Abstract Generative Artificial Intelligence (GenAI) has emerged as a pivotal driver of transformation in the knowledge economy, reshaping how organizations create, distribute, and utilize knowledge. This paper examines how GenAI acts as a catalyst for organizational ambidexterity—the ability to simultaneously exploit existing capabilities while exploring new opportunities, within the broader context of digital transformation. Drawing on a systematic review of recent academic and industry literature (2020–2025), this study synthesizes insights from management, information systems, and artificial intelligence research to develop an integrative framework linking GenAI capabilities to organizational change dynamics. The findings reveal that GenAI enables three distinct but interrelated impacts on business workflows: automation, which streamlines repetitive and routine tasks to enhance efficiency and exploitation of existing resources; augmentation, which empowers human decision-making and collaboration by enhancing creativity and problem-solving; and innovation, which facilitates the exploration of new business models, value propositions, and organizational structures. Collectively, these mechanisms drive a dual process of exploitation and exploration, positioning GenAI as a strategic enabler of organizational ambidexterity. The study highlights that responsible GenAI adoption requires not only technological integration but also governance, cultural, and skill development initiatives to ensure sustainable value creation. It proposes managerial strategies for aligning GenAI deployment with ambidextrous organizational design, including adaptive governance frameworks, continuous learning systems, and cross-functional collaboration models. By bridging the gap between technological innovation and management theory, this paper contributes a multidimensional understanding of how GenAI transforms organizational workflows, structures, and strategic capabilities. Ultimately, this work advances the discourse on digital transformation by illustrating how generative technologies reshape the foundations of organizational learning and adaptability in the knowledge economy, offering actionable guidance for managers, policymakers, and researchers navigating this evolving landscape.
Tiwari et al. (Sun,) studied this question.