KI-gestützte Risikoprävention in SAP S/4HANA-Migrationsprojekten. Entwicklung eines Konzepts zur Vermeidung und Beherrschung kritischer Einflussfaktoren mittels Experteninterviews

This dissertation aims to develop an AI-supported risk management concept to support SAP S/4HANA migration projects. Given the need to replace existing and outdated ERP systems by 2033 at the latest, companies are facing complex challenges. Previous studies and case studies show a high failure rate for ERP projects. These are often due to inadequate planning, lack of acceptance on the part of specialist departments and unclear responsibilities throughout the project environment. The aim of this work is to systematically identify risks and establish measures for risk prevention and hazard control based on a practical, AI-based risk prevention system. The theoretical framework is based on the Unified Theory of Acceptance and Use of Technology (UTAUT). In particular, the acceptance and use of the developed solution must be evaluated. In the empirical part, twelve qualitative, guided expert interviews were conducted. The evaluation was carried out using a structured categorisation in accordance with qualitative content analysis based on Kuckartz. Key risk factors were identified in categories such as project management, technology, business processes, data quality and change management. In addition, these findings were linked to the UTAUT dimensions (e.g. performance expectations, effort expectations, social influence) and systematically evaluated. Based on these findings, a design-led reference model was developed that describes an AI-supported risk management system. For clarity, an exemplary management dashboard, parameterisation approaches, decision-making logic and an evaluation scheme for determining maturity and effectiveness were formulated. Predictive analytics and machine learning are used in this process. The central goal is to identify patterns in the project data fed into the system at an early stage and to provide decision-makers with well-founded recommendations to ensure the success of future SAP S/4HANA projects. The dissertation concludes with specific recommendations for action in research and practice. It shows how AI can be integrated into ERP migration projects in order to systematically minimise risks. This research work combines theoretical foundations, empirical data collection and practical implementation. Through this integration, the work makes an innovative contribution to the professionalisation of risk management in digital transformation processes.