Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of evolutionary multitasking (EMT) fills this gap. It unlocks a population's implicit parallelism to jointly solve a set of tasks, hence creating avenues for skills transfer between them. Despite it being early days, the idea of EMT has begun to show promise in a range of real-world applications. In the backdrop of recent advances, the contribution of this paper is twofold. First, we present a review of several application-oriented explorations of EMT in the literature, assimilating them into half a dozen broad categories according to their respective application areas. Each category elaborates fundamental motivations to multitask, and contains a representative experimental study (referred from the literature). Second, we present a set of recipes by which general problem formulations of practical interest, those that cut across different disciplines, could be transformed in the new light of EMT. We intend our discussions to underscore the practical utility of existing EMT methods, and spark future research toward novel algorithms crafted for real-world deployment.