997 KB0 KB201020262025Dimitrov, GeorgiGeß, ChristianPetrova, Pepa Vlletnik:16številka:2Str. 1-18ISSN:1855-931XCOBISSID_HOST:260265219URN:URN:NBN:SI:doc-0JFRJPA1enGEA CollegeAdvances in business related scientific research journalEnterprise resource planning (ERP) systemsgenerative artificial intelligence (GenAI)text classificationNew approach for classifying unstructured data to understand the influence of new technologies in future ERP development|The rapid development of emerging technologies is changing the business world every day. Process automation, artificial intelligence (AI) consultants, mobile devices, cloud technologies, no-code programming, and many more are influencing the requirements for business systems. The current study aims to compare human experts and generative AI (GenAI) in classifying unstructured text to identify the emerging technologies that will have the greatest impact on the future development of enterprise resource planning systems. The basis for the comparison is statistical indicators such as the average Jaccard index and Krippendorff’s alpha, which validate the classification from both approaches.The results indicate that GenAI holds potential for process automation, whereas human classification is more closely aligned with statistical indicators. GenAI has difficulties with contextual nuances and specific categories. Hence, a four-step funnel-based framework for the efficient classification of unstructured text was developed, which integrates GenAI for initial structuring and training while emphasizing the indispensable role of human supervision for quality assurance to leverage automation while maintaining the validity of the results. This approach significantly reduces manual effort while maintaining reliable analysisTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaGEA College