DOI: 10.52340/ekonomisti.2026.01.12

Extended Abstract

This paper examines the role and significance of Artificial Intelligence (AI) in the design and implementation of contemporary economic policy. The primary objective of the study is to demonstrate that AI should no longer be regarded merely as a technological innovation or auxiliary digital tool, but rather as a systemic and strategic component of modern economic policy instruments. The analysis is grounded in the theoretical evolution of economic policy frameworks, public administration models, and the concept of Evidence-Based Policymaking (EBPM), which increasingly relies on data quality, analytical capacity, and predictive accuracy.

The theoretical discussion highlights the transition from traditional bureaucratic governance models to hybrid frameworks that integrate principles of New Public Management and New Public Governance. These transformations have rendered economic policymaking more complex, multi-actor-oriented, and data-dependent. In such an environment, the effectiveness of economic policy is critically determined by the ability of public institutions to collect, process, and interpret large-scale data in a timely and systematic manner. This structural shift creates the institutional and technological conditions under which AI emerges as an indispensable element of economic governance.

Within this framework, the paper conceptualizes AI through three interrelated functional dimensions: administrative, analytical, and strategic AI. Administrative AI focuses on the automation of public services and bureaucratic processes, contributing to reduced operational costs, increased transparency, and enhanced efficiency of public administration. Analytical AI enables the processing and synthesis of large volumes of economic, financial, and socio-structural data, facilitating the identification of complex patterns, improved forecasting, and more robust risk assessment. Strategic AI, in turn, supports simulation-based policy modeling, allowing policymakers to evaluate alternative scenarios and anticipate potential outcomes prior to real-world implementation.

A central contribution of the study is the development of a three-layer conceptual model of AI integration in economic policymaking. This model consists of data infrastructure, analytical algorithms, and intelligent decision-support systems. Together, these layers form a coherent governance architecture that enhances data-driven decision-making, policy predictability, and strategic flexibility. The model illustrates how AI transforms economic policy from a predominantly retrospective and static process into a dynamic, forward-looking, and analytically grounded system.

Special attention is devoted to the context of Georgia as a small and open economy. The analysis suggests that AI integration in economic policy and public governance represents a particularly valuable opportunity for accelerating economic development, strengthening innovative entrepreneurship, and enhancing integration into global markets. Existing digital public services and data infrastructures in Georgia provide a foundational platform for the further development of AI-based analytical and decision-support systems. In this regard, AI is not only a technological asset but also a strategic lever for improving state capacity and competitiveness.

The findings of the study confirm that the systematic and institutionally embedded adoption of AI significantly enhances the effectiveness, accuracy, and predictability of economic policy. By enabling real-time analysis, scenario-based simulations, and evidence-driven decision-making, AI reduces uncertainty and policy risk while improving governance outcomes. Consequently, the future success of economic policy increasingly depends on the ability of governments to integrate AI into policymaking processes through scientifically grounded, ethically responsible, and strategically coherent approaches. In this sense, AI constitutes a key pillar of modern economic governance and a critical determinant of long-term national competitiveness.