The integration of artificial intelligence (AI) and big data technologies is transforming traditional business management practices. This study examines how AI-driven innovations improve decision-making, operational efficiency, and organizational adaptability in large enterprises. The paper examines the application of AI across various managerial domains, such as supply chain optimization, customer engagement, performance evaluation, and strategic planning, and identifies key opportunities for technological advancement, while also addressing critical implementation challenges. These include limitations in infrastructure, data integration, organizational culture, and workforce readiness. Through a conceptual framework supported by literature analysis and algorithmic modeling, the study describes how intelligent technologies can support real-time decision-making, automate complex processes, and support innovation. The paper also proposes a model for enterprise management that integrates cloud computing and neural network-based error correction for financial data, emphasizing the potential of AI to reduce operational risks and improve analytical accuracy. Overall, the research aims to contribute to a clearer understanding of AI’s strategic role in business management and offer a forward-looking perspective on its future development within enterprise systems. The study proposes a cloud-based enterprise management model integrated with a convolutional neural network (CNN)-based financial error correction mechanism, and the empirical evaluation shows strong anomaly-screening performance on the study dataset relative to the included classical baselines. In the empirical component of this study, “error correction” is operationalized as the detection and prioritization of abnormal financial-record patterns associated with default-risk outcomes, enabling audit and review workflows inside enterprise systems rather than automatic modification of ledger entries.
Bndyan et al. (Thu,) studied this question.