Explainable AI in Context: Investigating How AI Use Cases Shape Stakeholders' Use in High‐Stakes Organizations

Artificial intelligence (AI) models have become increasingly complex, raising concerns among various stakeholders, particularly in high‐stakes organizations. The introduction of explainable AI tools to address these concerns has been received with mixed reactions. We investigate stakeholders' use of explainable AI tools in the transaction monitoring process for anti‐money laundering detection at a financial organization. We argue that stakeholders' proximity to the AI model leads to varying aspects of trust and hence the use of explainable AI tools across different AI use cases. Depending on their trust, stakeholders use explainable AI differently. The study contributes to the current discourse by proposing a conceptual framework for understanding the use of explainable AI tools in high‐stakes organizations and underscores the importance of use cases for stakeholders' proximity to and trust in the AI model, which, in turn, determine the use of explainable AI tools.