Artificial intelligence and small-business competitiveness in an emerging economy

Abstract

Small and medium enterprises (SMEs) are the driving force of developing economies, yet they remain limited in resources to implement artificial intelligence (AI), and there is little empirical evidence that AI adoption is associated with small business growth in the region, particularly in Central Asia. This research makes two contributions. First, we propose a concise framework that links classes of AI technologies to SME competitiveness features and identifies machine learning, natural language processing, and generative AI as low-cost, quick-return priority areas for resource-constrained firms. Second, using 16 years of data (2010–2025) for the Bukhara region of Uzbekistan, we estimate ARIMA models for each indicator and forecast eight small business indicators to 2030. Small business output is projected to grow at a steady rate of approximately 16% by 2030, with digital adoption and preferential credit support emerging as indicators associated with long-run growth. Forecast accuracy is high for the human-capital indicator and acceptable for output. The evidence is associational rather than causal: the framework and the forecasts converge on a single, cautiously formulated proposition—the adoption of low-cost AI is measurably associated with the sustainable competitiveness of SMEs, providing a practical, evidence-based starting point for regional AI policy in developing countries.