Managing Generative AI: Organisational Theories and Practices to Maximize Value and Mitigate Risks

The rapid growth of Generative Artificial Intelligence (GenAI) has revolutionized management, presenting unique opportunities and risks. While GenAI’s potential to enhance productivity and foster innovation is widely acknowledged, its integration raises complex ethical, operational, and regulatory challenges. Recent studies highlight GenAI’s ability to streamline internal processes, support data-driven decision-making, and advance ideation, extending the problem and solution space in product innovation. However, GenAI’s adoption can fragment innovation processes, disrupt work identity, and amplify ethical concerns such as bias and loss of agency. The rise of “AI washing” further complicates these issues by eroding trust. Additionally, regulatory challenges surrounding data privacy and transparency require organizations to approach GenAI responsibly. This research track invites case studies and theoretical work on managing GenAI within organizations, emphasizing frameworks for responsible AI, human-AI collaboration, and design strategies that balance GenAI’s benefits with ethical stewardship for sustainable innovation practices.