Veröffentlichungen von Victoria Sofia Santos Gabriel

o Even though the transformative potential of Artificial Intelligence (AI) for business operations and strategy is widely acknowledged, our understanding of which types of AI create value for companies remains limited. This study examines the relationship between different types of AI and their value over time. Using natural language processing and multivariate regression analysis on a comprehensive dataset of 461,207 AI patents, we demonstrate that value effects vary across the different types of AI and change over time. AI patents categorized as mechanical showed positive value associations in early years that reversed after 2019, whereas intuitive AI patents exhibited positive value associations throughout the observation period, with increasing effect magnitudes after 2019. This research contributes to existing literature by offering a nuanced view of AIs’ impact and practical implications for strategic AI usage and innovation in organizations.

This paper aims to consolidate and advance the understanding of the business value of Generative Artificial Intelligence (GenAI) by reviewing existing literature from academic sources and industry reports. Based on the existing literature on IT business value we categorized the impact of GenAI along three dimensions: strategic, informational, and transactional. Strategic implications reveal the potential of GenAI to drive innovation and growth, emphasizing the need for a balance between innovation and cost efficiency. Informational implications underscore the importance of integrating GenAI into decision-making processes effectively to enhance market analysis and R&D functions. Transactional implications focus on maximizing business value through managing transitions and reshaping job roles. The paper calls for further research to validate case-based findings quantitatively, explore innovation-cost efficiency interplay, and understanding long-term implications to fully leverage the benefits of GenAI.