Navigating the Generative AI Adoption Dilemma: Pathways and Trade-offs through a Data Ecosystem Perspective

Abstract

The rapid rise of Generative AI (GenAI) chatbots such as ChatGPT and Claude presents organizations with a strategic dilemma: adopt powerful external solutions or invest in developing secure, internal alternatives. This study conceptualizes the "GenAI adoption dilemma" and examines it through the lens of internal data ecosystems. Based on qualitative case study research, including 46 interviews with senior professionals in a multinational consultancy firm, we identify three strategic pathways: internal, external, and hybrid chatbot adoption. We analyze them across five criteria: cost, risk, time to market, data ecosystem integration, and competitiveness. We develop a framework to guide decision-makers in navigating this emerging challenge. Our findings primarily contribute to GenAI chatbot adoption literature by framing it as a systemic organizational decision rather than a purely technological, they extend data ecosystem theory by empirically linking GenAI chatbots adoption to internal infrastructure, governance, stakeholders, and data practices, and provide insights for IT adoption and outsourcing literature.

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