Knowledge work augmentation with AI: a taxonomy of AI-based technology characteristics and their implications for meaningful work

Abstract

The augmentation of knowledge work with artificial intelligence (AI) causes changes in the nature of work that affect workers’ opportunities for engaging in meaningful work. Prior research has largely emphasised performance outcomes, offering limited insight into how specific AI characteristics shape workers’ experiences. Addressing this gap, we develop a taxonomy of AI-based technology characteristics in knowledge worker augmentation and examine their links to meaningful work. We conducted a two-iteration literature synthesis (N = 62 studies), developing a taxonomy of AI-based technology characteristics in knowledge worker augmentation. The analysis identifies five core characteristics (i.e. adaptability, human likeness, independence, inscrutability, and knowledge production) and explicates the mechanisms through which they influence knowledge work. The findings show that knowledge worker augmentation is configurational, with different characteristic constellations producing distinct meaningful work implications. The study offers a human-centered lens for knowledge work augmentation.