Explainable AI in Enterprise Decision Making: Bridging Transparency and Performance

Abstract

Abstract
As artificial intelligence (AI) systems increasingly inform enterprise decision-making, the need for transparency and accountability grows. While AI models offer remarkable performance, their black-box nature poses significant challenges for trust and interpretability. This paper explores the intersection of explainable AI (XAI) and enterprise decision-making, focusing on how organizations can balance the trade-off between model transparency and performance. Through an extensive literature review and in-depth case study analysis, we propose a conceptual framework to bridge the gap, offering practical insights into adopting XAI without compromising efficiency. The study contributes to both academic discourse and industry practice by guiding enterprises toward responsible, high-performing AI integration.