This study explores the adoption and usage of AI-powered data analytics in Small and Mediumsized Enterprises (SMEs) in the process of internationalization. While many AI-integrated tools are available in the market, SMEs have not fully leveraged their utility for strategic market insights, particularly in the context of international expansion.

This thesis attempts to fill the research gap in knowing how SMEs use AI-powered data analytics in producing strategic support for internationalization and fostering their general operational productivity. The practical deployment of AI-powered data analytics and business intelligence platforms is demonstrated through 12 IT SMEs in a qualitative multi-case study across seven countries.

The results revealed that the applications of AI mainly support accelerated market research, customer segmentation, price-setting, competitive analysis, and process automation, to name a few. However, it is not always consistently applied and is hindered by cost, technical skills, and integration challenges. Drawing on Diffusion of Innovation Theory, the TOE Framework, the Uppsala Model, and Resource-Based View theories, it is outlined as a result of this research how AI becomes a strategic enabler for SMEs supported by committed leadership and a certain level of digital maturity. It gives practical suggestions on how to close the gap between AI's potential and its actual use in SMEs' global strategy.