Document Type : Research Paper
Authors
1 Assistant Prof., Department of Industrial Management, Faculty of Human Sciences, Meybod University, Meybod, Iran.
2 Masters student of Industrial Management, Department of Management, Meybod University, Meybod, Iran.
Abstract
Artificial Intelligence (AI), as one of the most transformative emerging technologies, is rapidly evolving and reshaping various sectors. For Iran, as a developing country, understanding the future trajectories of AI and implementing strategic planning for its optimal development are of critical importance. This study aims to explore the future of AI development in Iran using a structural scenario planning approach with a horizon set to 2035. The research is applied in nature and employs a descriptive-survey method for data collection. The statistical population includes university professors, managers, and experts in the AI industry. Unlike previous studies that primarily focused on specific domains of AI, this research adopts a comprehensive and national-level perspective, introducing the first structural scenario framework for AI development in Iran. It identifies and analyzes four key scenarios: the AI Vacuum, the AI Renaissance, the AI Mirage, and AI Transactions. These scenarios are built upon the analysis of critical driving forces, such as governmental policies, advanced technological infrastructure, challenges of technological singularity, geopolitical dynamics, innovation accelerators, and AI applications across industries. The findings reveal that the future development of AI in Iran is highly dependent on governmental support and the advancement of appropriate infrastructure. While critical scenarios demand immediate policy intervention, the more desirable ones offer significant opportunities for sustainable AI growth. These scenarios can serve as a foundation for designing targeted policy strategies, such as a national AI roadmap and the restructuring of innovation support systems, thereby providing a structured framework for decision-making under conditions of uncertainty.
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