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

• URL: http://arxiv.org/abs/2603.20688v1
• Date: Sat, 21 Mar 2026 07:08:03 GMT
• Title: Artificial Intelligence in Experimental Approaches: Growth Hacking, Lean Startup, Design Thinking, and Agile
• Abstract summary: This study examines how organizations implement AI in experimental methodologies such as growth hacking, lean startup, design thinking, and agile methodology.<n>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.<n>Despite the clear advantages of AI integration, organizations face barriers such as skill gaps, ethical concerns, and data governance issues.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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