How Large Language Models Can Change Innovation Management?

This study explores how generative Large Language Models (LLMs) can transform innovation management by shaping and enhancing practices across various business domains. Employing a data-driven approach, we gather and analyze the tasks users direct to these models, providing a quantitative, detailed perspective on their potential influence. Drawing from a dataset of over 3.8 million tweets, we identify and categorize 31,747 unique tasks, including a focused case study on ChatGPT. To achieve this, we combine two Natural Language Processing (NLP) techniques—Named Entity Recognition (NER) and BERTopic—thereby capturing the granular tasks associated with LLMs (NER) and grouping them into coherent business clusters (BERTopic). Our findings uncover a broad spectrum of applications, ranging from programming assistance to creative content generation, highlighting the versatility of LLMs. In particular, the analysis points to six emerging areas for ChatGPT: human resources, programming, social media, office automation, search engines, and education. We then connect these areas to the four stages of the innovation process—idea generation, screening/selection, development, and diffusion/sales/marketing—proposing a research agenda that integrates LLM-driven insights with key innovation management activities.