Augmenting human innovation teams with artificial intelligence: Exploring transformer‐based language models

TLDR

Transformer‑based language models have been increasingly adopted across industries, driving significant productivity gains in business operations. The article investigates how transformer models can augment human innovation teams in new product development to expand problem and solution spaces and improve innovation performance, and it outlines a research agenda for hybrid human–AI teams. The authors propose an AI‑augmented double diamond framework to guide NPD tasks such as text summarization, sentiment analysis, and idea generation. The study highlights limitations of transformer models and their potential impact on established NPD practices. Minor formatting edits were performed by humans.

Abstract

Abstract 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.)