Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models - Sebastian G. Bouschery; Vera Blazevic; Frank T. Piller (Journal of Product Innovation Management, 2023)
The use of transformer-based language models in artificial intelligence (AI) has increased adoption in various industries and led to significant productivity advancements in business operations. This article explores how these models can be used to augment human innovation teams in the new product development process, allowing for larger problem and solution spaces to be explored and ultimately leading to higher innovation performance. The article proposes the use of the AI-augmented double diamond framework to structure the exploration of how these models can assist in new product development (NPD) tasks, such as text summarization, sentiment analysis, and idea generation. It also discusses the limitations of the technology and the potential impact of AI on established practices in NPD. The article establishes a research agenda for exploring the use of language models in this area and the role of humans in hybrid innovation teams. (Note: Following the idea of this article, GPT-3 alone generated this abstract. Only minor formatting edits were performed by humans.)
Keywords: 650; artificial intelligence; GPT-3; hybrid intelligence; innovation teams; prompt engineering; transformer-based language models