The Use and Impact of AI-Tools in Early-Stage Startups
Type
conference paper
Date Issued
2024-09-05
Author(s)
Abstract
Purpose - While artificial intelligence (AI) is a hot topic in public debate and academic dis-course, most research is conceptual with only few insights into how AI is used in entre-preneurship practice. This study explores empirically how and with what effect early-stage IT startups are utilizing AI.
Design/methodology/approach - Following a mixed-method approach, we first analyze qualitative data on how AI-tools are used in early-stage startups. In a quantitative study, using Crunchbase data, we analyze the efficiency of startups before and after the intro-duction of ChatGPT-3, focusing on the number of employees and the time to achieve seed funding. Conceptually, our study builds on the External Enabler Framework.
Findings - Our qualitative study indicates that AI is mainly employed to streamline pro-cesses, saving time and resources, rather than directly influencing the offered product. While AI is considered important, it has not replaced human agency. The quantitative study suggests that startups using AI require fewer employees and achieve critical mile-stones, specifically securing seed funding, more rapidly.
Research limitations/implications - It is challenging to distinguish between AI-infused compression and conservation effects and investor-related supply-side effects, resulting from the hype surrounding AI. Future research is needed to explore AI's broader influence on entrepreneurial processes.
Originality/value - Our study is one of the first to uncover the use of AI tools in early-stage startups allowing entrepreneurs to compare to others. For academics, our study con-tributes to the discussion around AI and agency in the entrepreneurial process and pro-vides insights into the applicability of the External Enabler framework.
Design/methodology/approach - Following a mixed-method approach, we first analyze qualitative data on how AI-tools are used in early-stage startups. In a quantitative study, using Crunchbase data, we analyze the efficiency of startups before and after the intro-duction of ChatGPT-3, focusing on the number of employees and the time to achieve seed funding. Conceptually, our study builds on the External Enabler Framework.
Findings - Our qualitative study indicates that AI is mainly employed to streamline pro-cesses, saving time and resources, rather than directly influencing the offered product. While AI is considered important, it has not replaced human agency. The quantitative study suggests that startups using AI require fewer employees and achieve critical mile-stones, specifically securing seed funding, more rapidly.
Research limitations/implications - It is challenging to distinguish between AI-infused compression and conservation effects and investor-related supply-side effects, resulting from the hype surrounding AI. Future research is needed to explore AI's broader influence on entrepreneurial processes.
Originality/value - Our study is one of the first to uncover the use of AI tools in early-stage startups allowing entrepreneurs to compare to others. For academics, our study con-tributes to the discussion around AI and agency in the entrepreneurial process and pro-vides insights into the applicability of the External Enabler framework.
Keywords
Entrepreneurship
Early-stage startups
Artificial Intelligence
Mixed-method study
Entrepreneurial agency
External Enabler framework
HSG Classification
contribution to scientific community
Event Title
Rencontres de St-Gall 2024
Event Location
St.Gallen
Event Date
2-4 September 2024
Official URL
Subject(s)
Division(s)