O uso de inteligência artificial potencializando a resolução de problemas em uma estrutura de Design Thinking

Abstract

Design Thinking (DT) stands out as an effective methodology for solving complex problems and generating innovative ideas. However, its application can present challenges related to the required time and resources. Generative Artificial Intelligence (AI), with its ability to process large volumes of data and generate relevant discoveries, emerges as a potential catalyst for optimizing DT sessions. Despite growing interest, the academic literature still lacks empirical studies investigating the practical application and actual user perception of this synergy, a gap this research aims to fill. This study is based on the hypothesis that generative AI can act as a catalyst in the DT process, optimizing its five stages—Empathize, Define, Ideate, Prototype, and Test—by providing relevant information and expanding the possibilities for analysis. Specifically, this research sought to evaluate whether the integration of AI can: i) stimulate creativity and the diversity of ideas; ii) improve team efficiency and productivity; and iii) enhance the quality and degree of innovation of the final solutions. To test this hypothesis, a mixed-method research approach was conducted, involving practical DT sessions with 101 participants. The sample was divided into four distinct experimental conditions to allow for a comparative analysis: a control group without AI (CG), an experimental group with AI (EG), individuals without AI (IC), and individuals with AI (IE). At the end of each stage, quantitative and qualitative data were collected through evaluation forms on the perception of the activity. The data analysis reveals a fundamental duality, in which the efficiency hypothesis was largely corroborated, with AI being universally perceived as a potent process accelerator. However, the results for creativity and quality are more complex, with qualitative data showing that the tool's effectiveness is critically mediated by the user's interaction skills, highlighting tensions between the agility provided by AI and challenges such as the superficiality of responses, the occurrence of "hallucinations," and the risk of dependency that can inhibit critical thinking. The research validates the potential of AI as a "creative co-pilot," whose contribution is maximized by human curation. As a result, a new DT framework with AI is proposed, which serves as a practical guide to optimize Human-AI collaboration, ensuring that empathy and strategic discernment remain at the core of the innovation process.