This thesis is positioned within the intersection of social entrepreneurship and artificialintelligence research streams and explores a timely topic and emerging phenomenon. Whileresearch has started paying attention to the role of artificial intelligence in various aspects oforganizational life, we turn our attention to how social entrepreneurs interact with artificialintelligence to achieve their social missions. We conduct a qualitative study and use aninterpretive research paradigm with an inductive approach to enrich the current understandingof the role of artificial intelligence in shaping processes through which social entrepreneursattempt to accomplish their social mission. Our primary data are collected through semi-structured interviews with social entrepreneurs who are located in different countries andemploy artificial intelligence in their daily work. The analysis revealed that social entrepreneursengage in creating new relations or building on existing relations to enable AI in four maindimensions during the social entrepreneurial process: AI implementation and management; AIdesign and development; AI ethics and openness; and community and collaboration. To betterunderstand the role of those dimensions, we discuss those findings in light of a framework thatis developed to further understand the processes through which social mission is achieved. Thefindings contribute to the existing body of literature on both social entrepreneurship and AI,providing a comprehensive understanding of the role of AI in social entrepreneurship, andoffering practical recommendations for social entrepreneurs who want to exploit AI. Further,they demonstrate how and where AI can be a powerful entity for social entrepreneurs inaddressing social and environmental challenges. However, we also shed light on thecomplexities and challenges that are inherent in AI management and showcase that AI is not asilver bullet to address social objectives.

While the proliferation of AI technologies offers opportunities for the workplace and its processes, their implementation in business effectively is still a challenge. Today, companies require strategic guidance in their AI management. Accordingly, there is a need for more research on the topic with a holistic approach including governance of data. Considering the challenges of the private sector and the gap in the IS research, this thesis focuses on the barriers to implementing AI in the private sector. It specifically assesses the sociotechnical mechanisms for AI evolution in the case of the agricultural machinery industry. The conducted case study suggests an overall approach including data governance for AI implementation and an alignment between the digital, it, and business strategies. Based on the research findings, this study suggests a model for AI management with three parts: The opportunities and the new data generation to realize these opportunities lie on the benefit side of the digital transformation while the sociotechnical mechanisms to tackle the barriers stand at the core. By introducing a model for AI management, the thesis offers a roadmap for the case company while bringing a new perspective to the literature and further research.

The launch of the chatbot ChatGPT in November of 2022 has sparked a fierce debate on how AI tools will affect future education. Currently, there have been multiple articles about how universities and educators do not yet have an action plan on how to respond to the consequences of the launch of the chatbot. The technology behind ChatGPT is generative AI and is anticipated to be the next big digital disruption. Generative AI and its effects on higher education is still unexplored territory. In this thesis we aim to answer the following research question: how can generative AI be managed as a digital disruption in higher education? We conducted a qualitative case study at a university in Sweden, where the participants were educators from different departments. The three main themes discussed in this thesis are: disruptive effects of generative AI; three elements of managing digital disruption; emerging challenges and opportunities. We contribute to existing research by providing a model on how the digital disruption caused by generative AI manifests in higher education and provide suggestions on how to manage it. Further, we suggest that future research add students’ perspective to the model, since this was not covered in this thesis.

New and innovative technology has changed the way we do things in all parts of our lives. Intelligent machines that can mimic human cognition and exceed its brainpower are on an uprise in all domains of the society we live in today. The concepts of Artificial Intelligence and Machine Learning is increasing in popularity, especially in the field of medical practice and research. Areas such as radiology have come a long way in using AI and ML to diagnose and decide on treatments but many other areas in healthcare are still behind in the research and usage of this technology. This study aimed to investigate AI management in relation to medical professionals and their current professional practice. This was done through a case study format of a project currently developing an AI application for future use in primary healthcare. Interviews with project members and physicianswere conducted and provided a foundation for analysis. The thesis shows that the current understanding of AI and its application is limited amongst the professionals, and the attitudes and trust are highly individual. Our conclusions highlight the need for a holistic view of the management of AI in healthcare, where perspectives from all actors involved in the change need to be raised and utilized in future developments and implementations.