By speaker > Liu Ying-Hsang

As Artificial Intelligence (AI) begins to transform global information landscapes, professionals need to navigate a complex transition from manual to AI-assisted metadata workflows. This presentation details findings from a large-scale survey of 752 information professionals conducted in 15 languages, using Structural Equation Modeling (SEM) to explain the mechanisms of AI engagement.

The research identifies perceived benefits as the primary driver of adoption, with a majority agreeing that AI will significantly reduce the time and effort required for metadata management. Furthermore, a dual effect was found. While ethical and operational concerns, such as algorithmic bias and a lack of human nuance, inhibit immediate task involvement, they simultaneously motivate professionals to develop new competencies and advocate for responsible AI practices.

The study also emphasizes the role of self-efficacy, finding that confidence in understanding AI concepts directly correlates with the intention to adopt Generative AI (GenAI) and predictive AI tools. Finally, results show that professional background moderates this transition. Specifically, technical confidence is a more critical driver for technology-focused roles, whereas practical experience carries more weight for those in other areas. These findings reveal that as information professionals encounter risks, such as algorithmic bias and lack of transparency, their response is to enhance their self-assessed capabilities in AI-related skills, rather than simply rejecting the AI technologies.