Recent advancements in artificial intelligence (AI) present transformative opportunities for service organizations to rethink value creation, delivery, and capture mechanisms. While extensive research has examined AI's technical capabilities,limited empirical work explores how organizational framing, readiness, and ecosystemengagement influence successful AI adoption in service contexts. This thesis investigates how AI enables business innovation in service-sector organizations and identifies the organizational and contextual conditions that influence strategic valuerealization through AI adoption.The study employs a qualitative, interpretive approach, drawing on six case studies of Austrian and Swiss service organizations across diverse sectors and AI maturity levels.The findings contribute to a novel three-dimensional framework for understanding AI enabled business innovation: Organizational Transformation, AI-Enabled Value Innovation, and Ecosystem and Environmental Context. This framework demonstrates that AI transformation unfolds through the dynamic interplay of these interdependent dimensions rather than isolated technological implementation.The empirical findings highlight the dominance of augmentation over automationlogics, with AI enhancing rather than replacing human expertise. Organizational capabilities emerged through iterative experimentation rather than linear planning,supported by governance structures that balanced local autonomy with strategic alignment. Value realization was shaped by persistent ambiguity, requiring firms torely on proxy metrics and evolving expectations rather than conventional return on investment measures. Theoretically, the study extends business model innovation literature by detailing AI’s differentiated impact on value dimensions in servicecontexts. It refines the application of dynamic capabilities in AI-driven transformations and contributes to service innovation research by showing how AI enables new forms of collaboration. From a managerial perspective, the findings underscore the importance of framing AI initiatives as capability-building efforts,prioritizing augmentation-first strategies, and designing governance mechanisms that enable both experimentation and organizational coherence.
Julian Rödiger (Wed,) studied this question.