I prompt, therefore I know: Making sense of expertise in an AI world

Abstract

The widespread use of generative artificial intelligence (AI) such as large language models is challenging traditional notions of expertise. While AI tools can increase efficiency and aid creativity, they are often prone to hallucinations, might miss contexts and raise challenges around accountability. To better understand how to approach AI, this study explores how AI experts and non-AI experts make sense of AI. Drawing on sensemaking theory and concepts of expertise, this study investigates how individuals with different levels of expertise in AI enact, select and retain information regarding AI. By exploring how the two groups of experts make sense, it is possible to reveal broader implications of AI for different professionals. Through 28 semi-structured interviews with professionals across various industries in Sweden, the study identifies a notable difference between the two groups of experts. Our findings show that AI experts engage with AI tools in a critical, intentional manner, emphasising approaches that mitigate potential drawbacks, such as verifying the material produced with AI and reflecting on limitations of AI. The non-AI experts on the other hand tend to reflect less and act more intuitively, often after limited experiences with AI. These differences in sensemaking approaches indicate that expertise is not only enacted but also situationally and relationally shaped by how these experts interact with the technology in a specific context. The study contributes to management research by providing a guideline for how to categorise AI expertise, insight into what factors affect sensemaking of AI and insight into how organisations can implement AI responsibly for different groups of experts.