🤖 AI Summary
This study addresses the challenges organizations face—such as skill gaps, ethical concerns, and data governance issues—when integrating artificial intelligence (AI) into experimental innovation approaches like growth hacking, lean startup, design thinking, and agile methodologies. Drawing on a systematic literature review of 37 studies published between 2018 and 2024 and guided by the PRISMA 2020 framework, this work offers the first integrative synthesis of AI with these four dominant experimental innovation paradigms. It reveals cross-method synergies through which AI enhances iterative cycles, boosts creativity, and improves decision-making via data analytics, real-time feedback, automation, and process optimization. The findings not only demonstrate AI’s significant contribution to amplifying experimental innovation efficacy but also identify critical success factors and implementation barriers, thereby proposing a strategic adoption pathway encompassing talent development, data governance, and ethical guidelines.
📝 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.