Abstract This article presents an applied discussion of the possibility of integrating conversation analysis (CA) methodology into that of machine learning. The aim is to improve the detection of that which resembles disengagement in the interaction between a robot and a human. We offer a novel analytical assemblage at the heart of the two disciplines, and namely on the level of the annotation schemes provided by conversation analysis transcription methods. First, we demonstrate that the need for a stable structure in establishing an interaction scenario and in designing robot behaviours does not prevent the emergence of ordinariness or creativity among the participants engaged in this interaction. Secondly, based on an actual case, we emphasize the possibility of systematicness in CA transcription to support the choice (a) of the categories targeted by prediction methods and defined by the annotation scheme, and (b) of the verbal and non-verbal features used to create prediction models.