As artificial intelligence (AI) tools have become pervasive in business applications, so too have interactions between AI and humans in business processes and decisionmaking. A growing area of research has focused on human decision and task delegation to AI assistants. Simultaneously, extensive research on algorithm aversion-humans' resistance to algorithm-based decision tools-has demonstrated potential barriers and issues with AI applications in business. In this paper, we test a simple strategy for mitigating algorithm aversion in the context of AI task delegation. We show that simply changing the framing of decision tasks can allay algorithm aversion. Through multiple studies, we found that participants exhibited a strong preference for human assistance over AI assistance when they were rewarded for task performance (i.e., money was gained for good performance), even when the AI had been shown to outperform the human assistant on the task. Alternatively, when we reframed the task such that the participant experienced losses for poor performance (i.e., money was taken from their endowment for poor performance), the bias for preferring human assistance was removed. Under loss framing, participants delegated the decision task to human and AI assistants at similar rates. We demonstrate this finding across tasks at differing levels of complexity and at different incentive sizes. We also provide evidence that loss framing increases situational awareness, which drives the observed effects. Our results offer useful insights on reducing algorithm aversion that extend the literature and provide actionable suggestions for practitioners and managers.