Building Trustworthy AI for Enterprises: A Framework Balancing Explainability, Compliance, and Performance

Artificial Intelligence (AI) is no longer merely a futuristic idea; it has subtly made its way into supply chains, bank branches, boardrooms, and hospitals. Although AI is capable of processing massive amounts of data and making predictions more quickly than any human, there is a significant issue: no one truly understands how it comes to its conclusions. This is frequently referred to as the "black box" issue. This lack of openness is a major obstacle in corporate and regulatory settings where actions need to be justified, explained, and audited. An new field called Explainable AI (XAI) aims to address this issue by improving the interpretability and human-friendliness of AI systems.

This article offers businesses a useful foundation for integrating XAI into their primary decision-making procedures. We show that XAI greatly enhances trust, compliance readiness, and operational efficiency using a mix of conceptual analysis, a comparative examination of XAI tools (SHAP, LIME, and ANCHOR), and a real-world inspired case study from the banking industry. According to our research, companies can have both accuracy and explainability with the correct framework.