In the context of generative AI, this study examines at how customer service is changing across sectors. Researchers determine the benefits and drawbacks of generative AI's influence on productivity, client happiness, and worker dynamics by examining secondary data. The results highlight the necessity of customized strategies including moral concerns in maximizing the potential of generative AI in enhancing customer service. This study adds knowledge that is essential for businesses managing the interface between AI technology and customer service.
This study investigates the significant effect of generative AI on the automation of commercial processes. The paper analyses the advantages and disadvantages of generative AI in simplifying processes across several industries, drawing on a wide range of secondary data sources. Enhanced effectiveness, cost savings, increased accuracy, scalability, as well as flexibility have been cited as key strengths. Strong governance structures are required since the incorporation of generative AI presents ethical and privacy issues. Aspects like the effects on the workforce, data security, and industry-specific issues all stand up as important determinants of the adoption of generative AI. The research presented here advances our knowledge of how Generative AI could potentially use to transform company operations.
The article outlines the scope of the issues surrounding the use of ChatGPT as a tool for creating employer branding. It is presented how the artificial intelligence tool can be used in external activities. The aspect related to digital competences, which are very important in the light of ChatGPT, is also taken into account. The research objective of the article is to assess the impact of modern digital tools based on artificial intelligence for employer branding, i.e., employer branding. Equally important is the use of digital competences, which contribute to the application of more advanced digital solutions. In order to address the research problem, a pilot quantitative study was conducted on a random sample of respondents. The research showed that there is low awareness of the existence of modern artificial intelligence tools and, consequently, the strengthening of employer branding activities through the use of innovative solutions. On the other hand, respondents believe that digital competences, i.e., knowledge, skills and experience to be used in the future, are of increasing importance. The research showed links between employer branding, digital competences and ChatGPT. A limitation of the research is the random selection of respondents, as well as the very nature of the pilot study.
This study focuses on content generation, and personalization, including ethical issues as it examines the effects of generative AI on marketing techniques. Through a thorough analysis of secondary data, researchers pinpoint trends and conclusions. Improved content quality as well as personalized client experiences are made possible by generative AI. However, it is important to address ethical issues like prejudice and privacy. These results highlight the necessity for marketers to take advantage of AI responsibly.
As kidney chronic disease is nowadays widely increasing which either caused by kidney disease or reduce the function of the kidney, it also affects the cardiac problems- scientifically which can lead to sudden heart attacks at the end-stage. Early diagnosis and adequate therapies can only help in stopping this disease, where dialysis and kidney transplantation is the only way to save the life of the patient. Detecting kidney disease through machine learning and through data mining techniques which can reveal the hidden problem of the kidney. Therefore, the current article is based on the comparative study using various Machine Learning techniques to detect kidney disease. This survey supports to find the accuracy of algorithms which are more useful to find the kidney disease in early stage. The comparative study of all the algorithms and by implementing the models on different platforms, and it is analyzed that which is the best algorithm to predict CKD (Chronic Kidney Disease). The machine learning techniques are compared like Probabilistic Neural Network (PNN), Multilayer Perceptron Algorithm (MLP), Logistic Regression (LOGR), Regression Tree (RPART), Support Vector Machine (SVM) and Radial Basis Function (RBF).