Given the increasing significance of artificial intelligence (AI) in global technology trade, this study focuses on identifying and prioritizing the primary AI applications within the software sector, characterized by rapid innovation cycles, cross-border flows of advanced technologies, and intense competitive dynamics. Utilizing the Fuzzy Best-Worst Method (FBWM), this research develops a structured decision-making framework aimed at assisting organizations involved in the international trade of digital technology platforms. As global technology trade encompasses the commercialization and worldwide distribution of often novel technologies, companies require a clear set of criteria for selecting AI applications that can enhance their global competitiveness. The key areas identified in this study include AI-driven supply chains designed to optimize global logistics; predictive analytics for accurate forecasting of international technology markets; accelerated research and development (R&D) and product innovation facilitated by AI; intelligent automation for seamless cross-border digital operations; and AI-enhanced cybersecurity to protect globally distributed technology assets. Expert insights were systematically collected and analyzed using the Fuzzy Best-Worst Method (FBWM), enabling a comprehensive prioritization process amid uncertainty. The findings indicate that predictive analytics for gaining insights into global technology markets and AI-enhanced cybersecurity are of paramount strategic importance. This underscores their vital roles in effective market positioning, risk mitigation, and maintaining trust in international technology exchanges. The proposed framework offers a detailed and transparent methodology for technology business managers and strategists seeking to navigate focused paths for AI investment and adoption within the global technology business ecosystem.