Bottlenecks and Policy Implications of the Artificial Intelligence Technological Innovation System in Iran

Document Type : Research paper

Authors

1 Assistant Professor of Sociology of Science, Technology, and Innovation, Institute for Cultural and Social Studies, Tehran, Iran

2 Master of Technology Management, University of Tehran, Tehran

10.22034/rahyaft.2026.12368.1697

Abstract

This study aims to identify and explain the functional, structural, and contextual bottlenecks of the artificial intelligence (AI) technological innovation system in Iran and to derive key points for policy intervention. Drawing on the Technological Innovation Systems (TIS) framework, the performance of the system is analyzed through the status of its core functions and the interactions among system functions, actor and institutional structures, and contextual conditions. The study adopts a qualitative approach using deductive–inductive thematic analysis. First, evidence concerning the performance and dysfunctions of system functions was extracted through a review of the literature, national documents, and policy reports. Second, data from 12 semi-structured interviews with academic experts (3), industry experts (4), and policymakers/regulators (5) were coded and interpreted using a “function–structure–context” matrix. The credibility of the findings was enhanced through participant validation, documentary triangulation, and peer auditing. The findings indicate that, despite the growth of knowledge production capacity, weaknesses in three key functions—resource mobilization, legitimation, and market formation—constitute the main bottlenecks of Iran’s AI technological innovation system. At the structural level, institutional fragmentation and overlap, geographical concentration, and weak university–industry linkages constrain the capacity of these functions. At the contextual level, macroeconomic uncertainty, sanctions, and gaps in data governance intensify external pressures and institutional uncertainty. The interaction of these factors generates three chains of systemic failure, leading to constrained development and commercialization capacity, increased regulatory uncertainty and weakened legitimacy, and, ultimately, a disconnect between knowledge production, innovative demand, and technological learning. Accordingly, policy interventions should focus on addressing systemic points of failure rather than merely creating new institutions. Key interventions include establishing or restructuring coordinated and sufficiently empowered AI regulatory arrangements; developing a data governance framework covering data quality, access, sharing, and protection; expanding computational infrastructure and innovative public procurement; strengthening university–industry intermediary institutions; and promoting clusters and innovation centers across different regions of the country. The contribution of this study lies in reconstructing the relationships among functional weaknesses, structural configurations, and contextual pressures and translating them into systemic failure chains and policy intervention points for the development of Iran’s AI technological innovation system.

