AI Agents in the Double Diamond: Augmenting Design Thinking Practice in Organizational Innovation

From Tools to Teammates: Designing Human-AI Collaboration in Innovation Work

Human-AI collaboration (HAIC) has moved from research curiosity to strategic priority for organizations pursuing faster, more inventive, and more reliable innovation outcomes. Many implementations underperform, however, because leaders treat artificial intelligence as a generic productivity tool rather than as a designed teammate whose role, capabilities, and trust requirements must match the task. This article synthesizes recent scholarship and practitioner experience into a structured playbook for executives, design leaders, and human resources partners. It argues that the value of HAIC depends on three deliberate design choices: who initiates the collaboration, how broad the AI's knowledge scope must be, and whether the cognitive mode is analytical or synthetic. Drawing on engineering design, aerospace, industrial product development, hospitality, and mental health contexts, the discussion translates research findings into operating practices, governance structures, and capability investments. The contribution is practical: a clearer way to decide what kind of AI teammate to build, deploy, and trust for any given problem.

Design Management in Industry 5.0: Synergy of AI, Humans, and Machines

Design management in Industry 5.0 faces a persistent gap due to fragmented conceptualizations that treat AI, human creativity, and machine capabilities as largely separate elements, limiting their effective integration within complex socio-technical systems. Addressing this gap, the present study develops and empirically validates a Design Management Model grounded in human-centered, ethical, and sustainability-oriented principles, framing design management as an adaptive and relational system rather than a linear or technology-driven process. Departing from the automation-oriented logic of Industry 4.0, the study adopts an augmented cognition perspective in which AI functions as a collaborative partner supporting, rather than replacing, human judgment. A sequential mixed-methods approach was employed, integrating systematic literature review, qualitative content analysis, expert evaluation, and Structural Equation Modeling (SEM) based on data from 316 participants, followed by empirical examination in two service-oriented case contexts, namely tourism/hospitality and healthcare services. The findings identify and validate six interrelated domains and demonstrate that Human–AI–Machine Synergy plays a central role in shaping design outcomes. More specifically, the results show that effective design management in Industry 5.0 depends on the coordinated interaction between cognitive processes, technological infrastructures, and organizational strategies, rather than on isolated technological advancement. Empirical applications further illustrate how the model supports ethically guided AI integration, enhances adaptive decision-making, and improves experience-oriented innovation across different contexts. By providing a validated structural framework that connects previously disjointed elements, this study contributes a clearer operational understanding of how human–AI collaboration can be embedded within design management practices in Industry 5.0.

Does AI Strengthen Business Leardership Decision Making: Literature Review

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

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.

Does AI strengthen business leadership decision making: literature review

AI does not replace the role of executive leaders; instead, it serves as a cognitive aid that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders.

Analogies and Metaphors for Agentic AI: How Industry Practitioners Explain Emerging AI Systems

This work conducts semi-structured interviews with industry practitioners working with agentic AI systems, indicating that agentic AI systems are mainly explained through organizational and anthropomorphic source domains, such as employees, teams, or assistants, which embed abstract system qualities within familiar social structures.