ECONOMIC OPPORTUNITIES AND CONSTRAINTS OF ARTIFICIAL INTELLIGENCE ADOPTION IN KAZAKHSTAN’S RESOURCE-ORIENTED ECONOMY

Ravil I. Mukhamediev, Farida Abdoldina, Y. A. Gertsen

2026

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DOI 10.32014/2026.2518-1726.463
Data confidence 70 medium
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Abstract

The article explores the economic opportunities and structural constraints of artificial intelligence adoption in Kazakhstan in a resource-oriented economy where extractive industries are a significant part and the level of high-tech diversification is relatively low. The study seeks to assess the potential macroeconomic impact of AI in several areas and to determine the conditions of its contribution to sustainable economic development. A sectoral analytical framework is used, which involves integrating information from the national accounts with AI productivity coefficients from international empirical research. It examines the main sectors of the economy, such as industry, trade, transport, energy, finance, agriculture and health, as well as the communications industry. Emphasis is placed on new technologies like machine learning, deep learning, large language models, and generative AI, which offer further automation and decision support opportunities for various sectors. Under the adopted assumptions, the model-based scenario estimate suggests that the potential economic impact of AI use in Kazakhstan may range between USD 1.6 and 2.59 billion per year, or around 0.9%–1.5% of GDP. This estimate should be interpreted as an indicative scenario-based result derived from international productivity coefficients and Kazakhstan’s sectoral GDP structure, rather than as a direct empirical measurement of the current level of AI adoption by Kazakhstani enterprises. The most substantial potential effect is concentrated in industry, trade, and transport, whereas the contribution of other sectors is more moderate but remains macroeconomically relevant. At the current stage, AI is mainly considered as a tool for efficiency improvement rather than as a driver of structural transformation. The study also highlights crucial constraints, such as a lack of digital infrastructure, human capital, research and development intensity, and reliance on resource-intensive industries. These all limit the diffusion and multiplier effects of AI technologies. It shows the need for policies that are coordinated with respect to technological modernization, education and economic diversification in order to attain the long-term benefits of AI. If left unchecked, AI can exacerbate existing structural imbalances, instead of helping to transition to a knowledge-based economy.