Generative Artificial Intelligence (GAI) is emerging as a promising tool with which to improve strategic decision-making in a business environment characterized by increasing complexity. There are external and internal factors that are part of the success of entrepreneurial initiatives. Relevant factors that make decision-making effective include the technological environment, as an external factor, and innovation, as an internal factor. Methods: This study reviews the existing literature on implementing GAI in business decision-making. It assesses its short-, medium- and long-term effects, considering the interaction between GAI and human judgment. Challenges related to uncertainty, complexity, and ambiguity are examined, and the relevant literature is reviewed to understand these aspects comprehensively. Results: The review shows that, despite the advanced capabilities of GAI to analyze data and generate patterns, human judgment remains crucial in situations of high uncertainty. The results suggest that combining GAI with human expertise can improve the accuracy and efficiency of strategic decision-making by integrating the strengths of both parties. Conclusions: The implementation of GAI can offer significant improvements in the efficiency and accuracy of business decisions. However, human judgment and experience remain essential, especially in uncertain contexts. The key to maximizing the benefits of GAI lies in finding the right balance between artificial intelligence and human capital.

This study investigated the key determinants of entrepreneurship in Ambato, Ecuador, through the application of predictive statistical models. The primary objective was to identify critical variables that influence the success and sustainability of entrepreneurial ventures in the region. A quantitative research design was employed, utilizing multivariate statistical techniques, including decision trees, random forest, and Principal Component Analysis (PCA). Data were collected through structured surveys administered to local entrepreneurs, addressing variables including demographics, education, initial investment, and business operations. The analysis revealed that factors such as geographic location, the entrepreneur’s educational attainment, business tenure, and operational characteristics have a significant influence on entrepreneurial viability. On the basis of these findings, the study proposes a predictive model to assist both policymakers and entrepreneurs in evaluating and enhancing business development strategies. The insights gained offer practical implications: entrepreneurs can refine their strategies for greater effectiveness, while local authorities can craft supportive policies, such as training programs and improved access to financing. The study highlights the importance of entrepreneurship education and targeted support for underdeveloped areas, thereby contributing to broader economic inclusion and enhanced employment opportunities in the region.