Design Thinking (DT) is spreading out in the managerial community as an alternative way to innovate products and services respect to the classical stage-gate model mostly linked to technology-push innovative patterns. At the same time few disruptive technologies – like Artificial Intelligence (AI) and machine learning – are impacting the ways companies manage their knowledge and activate innovation and design processes. What is the impact that AI is exerting on DT practices? What are the main changes that DT is undergoing? These questions are analyzed in this paper, where the aim consists in increasing the understanding of the transformation that is occurring in DT and more general in innovation practices. Through a qualitative case study analysis made on startups offering AI based solutions supporting multiple or individual DT phases, the article pinpoints few main changes: i) a facilitation in blending the right mix of cultures and creative attitudes in innovation teams; ii) the empowerment of the research phase where statistical significance is gained and user analysis are less observer-biased; iii) the automatization of the prototyping and learning phases.
The impact of Artificial Intelligence on Design Thinking practice: Insights from the Ecosystem of Startups
Cautela, Cabirio;Mortati, Marzia;Dell'Era, Claudio;Gastaldi, Luca
2019-01-01
Abstract
Design Thinking (DT) is spreading out in the managerial community as an alternative way to innovate products and services respect to the classical stage-gate model mostly linked to technology-push innovative patterns. At the same time few disruptive technologies – like Artificial Intelligence (AI) and machine learning – are impacting the ways companies manage their knowledge and activate innovation and design processes. What is the impact that AI is exerting on DT practices? What are the main changes that DT is undergoing? These questions are analyzed in this paper, where the aim consists in increasing the understanding of the transformation that is occurring in DT and more general in innovation practices. Through a qualitative case study analysis made on startups offering AI based solutions supporting multiple or individual DT phases, the article pinpoints few main changes: i) a facilitation in blending the right mix of cultures and creative attitudes in innovation teams; ii) the empowerment of the research phase where statistical significance is gained and user analysis are less observer-biased; iii) the automatization of the prototyping and learning phases.File | Dimensione | Formato | |
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