As machine learning continues to reshape enterprise workflows, the focus has expanded beyond model accuracy to broader concerns, including interpretability, fairness, and responsible AI governance. Organizations across industries—from telecom and finance to healthcare and retail—are increasingly expected to justify, audit, and govern the decisions made by machine learning systems. This shift marks a new phase in the maturity of AI adoption: one where trust, transparency, and accountability are no longer optional—they are essential.