Artificial intelligence in innovation: processes and applications in the service context

• Ano de defesa: 2025
• Autor(a) principal: Crispim, Rafael Toassi
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Artificial intelligence; Geração de conhecimento; Gestão da inovação; Innovation management; Inovação de serviços; Inteligência artificial; Knowledge generation; Mudança tecnológica; Service innovation; Technological change
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/12/12139/tde-01072025-103838/

The integration of artificial intelligence (AI) into firms innovation processes is reshaping how organizations develop new products, services, and business models. However, empirical insights on AIs role in innovation remain fragmented. This doctoral thesis addresses this gap through three interrelated studies, adopting an evolutionary economics perspective to examine how AI influences innovation dynamics at the firm level. The first study systematically reviews empirical research on AI-driven innovation, identifying key organizational and innovation outcomes, such as accelerated R&D, enhanced decision-making, and business model transformation, while also noting AIs limitations in fostering creativity and radical innovation. The second study delves into how different AI types mechanical, thinking, and feeling AI are integrated across innovation stages, based on qualitative evidence from the healthcare sector. The findings indicate AIs uneven adoption, with mechanical AI being more prevalent across the innovation process, while ideation stage (front-end) faces greater challenges. The third study explores how AI-driven service innovation fosters the convergence of distinct service operational models, enabling firms to integrate efficiency-driven and customer-centric approaches. Through a multiple case study of small service firms, the research demonstrates AIs dual role in integrating automation while enhancing customization. Together, these studies contribute to the literature on AI and innovation by clarifying AIs role in managing the innovation processes, influencing firm capabilities, and redefining service operational models. The findings provide theoretical and managerial implications, guiding firms in effectively leveraging AI for innovation while addressing its inherent challenges derived from this technological change