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Identifying high-impact departments for agentic artificial intelligence in the German startup environment-transforming data governance: leveraging AI for efficient and effective data management in complexe corporate structures
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This thesis examines which startup departments offer optimal opportunities for implementing Agentic AI systems. Through grounded theory analysis of interviews with startup professionals, four critical dimensions emerged: departmental AI suitability, engineering readiness,
operational complexity barriers, and organizational infrastructure prerequisites. Sales & Marketing and Software Engineering departments demonstrated exceptional readiness due to structured, repetitive tasks and existing technical capabilities. Conversely, departments
requiring extensive domain expertise and managing physical operations faced substantial barriers. The research provides strategic recommendations for phased AI adoption, emphasizing robust data infrastructure and tailored departmental approaches.
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Agentic AI Startup AI adoption AI departmental readiness AI implementation strategy Autonomous AI systems Organizational infrastructure Grounded theory CEMS MIM