<p>Algorithmic recommendations have drastically expanded in recent years to aid human decision-making. In this paper, we seek to understand the users of these tools and when, where, and why they obtain algorithmic advice. We do so examining data from two behavioural decision-making experiments (<i>N</i> = 216) and applying the Timed Racing Diffusion Model (TRDM) across choices and response times. Our experiments find that people are sensitive to when algorithmic advice is worthwhile obtaining. Notably, our results privilege experience and show that opportunities to test the recommendation accuracy can be as useful as descriptive information stating the same. Our main finding, however, centers on the time-course of when individuals choose to obtain a recommendation. We find that over time, algorithmic advice is sought as a means to terminate difficult decisions that one cannot derive on one’s own. The TRDM proposes a unifying cognitive mechanism for this pattern of recommendation seeking based on decision urgency though our individual differences analyses identify a diversity of strategies adapted to the same decision environment. Overall, our findings characterise decision-makers as adept users of decision aid tools, and that despite the possibility of recommendation errors, individuals are capable of appreciating the utility of helpful, albeit imperfect, recommendations.</p>