Artificial intelligence (AI) is transforming decision-making in various organizational settings by supporting human decision-makers. However, although several AI-augmented decision-making applications occur in multi-decision settings, current literature treats AI reliance in those settings as purely influenced by single-decision level variables, fully neglecting factors specific to the multi-decision setting. Yet, understanding such factors is crucial, as reliance in single decisions might be influenced by the overall cognitive experience with the task and the AI tool. In particular, the AI-augmented decision-making literature has overlooked two cognitive factors specific to multi-decision settings – i.e., cognitive load stemming from the task-level workload and cognitive dissonance from a task-level disagreement with AI in the multi-decision session. We develop a Cognitive Multi-Decision AI-Augmentation Theory of AI reliance in multi-decision settings and theorize that a higher task-level workload increases AI reliance, whereas experiencing higher task-level disagreement with AI during the multi-decision session reduces AI reliance in single decisions. We test our theoretical framework in two multi-decision innovation selection contexts by conducting a lab experiment with 53 young business professionals, a field experiment with 88 managers in one firm, and 20 interviews to contextualize our results. We explore the findings´ implications for theories of AI reliance and organizational practice.
