EXPLAINABLE AI MODELS FOR BUSINESS INSIGHTS: LEVERAGING AZURE ML AND INTERPRETABLE TECHNIQUES

This article examines the implementation of explainable AI (XAI) models within Azure Machine Learning for generating business insights.The article addresses the critical challenge of AI model transparency in business contexts, particularly focusing on small and medium-sized enterprises.The article investigates how interpretable machine learning techniques, including SHAP and LIME, can enhance stakeholder trust and model adoption in business environments.The article demonstrates that implementing structured XAI frameworks significantly improves decision-making processes and operational efficiency across various sectors.By analyzing implementations in healthcare, manufacturing, and financial services, the article establishes that combining local and global explanation methods with proper visualization strategies leads to enhanced model interpretability and stakeholder acceptance.The article suggests that organizations can achieve substantial improvements in AI adoption and utilization through properly implemented explainable AI frameworks, particularly when technical explanations are effectively translated into business-relevant insights.