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References

Adebayo Olusegun, A., Adepoju, O., & Adetunji, A. (2023). Artificial intelligence adoption in developing countries: Opportunities and challenges. Technology in Society, 72, 102182. https://doi.org/10.1016/j.techsoc.2022.102182
Akbari, A., Danaeefard, H., Abdolhamid, M., Mahmoudi, M., & Khosravi, M. (2024). Identification and prioritization of barriers and challenges of data-driven governance from the perspective of using artificial intelligence and data-based technologies in the public sector. Strategic Studies of Public Policy, 14(51), 56-81. (Persian) https://doi.org/10.22034/sspp.2024.2026836.3610
Akbari, A., Soltani, A., & Falahatkar, S. (2025). AI governance (7): Capacity assessment for AI development in the country and recommendations for capacity synergy. Tehran: Islamic Parliament Research Center. (Persian)
Alinaghian, A., Safdari-Ranjbar, M., & Mohammadi, M. (2023). Designing a policy package for the development of artificial intelligence in Iran. Public Policy, 9(1), 22-46. (Persian) https://doi.org/10.22059/jppolicy.2023.92986
Artificial Intelligence Atlas of Iran. (2024). Iran artificial intelligence atlas: A tool to get to know the activists of Iran's artificial intelligence ecosystem. Ecomotive. Retrieved from: https://ai-atlas.ir (Persian)
Bergek, A., Hekkert, M., Jacobsson, S., Markard, J., Sandén, B., & Truffer, B. (2015). Technological innovation systems in contexts: Conceptualizing contextual structures and interaction dynamics. Environmental Innovation and Societal Transitions, 16, 51-64. https://doi.org/10.1016/j.eist.2015.07.003
Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., & Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37(3), 407-429. https://doi.org/10.1016/j.respol.2007.12.003
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies: 'Engines of growth'? Journal of Econometrics, 65(1), 83-108. https://doi.org/10.1016/0304-4076(94)01598-T
Carlsson, B., & Stankiewicz, R. (1991). On the nature, function, and composition of technological systems. Journal of Evolutionary Economics, 1(2), 93-118. https://doi.org/10.1007/BF01224915
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd Ed.). Thousand Oaks: Sage Publications.
Edquist, C. (2005). Systems of innovation: Perspectives and challenges. In J. Fagerberg, D. Mowery, & R. Nelson (Eds.), The Oxford handbook of innovation (pp. 181-208). Oxford: Oxford University Press.
European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance). Retrieved from: https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V. & Vayena, E. (2018). AI4People— An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707. https://doi.org/10.1007/s11023-018-9482-5
Hekkert, M. P., & Negro, S. O. (2009). Functions of innovation systems as a framework to understand sustainable technological change: Empirical evidence for earlier claims. Technological Forecasting and Social Change, 76(4), 584-594. https://doi.org/10.1016/j.techfore.2008.04.013
Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74(4), 413-432. https://doi.org/10.1016/j.techfore.2006.03.002
Heydari, H., Mansoori, S., Naseri Jezeh, M., & Pazhouhan, M. (2026). Exploring open data system promotion strategies in stakeholders' narratives: A qualitative study. Sciences and Techniques of Information Management, 12(1), 124-138. (Persian) https://doi.org/10.22091/stim.2025.12883.2255
Heydari, H., Mirshafiei, V., & Khaledi, A. (2025). AI companies in Iran: A qualitative study of business barriers and challenges. Science and Technology Policy, 15(4). (Persian) https://dor.isc.ac/dor/20.1001.1.24767220.1404.15.4.2.4
Kameli, B., & Taheri, R. (2023). Institutional mapping and national division of labor in the field of artificial intelligence development. Tehran: Islamic Parliament Research Center. (Persian)
Kanani, F., Rasoulian, P., Hafezi, R., & Ahangari, S. S. (2023). Analysis of Iran's artificial intelligence ecosystem and identification of its institutional and functional gaps. Science and Technology Policy, 16(2), 59-77. (Persian) https://doi.org/10.22034/jstp.2023.11303.1648
Kattel, R., & Mazzucato, M. (2018). Mission-oriented innovation policy and dynamic capabilities in the public sector. Industrial and Corporate Change, 27(5), 787-801. https://doi.org/10.1093/icc/dty032
Klein Woolthuis, R., Lankhuizen, M., & Gilsing, V. (2005). A system failure framework for innovation policy design. Technovation, 25(6), 609-619. https://doi.org/10.1016/j.technovation.2003.11.002
Lundvall, B. A. (1992). National systems of innovation: Toward a theory of innovation and interactive learning. London: Pinter Publishers.
Mahmoudi, M., Mohammadzadeh, M., & Akbari, E. (2024). AI governance (5): The AI regulatory system in the country. Tehran: Islamic Parliament Research Center. (Persian)
Markard, J., & Truffer, B. (2008). Technological innovation systems and the multi-level perspective: Towards an integrated framework. Research Policy, 37(4), 596-615. https://doi.org/10.1016/j.respol.2008.01.004
Markard, J., Hekkert, M., & Jacobsson, S. (2015). The technological innovation systems framework: Response to six criticisms. Environmental Innovation and Societal Transitions, 16, 76-86. https://doi.org/10.1016/j.eist.2015.06.009
Miremadi, S. E. (2019). Technological innovation system: A model for innovation and technology development policy. Journal of Science and Technology Policy, 12(2), 171-192. (Persian)
Miremadi, S. E., Ahmadi, A. M., Sazgarnejad, A., Abedinzadeh, Z., Jadidzadeh, A., Zohourian, K., Einol-Dast, Z., & Bahmanpour, M. H. (2024). Iran AI Index 2024. Sharif Center for Artificial Intelligence Strategy and Transformation & Policy Lab, Sharif University of Technology. Retrieved from: https://aistrategy.tsc.sharif.ir/fa/report/4 (Persian)
Nambisan, S., Wright, M., & Feldman, M. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8), 103773. https://doi.org/10.1016/j.respol.2019.03.018
Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge: Harvard University Press.
North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511808678
Payandeh, R. (2024). Designing a data governance system in the private sector using Soft Systems Methodology (SSM): A case study of the Iran Chamber of Commerce, Industries, Mines and Agriculture. Strategic Studies of Public Policy, 14(51, Special Issue), 22-44. (Persian) https://doi.org/10.22034/sspp.2024.2027704.3640
PwC. (2025). The fearless future: PwC’s 2025 global AI jobs barometer. Retrieved from: https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf
Safdari-Ranjbar, M. (2024). Policy report on the review of the National Artificial Intelligence Document. Tehran: National Research Institute for Science Policy. (Persian)
Smith, A., & Stirling, A. (2010). The politics of social-ecological resilience and sustainable socio-technical transitions. Ecology and Society, 15(1), 11.
Supreme Council of the Cultural Revolution. (2024). National artificial intelligence document of the Islamic Republic of Iran.Tehran: Islamic Parliament Research Center. Retrieved from: https://rc.majlis.ir/fa/law/show/1811432
Tabatabaei, S. M., Miremadi, S. E., & Zarghami, M. (2024). Analysis of institutional barriers to data-driven governance at organizational and inter-organizational levels: A case study of shared access to water data in Iran. Journal Strategic Studies of Public Policy, 14(51), 82-103. (Persian) https://doi.org/10.22034/sspp.2024.2033080.3667
Tortoise Media. (2024). The Global Artificial Intelligence Index 2024. Retrieved from: https://www.tortoisemedia.com/2024/09/19/the-global-artificial-intelligence-index-2024
UNCTAD. (2023). Technology and innovation report 2023: Opening green windows - Technological opportunities for a low-carbon world. Geneva: United Nations Conference on Trade and Development. https://doi.org/10.18356/9789210024778
Secundo, G., Spilotro, C., Gast, J., & Corvello, V. (2025). The transformative power of artificial intelligence within innovation ecosystems: A review and a conceptual framework. Review of Managerial Science, 19(9), 2697–2728. https://doi.org/10.1007/s11846-024-00828-zWieczorek, A. J., & Hekkert, M. P. (2012). Systemic instruments for systemic innovation problems: A framework for policy makers and innovation scholars. Science and Public Policy, 39(1), 74-87. https://doi.org/10.1093/scipol/scr008
World Bank. (2024). Global trends in AI governance: Emerging approaches and considerations for developing countries. Washington, DC: World Bank Group.
Zhang, J., & Lu, Y. (2022). The infrastructure of artificial intelligence: Cloud computing, data centers, and computational resources. AI & Society, 37(3), 811-825. https://doi.org/10.1007/s00146-021-01219-9
Zhang, T., Li, X., & Chen, Y. (2025). Cost trends and diffusion pathways of large AI models. Nature Machine Intelligence, 7(2), 99-110. https://doi.org/10.1038/s42256-025-00789-3

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