Enhancing Strategic Decision-Making with AI-Powered Business Analytics

Abstract

Strategic decision-making in modern enterprises is being transformed by the combination of Artificial Intelligence (AI) and Business Analytics (BA). Traditional analytics are insufficient in today's business data environment due to its pace, scale, and variety. Artificial intelligence (AI) technologies including machine learning, deep learning, and natural language processing provide real-time interpretation and predictive modelling, which increase agility and competitiveness. The qualitative literature study was combined with quantitative analysis using randomly chosen datasets from four industries: manufacturing, banking, healthcare, and retail. AI algorithms were simulated in Python, and their effects on important performance indicators for strategic decision-making were evaluated. AI-driven analytics enhanced decision quality and operational effectiveness in every area studied. AI has improved customer segment analysis in retail with a 16% increase in revenue. The finance department successfully detects 48% of fraud. Predictive maintenance performed very well, reducing production downtime by an extra 18%, while its use in healthcare increased diagnostic accuracy rates by nearly 35%. AI differs significantly from traditional analytics in a number of important areas, including agility, accuracy, and infinite customer insights. The advantages far outweigh the disadvantages, despite a number of other issues, like algorithmic bias, data privacy, high implementation costs, etc. This research proposes the development of ethical and explicable frameworks for an AI system to facilitate sustainable adoption and value generated acquisition for commercial concerns.