for gradual and efficient utilization of AI.