Artificial intelligence and the future of work – lessons from the sociology of expectations

Purpose What is the future of work going to look like? The aim of this paper is to show how the sociology of expectations (SE) – which deals with the power of visions – can make important contributions in terms of thinking about this issue by critically evaluating the dominant expert positions related to the future-of-employment- and artificial intelligence (AI) debate. Design/methodology/approach After providing a literature review regarding SE, an approach based on the latter is applied to interpret the dominant ideal-type expert positions in the future of work debate to illustrate the value of this perspective. Findings Dominant future scripts can be characterized by a focus on the effects of AI technology that give agency to technology and to the future, involve the hype of expectations with polarized frames, and obscure uncertainty. It is argued that these expectations can have significant consequences. They contribute to the closing off of alternative pathways to the future by making some conversations possible, while hindering others. In order to advance understanding, more sophisticated theorizing is needed which goes beyond these positions and which takes uncertainty and the mutual shaping of technology and society into account – including the role expectations play. Research limitations/implications The study asserts that the dominant positions contain problematic assumptions. It makes suggestions for helping move beyond these current framings of the debate theoretically. It also argues that scenario building and backcasting are two tools that could help move forward thinking about the future of work – especially if this is done in a way so as to build strongly on SE. Practical implications The arguments presented herein enhance sense-making in relation to the future-of-work debate, and can contribute to policy development. Originality/value There is a lack of adequate exploration of the role of visions related to AI and their consequences. This paper attempts to address this gap by applying an SE approach and emphasizing the performative force of visions.