Engineers working on tasks in technology-intensive project environments face substantial task resolution uncertainty. This can result in poor capacity utilization as they expend effort on tasks that are not successfully resolved. A deeper understanding of task-related uncertainty can help the firm optimize effort allocation across tasks by implementing well-designed task closure policies that facilitate superior capacity utilization and capacity planning. In this article, we characterize the empirical distribution of task uncertainty and demonstrate how the resolution process affects task outcomes in project environments. Using a combination of empirical estimation, and analytical and simulation modeling, we develop insights into task-related decision-making and engineer effort allocation. Using real-world task resolution data from a software maintenance setting, we first model and estimate a beta-geometric survival distribution which indicates that the likelihood of successful task resolution substantially reduces with the time a task remains in the system. Using analytical and simulation modeling, we then examine the implications of imposing task closure policies to improve engineer effort allocation and increase system productivity. We demonstrate that adopting well-designed task closure policies can significantly improve engineer resource utilization in capacity-constrained settings without a substantial negative impact on project outcomes. We discuss the implications of our research for theory and practice.