AI-Augmented Design Thinking: Potentials, Challenges, and Mitigation Strategies of Integrating Artificial Intelligence in Human-Centered Innovation Processes

  • The integration of artificial intelligence (AI) into innovation management has expanded into creative domains such as Design Thinking (DT), yet its role within complex, collaborative innovation frameworks remains underexplored. This study addresses this gap by investigating how professionals perceive and utilize AI in DT workshops. Using affordance theory as a conceptual lens, we conducted observational studies and semi-structured interviews with DT experts to identify AI's action potentials, constraints, and mitigation strategies in this context. Our findings highlight four key affordances of AI in DT workshops: enhanced creativity, support for analytical tasks, facilitation of task initiation, and acceleration of processes. However, these benefits are tempered by challenges, including reduced team collaboration, diminished ownership of AI-generated outputs, and disruptions to workshop flow. The study reveals distinct human-AI interaction archetypes, underscoring the dynamic interplay between human expertise and AI capabilities. ToThe integration of artificial intelligence (AI) into innovation management has expanded into creative domains such as Design Thinking (DT), yet its role within complex, collaborative innovation frameworks remains underexplored. This study addresses this gap by investigating how professionals perceive and utilize AI in DT workshops. Using affordance theory as a conceptual lens, we conducted observational studies and semi-structured interviews with DT experts to identify AI's action potentials, constraints, and mitigation strategies in this context. Our findings highlight four key affordances of AI in DT workshops: enhanced creativity, support for analytical tasks, facilitation of task initiation, and acceleration of processes. However, these benefits are tempered by challenges, including reduced team collaboration, diminished ownership of AI-generated outputs, and disruptions to workshop flow. The study reveals distinct human-AI interaction archetypes, underscoring the dynamic interplay between human expertise and AI capabilities. To mitigate constraints, we propose strategies such as pre-preparing AI-generated content, defining clear roles for AI and human inputs, and fostering collaborative reflection on AI outputs. By illuminating AI's potential and limitations within DT, this research contributes to the innovation management literature and offers actionable insights for practitioners seeking to integrate AI into hybrid innovation processes.show moreshow less

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Metadaten
Author:Leonie Polster, Volker BilgramORCiD, Sarah Görtz
DOI:https://doi.org/10.1109/EMR.2024.3512866
ISSN:0360-8581
Parent Title (English):IEEE Engineering Management Review
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Document Type:Article
Language:English
Reviewed:Begutachtet/Reviewed
Release Date:2025/01/13
Pagenumber:36
First Page:1
Last Page:36
institutes:Fakultät Betriebswirtschaft
Research Themes:Digitalisierung & Künstliche Intelligenz
Licence (German):Creative Commons - CC BY-ND - Namensnennung - Keine Bearbeitungen 4.0 International
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