The country's AI technology development capacities in light of strengths and opportunities: An analysis based on the SOAR framework

Document Type : Original Article

Author
Researcher at the Islamic Consultative Assembly Research Center, PhD student in Public Policy, Allameh Tabatabaei University
Abstract
Artificial Intelligence Technology Development Capacities in Iran

Iman Akbari

Background and Objective: Artificial intelligence (AI) is poised to play a pivotal role in the future of societies. Evidence of this influence is already observable in domains such as the military, media and cyberspace, interactive robotics, and numerous other areas. The specific conditions, requirements, and strategic objectives of our country underscore the importance of carefully examining national capacities for the development of this transformative technology, with a view to informed planning and action. In this regard, two main domains must be distinguished: (1) the development of the foundational layer (models and algorithms, national strategic orientation, network architecture and infrastructure, etc.); and (2) the development of the application layer, where AI serves as a transformative tool across various sectors.
Methodology: This study employed the SOAR framework to identify the strengths, opportunities, aspirations, and results associated with AI development at both the foundational and application layers, with the goal of capacity assessment for advancing this technology in the country. Semi-structured interviews were conducted with ten domain experts to gather insights.
Findings: The analysis identified six key areas of national capacity for AI development: human capital, institutional capacity, regional and international partnerships, investment, innovation ecosystem, and infrastructure. Based on these findings, policy recommendations were formulated to leverage these capacities for AI advancement in the country.
Conclusion: The most significant recommendations include: defining one or two strategic focus areas for the country; ensuring coordination among governmental bodies, the private sector, and academic institutions; establishing a strategic studies center and a national laboratory; emphasizing technological independence in critical domains; building on national experience in supporting innovation; creating mechanisms to direct domestic investment toward knowledge-based and emerging technologies; activating diplomacy to foster collaboration with advanced and like-minded countries; guiding talented professionals toward AI development while designing incentives for the return of experts in this field; and strengthening specialized approaches to policymaking, investment, and implementation in relation to AI.
Keywords

Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: a systematic literature review. AI and Ethics 2024 5:3, 5(3), 3265–3279. https://doi.org/10.1007/S43681-024-00653-W
Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: themes, knowledge gaps and future agendas. Internet Research, 33(7), 133–167. https://doi.org/10.1108/INTR-01-2022-0042
Bullock, J. B., Chen, Y. C., Himmelreich, J., Hudson, V. M., Korinek, A., Young, M. M., & Zhang, B. (2022). The Oxford Handbook of AI Governance. The Oxford Handbook of AI Governance, 1–1077. https://doi.org/10.1093/OXFORDHB/9780197579329.001.0001
DeepSeek rumored to build R2 AI model using Huawei AI chips - Huawei Central. (n.d.). Retrieved 3 May 2025, from https://www.huaweicentral.com/deepseek-rumored-to-build-r2-ai-model-using-huawei-ai-chips/#google_vignette
Framework for Anticipatory Governance of Emerging Technologies. (2024). 165. https://doi.org/10.1787/0248EAD5-EN
Hughes, N. (2024). Blended learning solutions in higher education: History, theory and practice. Blended Learning Solutions in Higher Education: History, Theory and Practice, 1–183. https://doi.org/10.4324/9781003359821
Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes. Information Systems Frontiers, 25(1), 123–141. https://doi.org/10.1007/S10796-022-10251-Y/FIGURES/1
Stavros, J., & Saint, D. K. (2010). SOAR: linking strategy and od to sustainable performance. In William J. Rothwell, Jacqueline M. Stavros, Roland L. Sullivan, & Arielle Sullivan (Eds.), Practicing organization development: A guide for leading change (pp. 377–398). Jossey-Bass Publishers. https://www.researchgate.net/publication/285056921_SOAR_linking_strategy_and_od_to_sustainable_performance
Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society, 40(2), 137–157. https://doi.org/10.1080/14494035.2021.1928377
Volume 6, Issue 12 - Serial Number 12
Humanities
Volume 6, Autumn and Winter 2025-2026, No. 12
March 2026
Pages 25-58