Human-AI Role Allocation in Design Thinking: Contributions to Initiation, Elaboration, and Decision-Making

Abstract

Small and medium-sized enterprises (SMEs) operate under persistent resource constraints and face growing pressure to innovate in an increasingly dynamic and digitalised market environment. In this context, generative artificial intelligence (AI) is emerging as a valuable tool for knowledge-intensive and creative work. However, its value does not arise from technological availability alone, but from how it is embedded in organisational processes, team interactions, and the allocation of roles between human actors and AI. This paper examines the role of ChatGPT as an AI assistant in team-based Design Thinking, with particular attention to how human–AI role allocation can be integrated into SME innovation processes.Drawing on concepts of hybrid intelligence and human-centred innovation, the central research interest lies in understanding how roles can be allocated between humans and AI across different stages of the innovation process without replacing human judgement, contextual understanding, and responsibility for final decisions.An exploratory experiment was conducted with 32 working professionals collaborating with ChatGPT in pairs across four Design Thinking phases (Brown, 2008): Empathise and Define, Ideate and Decide, Prototype, and Test. Within a 60-minute session, participants developed an innovation concept for a fictitious Vienna-based company offering a hydroponic planting system for indoor and balcony use. The setup compared two interaction sequences: in Team A, an AI–Human–AI pattern in which the AI initiated and supported preselection; in Team B, a Human–AI–Human pattern in which humans framed the task and retained final decision-making authority.Methodologically, the study combines standardised questionnaires (NASA-RTLX, SUS), open-ended responses, and documented chat transcripts; the present paper reports descriptive findings only. By focusing on human–AI role allocation within teams rather than on individual AI use, the paper contributes to current discussions on how SMEs can integrate generative AI into innovation processes in a purposeful, human-centred and methodologically reflective way.