From Conventional to Cutting-Edge: A Comparative Study of AI-Enhanced and Traditional Management of Innovation Projects

Abstract

Integrating Artificial Intelligence (AI) into innovation management transforms how projects are managed, presenting both opportunities and challenges. Traditional management of innovation projects relies heavily on human intuition and creativity. At the same time, AI offers the potential to automate tasks, enhance decision-making through data-driven insights, and detect trends in real-time. This paper examines the evolving role of AI in innovation projects, evaluates the effectiveness of AI-enhanced approaches in managing innovation projects, and compares them with traditional project management methods. The primary objective of this research is to assess how AI can complement, augment, or even replace traditional project management practices across phases of innovation projects. The focus is identifying AI’s strengths and limitations in various phases of innovation projects and how it complements or replaces traditional practices. After the introduction and theoretical foundations, a mixed-method research approach is employed. First, a bibliometric analysis is conducted using the Web of Science database to track research trends, collaboration networks, and influential publications in the field of AI and innovation management. Second, a focused literature review is performed on studies published between 2019 and 2024, specifically examining AI applications in managing innovation projects. These methods provide the basis for a comparative analysis of AI-enhanced versus traditional management of innovation projects. The study reveals that AI significantly enhances project efficiency by automating data processing, optimizing resource allocation, and improving trend detection. It also supports decision-making by offering real-time, data-driven insights. However, challenges remain in integrating AI into projects due to technical complexity, data quality, and ethical concerns. The findings suggest that while AI improves operational efficiency and decision-making, human intuition and creativity are still essential, particularly for strategic thinking and ethical considerations. The study recommends a hybrid approach, where AI handles data-heavy tasks, while human expertise guides creative and ethical decisions. Limitations of the paper include reliance on the Web of Science database, restricting the scope of the literature reviewed, and focusing on a specific timeframe. These limitations provide space for further improvement of the work in the continuation of the research. Future research should focus on developing hybrid models that would combine human intuition and AI-driven management practices to create more balanced innovation strategies. Other directions for exploration include improving AI’s capacity to handle tasks requiring ethical judgment and creativity, such as through explainable AI (XAI) to address transparency issues. Research could also investigate the challenges of data dependency by enhancing data quality and accessibility for AI systems, as well as examining AI’s role in fostering collaboration in geographically dispersed teams and improving communication and project coordination in global innovation networks.