An Investigation into the Extent to Which AI-driven Automation Altered the Structural Economic Viability of Dropshipping as an Entrepreneurial Model.

Abstract

This study examines the extent to which AI-driven automation has altered the structural economic viability of dropshipping as an entrepreneurial model. While dropshipping has traditionally been characterized by low barriers to entry and minimal capital requirements, it has also faced significant structural limitations, including thin profit margins, high competition, and reliance on third-party suppliers. Using a qualitative research approach based on secondary data and conceptual economic analysis, this paper evaluates the impact of AI across three key dimensions: skill barriers, capital efficiency, and market competition. The findings indicate that AI significantly reduces the skill and knowledge required to operate a dropshipping business while improving capital efficiency through automation of marketing, product research, and content creation. However, these same developments contribute to increased market saturation, intensifying competition and raising customer acquisition costs through auction-based advertising systems. As a result, while AI enhances accessibility and short-term operational efficiency, it does not fundamentally resolve the structural constraints of the model. The study further identifies an emerging shift toward “branded dropshipping” as an adaptive response enabled by AI, allowing for greater differentiation and potential margin improvement. Nevertheless, this evolution does not eliminate underlying economic limitations. Overall, the research concludes that AI has not made dropshipping inherently more sustainable but has instead reshaped its dynamics, creating a more accessible yet increasingly competitive and uncertain entrepreneurial environment.