The power of artificial intelligence in decision making and various other industrial applications is well known by researchers and practitioners. However, the use of artificial intelligence in risk assessment, particularly in the context of textile SMEs, is still limited. This study examines the existing literature on SME innovation experiences and aims to highlight the extent of the analytical framework to determine the effectiveness of AI-based implementation. The purpose of this study is to analyze the process of establishing an AI-based risk profiling (AHP) model in a phased investment setting of a private textile enterprise in Uzbekistan. The study adopted a quantitative research design and was supported by PLS-SEM as the modeling and analytical tool. A total of 150 SME managers and experts participated in this study. Multi-criteria analyses and pairwise comparisons were conducted on investment decision factors in the context of a textile SME sector from the year 2015 to 2024. The findings show that the AI-based selected strategies enhance higher-order thinking skills when making decisions, particularly for strategies that include the general framework in implementing higher-order thinking skills for risk evaluation and the major criteria at each stage of its application. Meaningful investment decisions must integrate analytical skills to evaluate uncertainty and manage risk among SMEs. This is considered an important skill in innovation-driven learning. This study highlighted some contributions to the understanding of the technological, organizational, environmental, and analytical factors on the implementation of an AI-based framework in the textile sector.
Rashidov Jamshid (Wed,) studied this question.