Analyzing the Role of Generative Artificial Intelligence (GenAI) in Supporting Customer Discovery Activities in Early-Stage Software Startups

Abstract

Early-stage software startups in Indonesia face a high mortality rate, predominantly driven by a lack of market need and ineffective problem validation. While Customer Discovery is a critical phase to mitigate these risks, it is often hindered by limited resources and time constraints. The emergence of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), offers new potential to accelerate iteration and analysis within this process. This study aims to identify Customer Discovery activities supported by GenAI and analyze the adoption levels and human-AI collaboration patterns using the Human-AI Collaboration and Adaptation Framework (HACAF). The research methodology employs a mixed-method approach, combining quantitative surveys and in-depth interviews with startup founders in Indonesia. The HACAF framework is utilized to evaluate adoption determinants, including perceptions of technology, compatibility factors, social factors, and personal/environmental factors. The findings are expected to provide a comprehensive mapping of GenAI's role as an assistant, collaborator, or co-creator in market validation, offering strategic guidance for startups to effectively integrate GenAI during the initial product development phase.