How organizations can innovate with generative AI

Abstract

Artificial intelligence (AI) is driving significant impact on businesses across many sectors. Specifically, generative AI (GenAI) is fueling new capabilities that have triggered a wave of innovation. For example, there has been massive hype surrounding the launch of ChatGPT, with growing speculation regarding its disruptive nature for organizations and society. The ongoing debate conveys a clear belief that ChatGPT will lead to far-reaching innovation. However, it is less clear whether such innovation can be managed. We seek to close this gap by identifying distinctive innovation strategies in terms of two key dimensions: automation and augmentation (high or low). We present a new typology of four generic innovation strategies: Traditional Tool (low automation, low augmentation), Basic Automation (high automation, low augmentation), Automated Assistance (low automation, high augmentation), and Assisted Augmentation (high automation, high augmentation). These strategies differ in relation to how we view automation and augmentation for innovation, and also the risks and challenges faced throughout the process and tactics for managing the process. The typology of four generic innovation strategies pinpoints how the four strategies essentially differ not only in relation to automation and augmentation for innovation but also in terms of risks and challenges faced in the process, as well as available tactics for managing the process. Building upon this framework, our insights suggest that practitioners can harness ChatGPT effectively by aligning their innovation objectives with the appropriate strategy, whether it be enhancing creative processes or streamlining operational efficiency, thereby navigating the complexities of innovation with a more structured and strategic approach.