The integration of artificial intelligence (AI) in business processes has revolutionized many industries by automating tasks, improving decision-making processes, and enhancing customer experiences. This review article examines the impact of AI in business areas, including its applications, challenges, limitations, current trends, and future work. The article begins with defining the importance of AI in business, followed by an overview of its applications in various sectors, such as customer service, marketing, finance, healthcare, manufacturing, logistics, and human resources. The advantages and benefits of AI implementation are explored, along with the examples of successful AI implementation and changes in business processes. The challenges and limitations of AI technology, such as ethical concerns, data privacy and security issues, technical expertise and knowledge, and high implementation costs, are explained. Current trends in AI integration, such as the integration of AI with other technologies, the growing demand for AI skills, and the development of more advanced and sophisticated AI algorithms, are presented. The review concludes with recommendations and predictions for the future of AI in business areas and proposes strategies for successful AI implementation while reflecting on ethical and social considerations. Our analysis in this article points to various open research areas and improves the understanding of AI in the business field.

The digitalization of music has led to increased availability of music globally, and this spread has further raised the possibility of plagiarism. Numerous methods have been proposed to analyze the similarity between two pieces of music. However, these traditional methods are either focused on good processing speed at the expense of accuracy or they are not able to properly identify the correct features and the related feature weights needed for achieving accurate comparison results. Therefore, to overcome these issues, we introduce a novel model for detecting plagiarism between two given pieces of music. The model does this with a focus on the accuracy of the similarity comparison. In this paper, we make the following three contributions. First, we propose the use of provenance data along with musical data to improve the accuracy of the model’s similarity comparison results. Second, we propose a deep learning-based method to classify the similarity level of a given pair of songs. Finally, using linear regression, we find the optimized weights of extracted features following the ground truth data provided by music experts. We used the main dataset, containing 3800 pieces of music, to evaluate the proposed method’s accuracy; we also developed several additional datasets with their own established ground truths. The experimental results show that our method, which we call ‘TruMuzic’, improves the overall accuracy of music similarity comparison by 10% compared to the other state-of-the-art methods from recent literature.

Generative Artificial Intelligence (GenAI) is rapidly transforming the beverage industry by fundamentally reshaping innovation pipelines, optimizing product development processes, and redefining consumer engagement strategies. This comprehensive review synthesizes evidence from 40 academic and industry studies to systematically map GenAI’s current applications, critically evaluate prevalent implementation challenges, and identify future opportunities for growth. Our key findings reveal that advanced GenAI models, including transformer architectures, generative adversarial networks (GANs), and multimodal AI systems, are driving significant advancements across three core areas: (1) Product Innovation, where AI accelerates processes such as flavor profiling, recipe optimization, and virtual prototyping, exemplified by AI-designed non-alcoholic spirits and precision-engineered reduced-sugar formulations; (2) Operational Efficiency, demonstrated through IoT-integrated AI enhancing quality control, facilitating predictive maintenance, and reducing waste in complex processes like brewing and fermentation; and (3) Marketing Personalization, leveraging tools like ChatGPT and DALL·E for dynamic content generation, granular sentiment analysis, and the execution of hyper-targeted campaigns.

The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures.

Journal of Information Processing Systems