Solutions to the problem of deriving business processes from goalsare critical in addressing a variety of challenges facing the services and businessprocess management community, and in particular, the challenge of quickly generatinglarge numbers of effective process designs (often a bottleneck in industryscaledeployment of BPM). The problem is similar to the planning problem thathas been extensively studied in the artificial intelligence (AI) community. However,the direct application of AI planning techniques places an onerous burdenon the analyst, and has proven to be difficult in practice. We propose a practicalyet rigorous (semi-automated) algorithm for business process derivation fromgoals. Our approach relies on being able to decompose process goals to a morerefined collection of sub-goals whose ontology is aligned with that of the effectsof available tasks which can be used to construct the business process. Once processgoals are refined to this level, we are able to generate a process design usinga procedure that leverages our earlier work on semantic effect annotation of processdesigns.We illustrate our ideas throughout this paper with a real-life runningexample, and also present a proof-of-concept prototype implementation.