The transition towards Generative Artificial Intelligence (GAI) is rapidly transforming the digital realm and providing new avenues for creativity for all humanity. In the past two years, several generative models have disrupted worldwide, including ChatGPT and DALL-E 2, developed by OpenAI, which are currently receiving significant media attention. These models can generate new content, respond to prompts, and automatically create new images and videos. Nevertheless, despite this progress of GAI, research into its application in business and industry is still in its infancy. Generative AI is bringing ground-breaking innovations that go beyond the limitations of conventional Contextual AI. This new type of AI can generate novel patterns in human-like creativity, encompassing various forms of content such as text, images, and media. It transforms how people communicate, create, and share content, taking organizations by surprise. Unfortunately, these organizations were not fully prepared as they were focused on the advancements and impacts of Contextual AI. Given the significant organizational-societal opportunities and challenges posed by generative models, it is crucial to comprehend their ramifications. However, the excessive hype surrounding GAI currently makes it difficult to determine how organizations can effectively utilize and regulate these powerful algorithms. In research, the primary question is how organizations can manage the intersection of human creativity and machine creativity, and how can they leverage this intersection to their advantage? To address this question and mitigate concerns related to it, a comprehensive understanding of GAI is essential. Therefore, this paper aims to provide technical insights into this paradigm and analyze its potential, opportunities, and constraints for business and industrial research.