Artificial Intelligence in Experimental Approaches: Growth Hacking, Lean Startup, Design Thinking, and Agile
Parisa Omidmand, Saeid Ataei

TL;DR
This paper systematically reviews how AI enhances experimental methodologies like growth hacking, lean startup, design thinking, and agile, highlighting benefits, real-world applications, and challenges faced by organizations.
Contribution
It provides a comprehensive analysis of AI integration in experimental approaches through a systematic review of recent literature, identifying key benefits and challenges.
Findings
AI improves decision-making and process optimization in experimental methods.
Real-world cases demonstrate successful AI-driven performance improvements.
Organizations face skill, ethical, and data governance challenges in AI adoption.
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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Big Data and Business Intelligence · Digital Transformation in Industry
