@article{SchweigerDanzinger, author = {Schweiger, Emma and Danzinger, Frank}, title = {Komplexe Innovationsprozesse mit KI st{\"a}rken}, editor = {Danzinger, Frank}, address = {Augsburg}, doi = {10.60524/opus-2221}, pages = {20}, language = {de} } @misc{Schweiger, type = {Master Thesis}, author = {Schweiger, Emma}, title = {Generative AI as a Catalyst for Business Model Innovation in Complex Environments}, doi = {10.60524/opus-2040}, abstract = {Recent advancements in Artificial Intelligence (AI), particularly with the publication of OpenAI's ChatGPT, have highlighted the transformative potential of generative AI (GAI) in private and business contexts. GAI tools, capable of generating new content such as text, images, and audio from training data, are increasingly used as human assistants for innovation tasks. Furthermore, the technology is increasingly applied to solve complex tasks. Within modern business environments, where these complexities are significant influences, GAI tools act as crucial drivers for business model innovation. This research systematically analyses existing literature and employs a mixed-methods approach to empirically assess the impact of GAI tools on business model innovation occurring in complex environments. Furthermore, an intensive experiment involving ten students developing an innovative service for the context of stationary retail was conducted. Here, the goal was to develop a functioning business model, along a three-month development process including theoretical input in workshop settings. Findings from qualitative observations, prompt analyses, and a quantitative survey reveal that GAI significantly fosters creativity and innovation, especially during ideation and prototype development. However, traditional creativity methods need adaptation for effective integration with GAI. These tools also enhance decision-making by allowing innovators to navigate these complex environments, although some constraints still exist. Future experiments in controlled settings like Living Labs are recommended to ensure validity, particularly in the dynamic stationary retail sector. Addressing limitations such as sample size and validating traditional creativity methods for GAI application is essential for future experiments. In conclusion, GAI tools significantly enhance business model innovation, but human collaboration remains essential for addressing external complexities and fostering creativity, ensuring cohesive decision-making.}, language = {en} } @inproceedings{SchweigerDanzinger, author = {Schweiger, Emma and Danzinger, Frank}, title = {Navigating Pitfalls: Understanding the Factors Behind AI Project Failures in Small and Medium-sized Enterprises}, series = {European Academy of Management (EURAM) 2024 Conference}, booktitle = {European Academy of Management (EURAM) 2024 Conference}, isbn = {978-2-9602195-6-2}, issn = {2466-7498}, pages = {36}, abstract = {According to the literature, between 60- 80 \% of data projects are prone to fail. With limited financial and human resources, SMEs especially struggle to keep up with exploit ing business advantages out of data automation. Consequently, this paper asks: Why do AI projects fail in SMEs? To answer the research question, the current literature has been analysed and compared to the results of eleven exploratory interviews with managers from SMEs. The literature reveals on the one hand the specifics of AI projects. On the other hand, five classes of failure factors for AI projects have been identified: Strategy, Data \& Analytics, People \& Management, Organization and Resources. The results from the interviews highlight Strategic Management, Change Management, Digital Infrastructure, as well as the Project Approach as relevant approaches to deal with project failure in SMEs. This study enhances existing knowledge in the field of AI project failure, by providing new empirical data and integrating these findings with relevant results from previous research that has not focussed on SMEs yet. This study offers important managerial implications for SMEs planning on introducing AI into their business. These include empowerment on a technological, personnel, organizational, and governmental level, to decrease the risk of future project failure in SMEs}, language = {en} } @techreport{MarkusSchweigerKartheiningeretal., author = {Markus, Benedikt and Schweiger, Emma and Kartheininger, Tamara and Danzinger, Frank and Krupp, Michael}, title = {{\"O}kosystementwicklung f{\"u}r KI-basierte Gesch{\"a}ftsmodelle am Beispiel der Digitalisierung und Elektrifizierung des {\"O}PNV}, publisher = {Technische Hochschule Augsburg}, address = {Augsburg}, doi = {10.60524/opus-1958}, pages = {11}, abstract = {Im Rahmen des Projekts des KI-Produktionsnetzwerks der Technischen Hochschule Augsburg beleuchtet dieses Whitepaper die Integration von K{\"u}nstlicher Intelligenz in Gesch{\"a}ftsmodelle und Unternehmens{\"o}kosysteme des {\"o}ffentlichen Personennahverkehrs ({\"O}PNV). Erfahren Sie, wie KI die digitale Transformation und Elektrifizierung des {\"O}PNV vorantreibt und welche strategischen Vorteile sich daraus ergeben. Schl{\"u}sselthemen des Whitepapers: \# Analyse des aktuellen Zustands und der Herausforderungen: Einblicke in die aktuellen Gesch{\"a}ftsmodelle von Busunternehmen und die bestehenden Herausforderungen im digitalisierten und elektrifizierten {\"O}PNV. \# Innovative Gesch{\"a}ftsmodellkonfigurationen: neue Gesch{\"a}ftsmodelle, die durch die Sektorenkopplung und Elektromobilit{\"a}t im {\"O}PNV entstehen und wie diese Modelle implementiert werden k{\"o}nnen. \# Strategische Handlungsempfehlungen: Empfehlungen, um die Integration von KI-Technologien in Gesch{\"a}ftsprozesse zu optimieren und nachhaltige Wertsch{\"o}pfung zu gew{\"a}hrleisten.}, language = {de} }