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ՀՀ-ում արհեստական բանականության տարածվածության մակարդակի վերլուծություն

Aug 2026 · Սոցիալ-տնտեսական զարգացման արդի հիմնախնդիրները Հայաստանի Հանրապետությունում=The contemporary issues of socioeconomic development in the Republic of Armenia · 0 citations · 10 references

Abstract

Artificial Intelligence (AI) constitutes a pivotal driver of technological advancement in the 21st century, profoundly transforming economic structures, governance mechanisms, educational models, and the scientific research environment. Currently, AI has transitioned from a niche research domain into a general-purpose technology integrated across numerous sectors; however, its maturity and depth of application remain highly uneven across various industries, regions, and enterprise scales. The Republic of Armenia (RA), possessing a developed IT sector and relatively robust human capital in technological disciplines, is currently in the initial stages of systemic AI implementation. While isolated initiatives, educational programs, and investment projects exist, the overall adoption rate remains limited and has yet to achieve large-scale, coordinated integration. This gap underscores the relevance of the current research. Concurrently, the evolution of AI generates new opportunities for enhancing productivity, innovation, and competitiveness, while simultaneously posing challenges related to labor market disruptions, the necessity for skills transformation, and the exacerbation of digital inequalities. The primary task of this study is to identify the current baseline of AI application. The overarching objective is to analyze and evaluate the present degree of AI penetration in Armenia within the context of global technological trends. Furthermore, the research aims to uncover the depth of AI integration across different sectors and assess its potential impact on the country's economic development prospects. The methodology employed for data collection and analysis incorporates systemic, descriptive, and comparative approaches, alongside economic-statistical clustering techniques. The empirical foundation of the study is supported by data from World Bank reports and International Monetary Fund (IMF) publications.

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