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We study the business-driven prioritization of workflows in enterprise transaction processing systems. Today, IT services are increasingly shared amongst workflows, and static prioritization is applied during phases of high system workload to mitigate the business impact of service queuing and resulting workflow response-time increase. Static prioritization rules can only be optimal for one particular workflow definition and demand mix scenario, while in practice workflow definitions are continuously re-engineered and demand varies over time. We propose a technique to automatically adapt workflow priorities aimed at minimizing overload-related costs resulting from workflow delays. We continuously align maximum service buffer lengths and bid-prices reflecting current marginal opportunity costs of invoking a shared service. New prioritization is derived by comparing expected costs when queuing a workflow with its current opportunity costs, computed as the sum of bid-prices of invoked services. Simulation outcomes based on industry data illustrate that the approach dominates standard queuing disciplines as well as static prioritization.