AI Adoption in Research & Innovation : Implementation of AI to reduce administrative workand better utilize existing information

Sammanfattning

Generative Artificial Intelligence is being explored across industries and is beginning to transform knowledge workflows in different sectors. At Research & Innovation departments dealing with complex documentation and intensive collaboration, the need to reduce administrative workload and enhance the accessibility of knowledge in the portfolio has become more relevant. While generative AI may provide substantial benefits in enhancing productivity, operational efficiency and knowledge access, these outcomes are context-dependent and remain uneven across organizations. Nowadays, effective AI adoption can be considered a major challenge for some long-established companies from the Technology Adoption and Organizational Change Management perspective. Therefore, understanding the challenges and barriers to implementing generative AI in such settings becomes highly relevant. Although previous research has explored the theoretical underpinnings of AI adoption in administrative contexts, empirical evidence from industrial research & innovation environments remains limited. To address this gap, this thesis formulates the following research questions: RQ1 – What individual and structural factors are influencing AI adoption in Research & Innovation offices of long-established industrial organizations?RQ2 – What are the challenges and best practices in implementing AI-based administrative solutions in such organizational contexts? Through a qualitative, interpretivist case study in Scania’s Research & Innovation Office, the research draws on interviews, five months of ethnographic fieldwork from January to June 2025, and iterative Design Science Research to develop custom GPTs and Retrieval Augmented Generation (RAG) solutions, this Master Thesis investigates how generative Artificial Intelligence can reduce administrative workload and enhance knowledge accessibility within research & innovation contexts of large industrial organizations. With an interdisciplinary nature, the research integrates perspectives from Technology Adoption and Organizational Change Management. This thesis contributes to the body of knowledge on digital transformation in industrial research and innovation settings by presenting a conceptual framework to assess AI adoption maturity through empirical indicators. The findings of this study provide both theoretical insights and practical guidance for AI adoption and illustrate the potential value, as well as the limitations, of technology-based intrapreneurship and AI-driven innovation in industrial contexts.