Decision theory has become widely accepted in the AI community as a useful framework for planning and decision making. Applying the framework typically requires elicitation of some form of probability and utility information. While much work in AI has focused on providing representations and tools for elicitation of probabilities, relatively little work has addressed the elicitation of utility models. This imbalance is not particularly justified considering that probability models are relatively stable across problem instances, while utility models may be different for each instance. Spending large amounts of time on elicitation can be undesirable for interactive systems used in low-stakes decision making and in time-critical decision making. I will discuss the issues involved in reasoning with incomplete utility models. I identify patterns of problem instances where plans can be proven to be suboptimal if the (unknown) utility function satisfies certain conditions. I present an approach to planning and decision making that performs the utility elicitation incrementally and in a way that is informed by the domain model.