How organizations can innovate with generative AI

Artificial intelligence (AI) is poised to have a profound influence on businesses across all sectors. Specifically, generative AI is set to underpin the development of potent and novel capabilities, ushering in a new wave of innovation. For example, there has been massive hype surrounding the launch of ChatGPT, with growing speculation regarding its disruptive nature of generative AI 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 – or even if – such innovation can be managed. We seek to close this gap here by identifying distinctive innovation strategies in terms of two key dimensions: automation and augmentation (high or low). This results in a 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). The 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. 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.