(untitled) (Journal of Informatics Education and Research, 2025)
The integration of Business Analytics (BA) and Artificial Intelligence (AI) is transforming strategic decision-making in contemporary businesses. The pace, scale, and diversity of today’s corporate data environment make traditional analytics inadequate. Real-time interpretation and predictive modeling are made possible by AI technologies like machine learning, deep learning, and natural language processing, which boost competitiveness and agility. Using randomly selected datasets from four industries—retail, banking, healthcare, and manufacturing—the qualitative literature evaluation was paired with quantitative analysis. Python was used to simulate AI algorithms and assess how they affected key performance metrics for strategic decision-making. In every area examined, AI-driven analytics improved operational effectiveness and decision quality. With a 12% boost in revenue, AI has elevated consumer segment analysis in retail. 36% of fraud is successfully detected by the finance department. Predictive maintenance worked exceptionally well, lowering production downtime by an additional 16%, while its applications in healthcare provided almost a 20% boost in diagnostic accuracy rate. Agility, precision, and limitless consumer insights are just a few of the significant ways that AI varies from traditional analytics. Although there are several additional problems, such as algorithmic bias, data privacy, significant implementation costs, etc., the benefits greatly exceed the drawbacks. In order to enable sustainable adoption and value generation acquisition for business concerns, this research suggests developing ethical and explicable frameworks for an AI system.