Digital transformation is the process of consuming digital technologies that drives business improvements and customer experience. Artificial intelligence plays a crucial role in businesses' digital transformation agendas. Technologies and algorithms are an important perspective in the implementation of chatbots. AI chatbots use natural language processing technology and offer solutions for modernizing traditional business processes. The key advantage of the implementation of chatbots in different domains is the impact by improving customers' experience and reducing costs. Chatbots are pieces of software that simulate human conversation through voice commands and/or text chats, intending to offer companies an approach on how to use this software to revolutionize their businesses. The aim of this paper is the analysis of the evaluation criteria; the study of insights into how chatbots can be implemented in the domains of banking, e-commerce, tourism, and call centres; and the discussion of some benefits and challenges of chatbots in driving the digital transformation of businesses.

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Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is the analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey.

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