Paper Number
ECIS2026-2352
Paper Type
CRP
Abstract
Advances in large language models have made AI-based assistants widely available for knowledge-intensive work, yet their role in entrepreneurial innovation remains poorly understood. This study examines how startup founders use AI-based assistants across innovation activities and what requirements founders articulate. We conduct semi-structured interviews with 19 founders of German startups and analyze their accounts through the lens of human-AI collaboration and digital agency. The findings reveal four task domains in which founders rely on AI-based assistants, each characterized by a distinct distribution of human and AI agency, and three interaction and control patterns that govern how founders calibrate delegation and oversight over time. These configurations clarify when AI-based assistants meaningfully augment entrepreneurial work, when their contribution remains limited, and when expectations are unmet. The study contributes to research on human-AI collaboration and digital entrepreneurship and informs the design of AI-enabled tools that better support early-stage innovation.
Recommended Citation
Liebschner, Jonas; Meier, Ida; Heinz, Daniel; and Satzger, Gerhard, "The Adoption Of AI-Based Assistants In Startup Innovation: Configuring Human–AI Agency In Entrepreneurial Work" (2026). ECIS 2026 Proceedings. 20.
https://aisel.aisnet.org/ecis2026/is_adopt/is_adopt/20
The Adoption Of AI-Based Assistants In Startup Innovation: Configuring Human–AI Agency In Entrepreneurial Work
Advances in large language models have made AI-based assistants widely available for knowledge-intensive work, yet their role in entrepreneurial innovation remains poorly understood. This study examines how startup founders use AI-based assistants across innovation activities and what requirements founders articulate. We conduct semi-structured interviews with 19 founders of German startups and analyze their accounts through the lens of human-AI collaboration and digital agency. The findings reveal four task domains in which founders rely on AI-based assistants, each characterized by a distinct distribution of human and AI agency, and three interaction and control patterns that govern how founders calibrate delegation and oversight over time. These configurations clarify when AI-based assistants meaningfully augment entrepreneurial work, when their contribution remains limited, and when expectations are unmet. The study contributes to research on human-AI collaboration and digital entrepreneurship and informs the design of AI-enabled tools that better support early-stage innovation.
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