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

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 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.