Enhancing Organizational Decision-Making through Explainable AI (XAI) Capabilities: A Systematic Literature Review

Abstract

The growing adoption of artificial intelligence (AI) in organizational and tactical decision-making has intensified concerns about transparency, accountability and the changing role of human decision-making in high-risk decision environments. Explainable artificial intelligence (XAI) has developed as a prominent approach for improving the explainability of AI-assisted decisions, however existing research remains fragmented across technical explainability, user reliance and moral governance perspectives. To address this gap, this study conducts a systematic literature review of 28 peer-reviewed studies published between 2020 and 2026 to examine how XAI is conceptualized and applied within organizational decision-making contexts from a human-centric perspective. Using the PRISMA framework and inductive thematic synthesis, the findings indicate that XAI is predominantly positioned as a transparency-and explanation-oriented decision-support capability that supports human sensemaking, oversight, and calibrated trust rather than autonomous decision-making. The reviewed literature primarily focuses on managerial decision contexts, while strategic governance, board-level decision-making and organizational accountability remain relatively underexplored. This study adds to the literature by developing a conceptually based Human-Centric XAI Maturity Framework that explains how explainability progressively evolves from interpretative decision support into an institutionally embedded organizational governance capability.