The Convergence of Artificial Intelligence and Business Analytics: Trends, Challenges, and Future Research Directions

Abstract

In the era of data-driven decision-making, the deep integration of artificial intelligence (AI) and business analytics (BA) is reshaping the global business landscape. This paper systematically reviews research progress in this interdisciplinary field, proposing an integrated theoretical framework—Data-Algorithm-Insight-Action (DAIA)—that reveals how AI propels BA from descriptive and diagnostic analysis toward predictive and even prescriptive decision-making. This evolution catalyzes a new paradigm of intelligent decision-making centered on generative AI. The paper delves into three core trends: the autonomous evolution of analytical paradigms, GenAI's end-to-end restructuring of the BA value chain, and the deep embedding of AI systems within organizational processes. It simultaneously identifies critical challenges across four dimensions during integration: ethics (privacy, bias), technology (explainability, trust deficits), organization (human-machine collaboration, skill mismatches), and strategy (quantifiable ROI, governance gaps).Addressing existing research gaps and practical pain points, the paper proactively proposes four future research directions: building responsible and trustworthy AI-BA systems; deepening the cognitive and behavioral mechanisms of human-AI collaboration; exploring GenAI-driven novel business models; and developing dynamically adaptive governance and strategic frameworks. To support the implementation of these directions, the paper further emphasizes the necessity of interdisciplinary collaboration, building industry-academia-research-application ecosystems, and fostering global governance dialogues. This study not only provides academia with a systematic knowledge map and theoretical guidance but also offers business managers a decision reference that combines depth with practicality for planning digital transformation strategies. It aims to propel the integration of AI and BA toward an efficient, equitable, trustworthy, and sustainable development path.