AI-Powered Business Analytics: Optimizing Operational

EasyChair Preprint 14889

Abstract

In an era of digital transformation, AI-powered business analytics has emerged as a crucial tool for optimizing operational performance across industries. This paper explores the application of artificial intelligence (AI) in enhancing business decision-making by analyzing large datasets, identifying trends, and providing actionable insights. Leveraging AI-driven techniques, such as machine learning, natural language processing, and predictive analytics, businesses can streamline operations, reduce costs, and improve efficiency. By automating routine tasks and enabling real-time data analysis, AI-powered solutions offer unparalleled accuracy and speed in identifying inefficiencies and opportunities. The study also highlights the role of AI in predictive maintenance, supply chain optimization, customer behavior analysis, and financial forecasting. Through case studies and empirical research, the paper demonstrates how companies that integrate AI into their business analytics framework achieve superior operational outcomes, fostering innovation and competitive advantage. Finally, the ethical considerations, challenges, and future trends in AI-powered business analytics are discussed to provide a comprehensive view of its transformative potential in optimizing performance and driving sustainable growth.

Keyphrases: Artificial Intelligence, Automation, Business Analytics, Decision making., Operational Performance, Predictive Analytics, machine learning

@booklet{EasyChair:14889,
  author    = {Anthony Collins},
  title     = {AI-Powered Business Analytics: Optimizing Operational},
  howpublished = {EasyChair Preprint 14889},
  year      = {EasyChair, 2024}}