Generative Artificial Intelligence and Its Implications for Business Management

Abstract

Artificial intelligence (AI) technologies that generate new outputs—texts, images, music, or product designs—based on data inputs are reshaping business management. These systems suggest treatment regimes for diagnoses, compose music, inspect semiconductor circuits, and create marketing strategies, even when they remain less sophisticated than humans in these domains. Such systems can generate new forms of open-source material, design new molecules, and formulate new recipes. The AI capability underlies many of the most commonly used systems, including ChatGPT, DALL·E, Bard, Midjourney, Claude, and Copilot. The attractive possibilities—which include enhanced data-driven insights, creativity augmentation, collaborative content generation, and productivity boosts—are narrowed by risks of exploitation, misinformation, biased recommendations, low-quality generation, and control loss.

This synthesis focuses on the implications of generative AI for business management, a larger scope than consideration of artificial intelligence in general. The place of generative AI in business management is critically important because if, after more than a decade of investment in corporate AI initiatives, managerial observations are still largely limited to implementation challenges (Houde et al., 2020) , anticipation of new potential should remain insightful. A broad set of questions is therefore posed: What continues to change regarding data sources, formats, types, dependence, ownership, ethics, and regulations in the age of generative AI? How can evolving data and artificial intelligence capabilities help companies unleash the value of all their existing data, wherever its origin, throughout the entire lifecycle from sourcing to creation, and springboard them into new business models as well? What data governance frameworks are needed to ensure compliance? What actual use cases have emerged, and what corresponding capabilities should companies consider developing? What organizational adaptations and change-management practices remain necessary?