## 5 Discussion  
The obtained results from both quantitative and qualitative research illustrate the potential for implementing ChatGPT within the scope of this study. It is noteworthy that the collection of 405 cases from the after-sales process is significant from the perspective of organizations in the automotive sector. The level of complexity of the after-sales process aligns with observations made by other authors [36], who analyzed it from a process-management perspective. Expanding on other studies, it has been indicated that the examined process consists of several subprocesses (e.g., the customer acceptance process, fault diagnosis and verification process, repair process, part-ordering process, technical documentation verification and technical-department contact process, quality-control process, and the warranty service process). This implies that while the process is highly standardized on one hand, on the other hand, as evidenced by the conducted process-mining study, numerous deviations are observable, necessitating consideration from various perspectives. In this article, the focus was on the time of execution, but equally significant are aspects of efficiency and effectiveness, as described in the work by [PERSON] et al. [37]. It is worth emphasizing that the result of these parameters is the aspect of customer satisfaction, a key metric from the perspective of the investigated issue [38]. From the customer's standpoint, prolonging the after-sales process extends the lead time, consequently resulting in waiting time becoming an unnecessary cost of service delivery for the customer. The aspect of customer satisfaction in the after-sales process is not only emphasized in this article but is widely discussed in the literature as a cognitive object aimed at improving service provision [39; 40; 41; 42]. This underscores the potential for transformation toward an end-to-end approach. According to [PERSON] and [PERSON] [43], an end-to-end business process focuses on the high-leverage aspects of an organization's operations to gain a competitive advantage and achieve better results. In the author's perspective, the set of activities in question can be defined as a \"from-complaint-to-claim\" process, particularly concerning repairs performed under the manufacturer's warranty, while other repairs entail a \"from-complaint-to-cash\" process. The starting point for modeling such a process, which marks the orientation of further research, is the happy path shown in Figure 4, serving as a reference representation of the process explored in this article. The issue of reliability presents a significant knowledge gap. Through the literature review, only a small number of publications dedicated to this topic were identified. Three distinct studies were conducted to investigate the reliability of ChatGPT. The first study, conducted by [44], aimed to measure the reliability of ChatGPT in a general question-answering scenario. The study meticulously curated a set of 5695 questions from ten datasets and eight domains. The authors found that ChatGPT's reliability varies across domains, particularly underperforming in law and science questions. They also demonstrated that system roles, originally designed by OpenAI to allow users to control ChatGPT's behavior, can impact its reliability. Additionally, the study showed that ChatGPT is vulnerable to adversarial examples, and even a single character change can negatively affect its reliability in certain cases. This study provided valuable insights into ChatGPT's reliability and highlighted the need to strengthen the reliability and security of large language models. The second study, conducted by [PERSON] (2023), focused on examining the consistency of ChatGPT's zero-shot capabilities in text annotation and classification. The author emphasized that ChatGPT is non-deterministic, meaning that identical inputs can lead to different outputs, similar to human coders.