AI jako spoluinvestor: vývoj inteligentního systému pro rizikové investování

The growing volume of investment opportunities in venture capital creates a need for more efficient approaches to their analysis. Investors face information overload and cannot apply in-depth analysis to a large number of opportunities. The aim of this thesis is to lay the foundation for an AI solution that addresses these challenges and to validate its concept with real users. The thesis covers the discovery phase of product development and follows established methodologies, Lean Startup and Continuous Discovery Habits. Methodologically, the thesis combines theoretical analysis of decision-making processes in venture capital with qualitative research through semi-structured interviews with investment professionals. Based on these findings, a solution was designed using the principle of progressive disclosure of information across three decision-making levels. A UX prototype was created and validated with investors. The validation results showed that users perceive the highest value in the system's ability to provide expert analysis in areas where they lack deep knowledge. A critical prerequisite for adoption is transparency and source attribution for every AI-generated claim. The majority of investors approached expressed interest in a design partnership, confirming that the concept meets real market needs. The practical contribution of the thesis is a validated concept ready for further stages of product development. The theoretical contribution consists of empirical insights into the perception and requirements for AI technologies in the venture capital environment.