Experiencing AI at work:How affordances shape motivation, agency and creativity

Abstract

Artificial Intelligence (AI) is increasingly embedded in organisational life, and this dissertation explores employees’ experience of AI in everyday work. It focuses on broad-application AI, commonly introduced through organisational initiatives led by HR, Learning & Development, or IT with the aim of supporting employees. Rather than approaching AI as a discrete tool with fixed effects, the dissertation examines unfolding relations in specific contexts and ask what matters in AI-inclusive work. This dissertation shows that AI applications are not merely sets of functionalities, but are inseparable from the situated context, shaping and being shaped by employees, work practices, and the organisational environment. It finds that conversational AI, using natural language instead of a menu-based interface for employee self-service, can contribute to a motivation and well-being supportive organisational environment by fostering greater autonomy, competence and relatedness. It also examines how employees interact with AI systems internal and external to their organisations (including unendorsed “shadow AI”), such as contextual search, content recommendations and generative AI in knowledge work. These interactions, shaped by past habits, present constraints and imagined futures, gradually reshape the boundaries of tasks, relationships and the meaning of work. Finally, the dissertation argues that different types of AI matter in different ways because they elicit different forms of engagement and different workplace experiences. Discriminative AI is positioned as a tool for the task, foregrounding efficiency and effectiveness, while also potentially giving rise to possible negative long-term experiences and raising questions about meaningful work. Generative AI is positioned as a medium for creative expression, foregrounding exploration, innovation, and creative actions, thereby fostering more creative and positive experiences at work. Overall, this dissertation offers insights for scholars and practitioners into emerging relations in AI-inclusive work, showing that the value and effects of AI do not reside in technology alone, but emerge through the relations among employees, AI characteristics and organisational context. In doing so, it offers a perspective that moves beyond short-term gains and highlights the longer-term value of creating work environments in which AI is experienced as useful, meaningful and supportive.