Assessing the ecosystem readiness for AI adoption in Uzbekistan's banking sector: a multi-stakeholder perspective
Abstract
Artificial Intelligence (AI) is increasingly a revolutionary force in global banking, although its use in emerging economies like Uzbekistan is less documented. The banking sector in Uzbekistan is made up of a mix of state and foreign banks in a transitional digital setting.
Problem Statement. Despite government-led efforts at the digitalization of the economy, little is known about the readiness of the banks of Uzbekistan for the adoption of AI technologies, or the strategic priority of such adoptions by different types of banks.
Unresolved aspects of the problem. Existing literature predominantly focuses on AI readiness at the macroeconomic level without specific details regarding the sectoral adoption processes. Empirical analysis integrating digital infrastructure, institutional readiness, and workforce competencies within the case of Uzbekistan's banking is also nonexistent.
Purpose of the Article. This research seeks to evaluate the AI adoption environment in the banking industry of Uzbekistan through the integration of various data sources in order to assess regulatory, technological, and human capital preparedness, along with visible implementation trends.
Presentation of the Main Material. The approach adopts a multi-source exploratory method, including Oxford Insights AI Readiness Index (2024) research, web scraping of bank websites for AI disclosures, and investigating labor market activity on platforms like Telegram, LinkedIn for AI-skilled personnel presence. The use of Playwright and BeautifulSoup in a Google Colab environment enabled successful keyword-based surveillance of publicly available AI-related projects.
Conclusions. The results indicate a split landscape: state banks focus on back-office automation while foreign banks are more likely to experiment with customer-facing AI solutions. With national digital agendas still unfolding, AI talent shortages, infrastructural limitations, and relative opaqueness persist. Based on this evidence, policy suggestions for AI planning and banking innovation in developing economies are presented.
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