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AI in Customer Service: Argument Flaws

Flaws in arguments can be a problem that affects the credibility and validity of individual studies and authors’ assessments due to erroneous or partially incorrect judgments caused by the misinterpretation of the existing rationale or subjective arguments. As an article for evaluation, the study by Prentice and Nguyen (2020) will be analyzed, which examines the role of artificial intelligence (AI) in client retention and customer service enhancement. As the authors state, today, AI is a tool that aims to retain customers and create convenient methods to maintain interest in specific services (the Australian hospitality industry) by affecting emotional intelligence (Prentice & Nguyen, 2020).

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However, following the presented argument, despite the convenience and workability of this engagement strategy, a significant part of the study participants advocate retaining employee service and approve of only a few factors in favor of AI (Prentice & Nguyen, 2020). Despite the value of the study from the perspective of evaluating a relevant topic, this argument contains several flaws that can be classified as base rate fallacies, in particular, a focus on few assessment variables, the subjectivity of evaluating innovative developments, as well as the limited functionality of the technology in question to draw comprehensive conclusions. This paper aims to present the aforementioned flaws from the standpoint of counterarguments and justify the existing fallacies by drawing on the current academic findings.

AI in customer service can be utilized as a tool to address different aspects of targeted customer service due to numerous many advantages over employee engagement. In their study, Prentice and Nguyen (2020) emphasize several comparisons of the variables on which their argument is built, in particular, responsiveness, reliability, empathy, and assurance. However, this approach limits the range of variables that need to be taken into account to evaluate the usefulness of AI in customer service. According to Huang and Rust (2021), interaction with the target audience by using artificial intelligence technology may be based on various interaction algorithms, including not only emotional intelligence but also thinking and mechanical mechanisms.

By evaluating the different nature of variables for assessment, Huang and Rust (2021) analyze additional types of target clients’ engagement and retention and remark that, by applying, AI, distinctive customer interaction methods can be promoted based on the development strategy of a particular organization. Emotional perception is taken as a basis, but in addition to this variable, other nuances should also be considered. As a result, in Prentice and Nguyen’s (2020) research, the focus on a small number of variables does not give a chance to assess the full range of the capabilities of the respective technology and prevents a comprehensive evaluation of its characteristics and capacities.

The evaluation of AI in customer service should be conducted not only based on individual perception but also with the help of real performance indicators to obtain a comprehensive picture of the relevance of this technology in the area under consideration. In their article, Prentice and Nguyen (2020) draw on a fairly large sample, but the background of their research lies in the subjective analysis of the functions of artificial intelligence, which, in turn, may depend on study members’ personal preferences. According to the authors, more than half of the participants involved do not belong to the young population, which may be associated with insufficient experience in operating innovative technologies and digital devices (Prentice & Nguyen, 2020).

Gursoy et al. (2019) provide a detailed assessment of AI in customer service by presenting detailed demographic characteristics of the sample engaged and measurement features that include emotional perception, performance expectancy, and other relevant parameters. Prentice and Nguyen (2020), in turn, measure the experience of Australian hotel guests, which narrows the potential range of objective assessments and increases the risk of bias due to limited target audiences. The flaw lies in insufficient data on how AI is applied by consumers of diverse demographics and which performance aspects are of most interest. Thus, the subjectivity of the assessment is the flaw to take into account.

In the field of customer service, the AI ​​functionality can be deployed and include various options and capabilities to implement to attract and retain the target audience. Prentice and Nguyen (2020) examine the emotional perception of this technology as a primary criterion for evaluating its capacities by customers. However, according to Libai et al. (2020), when pursuing customer relationships, service providers need to pay attention to the multiplicity of functions that AI possesses. For instance, the authors argue in favor of compiling retention algorithms based on remembering clients’ data, habits analysis, purchasing behavior, and other relevant criteria (Libai et al., 2020).

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In addition, Prentice and Nguyen (2020) overlook a significant aspect of utilizing AI on social media as one of the main shopping platforms today. Targeted advertising, a facilitated search for goods and services, and many other options are available to consumers due to the modern technologies of engagement and retention through artificial intelligence. The focus on emotional perception limits the scope of the analysis and is a flaw that affects the credibility of the argument. Therefore, a comprehensive assessment of functionality makes it possible to avoid the limitations of conclusions and expand the outcomes of the study of AI perception by target consumers.

Based on the assessment of the reviewed study on the role of AI in the field of customer service, the key flaws in the argument have been highlighted: few evaluation variables, a subjective assessment of the technology’s capabilities, and a limited range of its functional capacities. They are categorized as base rate fallacies because conditional probabilities are taken into account instead of prior probabilities. To strengthen the argument, additional aspects of AI’s role in customer service should be provided.

In particular, more evaluation variables need to be considered but not just emotional perception. The limitation of sampling from a demographic perspective can be addressed by the broader coverage of the topic rather than just one industry within one country. Analyzing AI’s comprehensive ​​functionality can provide sufficient arguments for a variety of options to implement to attract and retain customers.


Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169. Web.

Huang, M. H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30-41. Web.

Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51, 44-56. Web.

Prentice, C., & Nguyen, M. (2020). Engaging and retaining customers with AI and employee service. Journal of Retailing and Consumer Services, 56, 102186. Web.

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