A critical review of state-of-the-art explainable artificial intelligence (XAI) methods and their business applications

Abstract

With the growing adoption of AI in high-risk business sectors such as finance and healthcare, ensuring transparency, explainability, and controllability of AI decision-making has become increasingly critical. Traditional “black box” models, particularly deep learning and large language models, pose challenges to trust, compliance, and operational reliability. In response, explainable artificial intelligence has emerged as a key research area bridging technological innovation and practical business needs. This article systematically examines the classification, mechanisms, and evolution of mainstream XAI methods, with a focus on both technological development and sustainable business applications. Guided by two core research questions, we analyze model-specific and model-agnostic advancements, summarize approaches in terms of faithfulness, cost-effectiveness, stability, and operational applicability, and analyze their adaptability in key industries such as finance and healthcare. Finally, we identify current challenges and future development trends in XAI, offering practical recommendations for building trustworthy, transparent, and sustainable business AI systems.