Parisa Omidmand | Searcharxiv

arXiv2026

Artificial Intelligence in Experimental Approaches: Growth Hacking, Lean Startup, Design Thinking, and Agile

Organizations increasingly adopt AI technologies to accelerate their performance and capacity to adapt to market dynamics. This study examines how organizations implement AI in experimental methodologies such as growth hacking, lean startup, design thinking, and agile methodology to enhance efficiency and effectiveness. We performed a systematic literature review following the PRISMA 2020 framework, analyzing 37 articles from Web of Science (WOS) and Scopus databases published between 2018 and 2024 to assess AI integration with experimental approaches. Our findings indicate that AI plays a pivotal role in enhancing these methodologies by offering advanced tools for data analysis, real-time feedback, automation, and process optimization. For instance, AI-driven analytics improves decision-making in growth hacking, streamlines iterative cycles in lean startups, enhances creativity in design thinking, and optimizes task prioritization in agile methodology. Furthermore, we identified several real-world cases that successfully utilized AI in experimental strategies and improved their performance across various industries. However, despite the clear advantages of AI integration, organizations face barriers such as skill gaps, ethical concerns, and data governance issues. Addressing these challenges requires a strategic approach to AI adoption, including workforce training, strict data management, and following ethical standards.

cs.CY↗

arXiv2025

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

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.

cs.DL↗