Abstract:

As the field of Artificial Intelligence (AI) advances, the exploration of AI applications in various domains becomes increasingly vital. This research examines the readiness of AI models such as Google BARD, a conversational AI chatbot, for potential business applications, focusing on its ability to simplify data. Google BARD, an acronym-free name inspired by the traditional role of a bard as a storyteller and poet, demonstrates capabilities encompassing content generation, language translation, and information retrieval. This study aims to assess the efficacy of Google BARD's text simplification feature in catering to the demands of modern businesses. To achieve this, a dataset of 42,654 review texts from distinct Disneyland branches across different countries was employed. The chatbot's API was utilized with a uniform prompt, "Simplify: review text," to generate simplified reviews. Results presented a spectrum of responses, including successful simplifications, errors, and instances of model self-reference. Quantitative analysis encompassing
response categorization, error prevalence, and response length distribution was conducted. Furthermore, Natural Language Processing (NLP) metrics were applied to gauge the quality of the generated content. The findings offer insights into Generative AI (Gen AI) models such as Google BARD's performance, highlighting proficiency in simplifying reviews while unveiling certain limitations in coherence and consistency. The analysis presents a comprehensive picture of the AI-generated outputs, ranging from succinct and accurate simplifications to instances where the model struggles to maintain contextual relevance. This research contributes to the ongoing discourse on AI adoption in business contexts by examining the practicality of Google BARD's text simplification feature. The study's outcomes provide implications for future development and implementation of AI-driven tools in businesses seeking to enhance content creation and communication processes. As AI continues to transform industries, an understanding of the readiness and limitations of AI models is essential for informed decision-making and effective integration.