از چشم‌انداز 1404 تا چشم‌انداز 1430: حکمرانی تطبیقی چشم‌انداز ملی ایران و نقش کلان‌داده و هوش مصنوعی

National visions are formulated and implemented in contexts marked by rapid technological change, complex public problems, economic volatility, geopolitical uncertainty, and growing demands for institutional accountability. Under such conditions, the effectiveness of a long-term national vision depends not only on the ambition of its goals or the attractiveness of its desired future, but also on the existence of mechanisms that connect broad objectives to measurable indicators, baselines, data sources, implementation arrangements, responsible institutions, periodic reporting, and policy revision. The central question of this article is how a national vision can move beyond being a largely directional and static document and become an adaptive, monitorable, learning-oriented, and revisable framework. Focusing on the transition from Iran’s 1404 Vision Document to the 1430 horizon, the article examines the institutional, data-related, and analytical mechanisms required for adaptive governance of national visions and clarifies the supporting role of big data and artificial intelligence in analysis, monitoring, scenario development, and policy decision support.



The study adopts an applied qualitative design and relies on directed document analysis, structured comparison of selected international experiences, and triangulation of evidence. The unit of analysis is not limited to the text of the 1404 Vision Document, but includes written evidence related to its design, implementation, monitoring, and review. In the final version, the documentary scope of the study is clarified more explicitly. In addition to the text of the Vision Document, the Fourth, Fifth, Sixth, and Seventh Development/Progress Plans, provisions related to monitoring, evaluation, productivity, science and technology, smart government, data governance, institutional division of labor, and performance reporting are introduced as part of the documentary evidence. The article also examines the experiences of Finland, Estonia, Singapore, South Korea, the United Arab Emirates, and Malaysia in order to identify complementary lessons on institutional foresight, data interoperability, artificial intelligence strategy, digital government, and the connection between national visions and development planning.



The analytical framework is organized around six dimensions of adaptive governance for national visions: operational definition of goals, indicators, and baselines; data quality, governance, and interoperability; analytical capacity, foresight, and scenario development; institutional coordination, responsibility, and accountability; policy review and learning mechanisms; and legal, ethical, public-trust, and human-oversight requirements. In this framework, foresight, digital transformation, data governance, and artificial intelligence governance are treated as related but distinct concepts. They can contribute to national-vision governance only when connected to the broader policy cycle of goal-setting, implementation, monitoring, learning, and policy adjustment.



The findings show that the 1404 Vision Document played an important role in articulating Iran’s broad national directions. However, clear, integrated, and traceable links among objectives, indicators, data, institutional responsibilities, implementation arrangements, reporting, and periodic review were not sufficiently embedded in the document and in parts of the complementary planning architecture. Evidence from economic performance, science and technology, welfare, human development, and regional standing presents an uneven picture. In the final version, the table on performance evidence has been strengthened by adding the latest available or near-end-of-period data, including evidence from recent reports on Iran’s economy, the Global Innovation Index 2024, the Human Development Report 2025, the Legatum Prosperity Index 2023, and the Seventh Progress Plan. These data are not used to provide a causal evaluation of the achievement or non-achievement of the vision’s objectives. Rather, they are used to strengthen the assessment of the monitoring, feedback, and policy-learning architecture.



The comparative analysis indicates that the effective use of big data and artificial intelligence in public governance depends less on technology alone than on data quality, system interoperability, institutional coordination, analytical capacity, transparency, accountability, and human oversight. Estonia highlights the importance of secure data exchange across independent systems; Finland demonstrates the institutionalization of foresight within policy processes; Singapore and South Korea illustrate links between artificial intelligence strategies, public-sector transformation, national development, and trustworthy governance; and the United Arab Emirates and Malaysia provide relevant lessons on national visions, future-oriented planning, indicators, and the connection between strategic visions and development programs.



Based on the findings, the article formulates three exploratory scenarios for the 1430 horizon: continued fragmented governance, dispersed digitalization, and adaptive data-driven governance. These scenarios are not predictions, but analytical types that clarify the implications of current institutional and policy choices. The study concludes that the 1430 Vision should connect every broad objective to an operational definition, measurable indicator, baseline, data source, responsible institution, implementation arrangement, financing mechanism, reporting cycle, and midterm review process. Big data and artificial intelligence should serve as decision-support tools, not as substitutes for expert judgment, public accountability, institutional responsibility, or citizens’ rights. The main limitation of the study is its reliance on documentary and secondary evidence and the absence of first-hand empirical data. Therefore, future research should validate the scenarios through expert consultation, design sector-specific indicators, assess institutional readiness for data exchange, and examine the legal and organizational requirements of adaptive governance in Iran.