Partnering with AI: the case of digital productivity assistants
Jocelyn Cranefield, Michael Winikoff, Yi-Te Chiu, Yevgeniya Li, Cathal Doyle, Alexander Richter
Abstract
An emerging class of intelligent tools that we term Digital Productivity Assistants (DPAs) is designed to help workers improve their productivity and keep their work-life balance in check. Using personalised work-based analytics it raises awareness of individual collaboration behaviour and suggests improvements to work practices. The purpose of this study is to contribute to a better understanding of the role of personalised work-based analytics in the context of (improving) individual productivity and work-life balance. We present an interpretive case study based on interviews with 28 workers who face high job demands and job variety and our own observations. Our study contributes to the still ongoing sensemaking of AI, by illustrating how DPAs can co-regulate human work through technology affordances. In addition to investigating these opportunities of partnering with AI, we study the perceived barriers that impede DPAs' potential benefits as partners. These include perceived accuracy, transparency, feedback, and configurability, as well as misalignment between the DPA's categorisations of work behaviour and the categorisations used by workers in their jobs.