A META-ANALYSIS OF AI-DRIVEN BUSINESS ANALYTICS: ENHANCING STRATEGIC DECISION-MAKING IN SMES

Abstract

This meta-analysis offers a comprehensive examination of how AI-driven business analytics influence strategic decision-making within small and medium-sized enterprises (SMEs), a sector often constrained by limited resources but increasingly pressured to compete in data-intensive environments. By systematically synthesizing 112 peer-reviewed empirical studies published between 2010 and 2025, this research explores the effects of artificial intelligence technologies—including machine learning, natural language processing, predictive analytics, and real-time data dashboards—on key decision outcomes such as speed, accuracy, responsiveness, and operational agility. The methodology followed the PRISMA 2020 guidelines to ensure transparency and replicability, utilizing a random-effects model to aggregate effect sizes across heterogeneous organizational contexts. The findings indicate that AI adoption significantly enhances decision-making performance across diverse business functions, with the most pronounced effects observed in marketing, financial forecasting, and supply chain operations. Moreover, the results demonstrate that AI technologies contribute not only to efficiency and precision but also to reducing cognitive biases, enhancing scenario planning, and enabling agile responses in dynamic environments. Moderator analysis reveals that medium-sized firms, those with advanced digital infrastructure, and organizations in data-intensive industries benefit the most from AI deployment. Furthermore, the integration of AI tools appears to scale more effectively in firms with greater absorptive capacity, robust data governance frameworks, and structured decision-making protocols. This study also highlights the role of AI in complementing managerial cognition by transforming complex, unstructured data into actionable insights, thereby supporting evidence-based decision cultures in SMEs. While adoption barriers such as cost, talent gaps, and technological inertia persist, the overall evidence confirms that when strategically aligned with internal capabilities, AI-driven analytics can function as a transformative enabler of strategic competitiveness. The meta-analysis offers theoretical advancement by quantifying cross-sector impacts and practical insights for SME leaders, policymakers, and researchers aiming to build intelligent, resilient, and data-literate enterprises in the era of digital transformation.