We developed components of a decision structure that could be used in an adaptive management framework for responding to invasion of hemlock woolly adelgid Adeleges tsugae on the Cumberland Plateau of northern Tennessee. Hemlock woolly adelgid, an invasive forest pest, was first detected in this area in 2007. We used a structured decision-making process to identify and refine the management problem, objectives, and alternative management actions, and to assess consequences and tradeoffs among selected management alternatives. We identified four fundamental objectives: 1) conserve the aquatic and terrestrial riparian conservation targets, 2) protect and preserve hemlock, 3) develop and maintain adequate budget, and 4) address public concerns. We designed two prototype responses using an iterative process. By rapidly prototyping a first solution, insights were gained and shortcomings were identified, and some of these shortcomings were incorporated and corrected in the second prototype. We found that objectives were best met when management focused on early treatment of lightly to moderately infested but relatively healthy hemlock stands with biological control agent predator beetles and insect-killing fungi. Also, depending on the cost constraint, early treatment should be coupled with silvicultural management of moderately to severely infested and declining hemlock stands to accelerate conversion to nonhemlock mature forest cover. The two most valuable contributions of the structured decision-making process were 1) clarification and expansion of our objectives, and 2) application of tools to assess tradeoffs and predict consequences of alternative actions. Predicting consequences allowed us to evaluate the influence of uncertainty on the decision. For example, we found that the expected number of mature forest stands over 30 y would be increased by 4% by resolving the uncertainty regarding predator beetle effectiveness. The adaptive management framework requires further development including identifying and evaluating uncertainty, formalizing other competing predictive models, designing a monitoring program to update the predictive models, developing a process for re-evaluating the predictive models and incorporating new management technologies, and generating support for planning and implementation.