Entrepreneurs operate in unpredictable environments, where informed decision-making is essential for survival and growth. While the use of artificial intelligence in business continues to expand, little is known about how startup founders apply these tools and what shapes their use in practice. This thesis examines entrepreneurial decision-making through the lenses of causation and effectuation logics, as described by Brettel et al. (2012); Reymen et al. (2015); Sarasvathy (2001) and addresses two main research questions: how AI supports decision-making in startups, and what factors lead to AI-supported decision-making.
An exploratory qualitative approach was used, relying on semi-structured interviews with startup founders. The data was analysed with the Gioia method to identify patterns and themes. This approach allowed to connect theoretical frameworks with real-world practices, and for examining whether entrepreneurs with different decision-making logics apply AI in distinct ways.
The findings revealed key barriers and success factors of AI use in startups, showing that adoption is not driven by a single cause but by a combination of personal and contextual elements. The study confirms that AI can enhance both causal and effectual decisions, with human oversight still playing a central role. As a result, the line between the two logics becomes blurred, which suggests a more mixed and flexible approach.
This research adds to theory by connecting how entrepreneurs make decisions with how they adopt AI tools. It offers practical insights for entrepreneurs, incubators, investors, and policymakers who aim to understand what drives effective and responsible AI use in startups.