Artificial Intelligence Applications in Lean Startup Methodology: A Bibliometric Analysis of Research Trends and Future Directions

TL;DR

This paper systematically analyzes research trends at the intersection of artificial intelligence and lean startup methodology, highlighting key themes, gaps, and future directions to advance digital entrepreneurship research.

Contribution

It provides the first comprehensive bibliometric overview of AI in lean startups, identifying research clusters, gaps, and suggesting directions for future interdisciplinary studies.

Findings

Three research clusters identified: AI in experimentation, AI learning systems, strategic AI implications.

Limited international collaboration and geographic concentration in developed economies.

Research gaps in ethics, cross-cultural validation, and empirical testing of AI frameworks.

Abstract

This study presents a comprehensive bibliometric analysis of the emerging intersection between artificial intelligence (AI) and lean startup methodology. Using the PRISMA 2020 framework, we systematically analyzed 12 peer-reviewed articles published between 2010 and June 2025, sourced from the Scopus database. The analysis employed VOS viewer software to conduct co-authorship, keyword co-occurrence, and citation network analyses. Results reveal three distinct research clusters: operational integration of AI within startup experimentation processes, AI-enhanced learning systems for entrepreneurial contexts, and strategic implications of AI for uncertainty management in startups. The findings indicate a developing research domain characterized by fragmented authorship networks, limited international collaboration, and geographic concentration in developed economies, particularly the United States and Germany. Key research themes include business model innovation, iterative methods, and machine learning applications, with artificial intelligence serving as a bridging concept across thematic clusters. The analysis identifies significant research gaps in ethical considerations, cross-cultural validation, and empirical testing of AI-enabled lean startup frameworks. While current research demonstrates growing interest in AI integration within entrepreneurial experimentation, the field requires enhanced theoretical consolidation, methodological rigor, and interdisciplinary collaboration to achieve practical relevance and academic maturity. This study contributes to the emerging discourse on digital entrepreneurship by providing a systematic overview of research trends and identifying priority areas for future investigation at the intersection of AI and lean startup methodologies.