Integration of generative artificial intelligence into the knowledge management and decision-making processes of knowledge-based startups
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Generative AI has experienced increasing adoption and integration into organizational processes over recent years, including incorporation into knowledge management processes and as a supportive tool for organizational decision-making. This thesis examines how generative AI is integrated into organisational knowledge management and decision-making processes of knowledge-based startups, as well as the implications that this integration has on decision-making approaches employed by startups. This study was conducted as a qualitative study, utilizing semi-structured interviews for the data collection. A total of nine interviews were conducted with decision-makers in nine distinct startups operating in knowledge intensive industries. The data was codified and analysed using thematic analysis.
The results indicated that AI was integrated into knowledge management processes through AI-supported knowledge acquisition, storage, transfer and application. In addition, AI had been integrated to support exploratory, structured, and procedural decision-making. The study further explored the approaches through which decision-makers engage with generative AI and their individual decision-making styles and found evidence that AI can support hybrid entrepreneurial decision-making approaches through causation or effectuation, and its integration can reinforce or complement the existing decision-making style of the user.
The study provided further understanding into how AI is reshaping theoretical domains such as knowledge conversion processes and influencing decision-making processes in startup contexts characterized by environmental uncertainty and resource constraints. Boundary conditions such as an individual’s level of expertise, knowledge base, and the decision-making domain were also found to influence decision-makers approaches to AI integration.