ARTIFICIAL INTELLIGENCE AND BIG DATA POSSIBILITIES FOR INVESTIGATION AND IMPLEMENTATION IN BUSINESS, ACCOUNTING, FINANCE, AND MANAGEMENT SYSTEM

The integration of Artificial Intelligence (AI) and Big Data analytics has emerged as a transformative force across business, accounting, finance, and management systems. This scoping review explores the methods, applications, and implementation strategies of these technologies, with a particular emphasis on the leather industry—a traditionally resource-intensive sector now facing increasing pressure to digitalize. The study identifies key AI techniques, including machine learning, deep learning, natural language processing, and robotic process automation, and maps their use in fraud detection, financial forecasting, customer segmentation, and decision support systems. Complementing these are Big Data methods such as descriptive, predictive, and prescriptive analytics, which enable real-time monitoring, demand forecasting, and supply chain optimization. By referencing recent literature and case studies, the review highlights practical use cases and platforms like Python, TensorFlow, Apache Spark, and IBM Watson. Applications within the leather industry are also examined, including computer vision for quality control, AI-driven ERP systems, and data platforms for sustainability tracking and compliance. While the potential benefits—such as improved productivity, cost reduction, and enhanced transparency—are significant, implementation is challenged by high costs, workforce skill gaps, and resistance to change in legacy systems. The review concludes with recommendations for future research and sector-specific frameworks to facilitate adoption in traditional manufacturing contexts. As digital transformation becomes a necessity rather than a choice, this study provides a roadmap for leveraging AI and Big Data to drive innovation, efficiency, and sustainability in the evolving landscape of business and industry.