ASYMMETRIC EVOLUTION OF ARTIFICIAL INTELLIGENCE IN BUSINESS MANAGEMENT: TRENDS AND CHALLENGES 2020-2024

The rapid advancement of artificial intelligence technologies has generated significant transformations in social and business processes globally. However, the adoption and implementation of AI in organizational contexts demonstrates considerable geographic, sectoral, and temporal heterogeneity. Developed economies have focused on optimization and strategic applications of AI, while developing economies face foundational barriers including technological infrastructure limitations, talent scarcity, and institutional constraints. This research responds to three critical gaps in the literature: (1) the lack of comprehensive analysis of how AI research priorities have evolved temporally, (2) insufficient documentation of asymmetric adoption patterns between developed and developing regions, and (3) limited understanding of the specific barriers constraining AI implementation in emerging markets and SMEs. METHODOLOGY This systematic review analyzed 263 articles from Scopus (2020-2024), applying PRISMA 2020 guidelines. Through iterative filtering based on publication type, temporal relevance, and thematic alignment, 30 studies were selected for detailed analysis: the top 10 most relevant articles from each of three periods (2020-2022, 2023, 2024). Analysis was conducted using Biblioshiny software, employing Spinglass algorithm for network clustering to identify thematic co-occurrence patterns and temporal shifts. Thematic coding categorized findings into three dimensions: AI application trends, mechanisms of strategic impact, and implementation barriers. KEY FINDINGS 1. Temporal asymmetry: Research emphasis shifted from operational efficiency (2020-2022) to predictive analytics (2023) to decision-making and risk management (2024). 2. Geographic asymmetry: China and India have displaced the United States in international collaboration on AI research, while Latin America remains marginalized with persistent adoption barriers. 3. Implementation gaps: Developing economies emphasize foundational challenges (infrastructure, talent, regulatory frameworks), while advanced economies pursue strategic optimization. RESEARCH IMPACT: This analysis contributes to understanding how geographic context shapes both AI research agendas and organizational capacity for technology adoption. Findings support development of context-sensitive AI policies and targeted capacity-building initiatives for emerging markets and SMEs. CITE: Muñoz Bonilla, H. A., & Espinosa Rodriguez, M. A. (2026). ASYMMETRIC EVOLUTION OF ARTIFICIAL INTELLIGENCE IN BUSINESS MANAGEMENT: TRENDS AND CHALLENGES 2020-2024 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.19141425 These data are made available under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). You are free to share and adapt the data, provided you give appropriate credit, indicate if changes were made, and do not apply additional restrictions. This dataset supports open science principles and enables researchers to verify findings, conduct secondary analyses, and extend research on AI adoption, business innovation, and regional technology disparities.