We propose a principled risk-based framework that enables organizations or complex systems to jointly perform financial planning and human-resource management functions in an environment characterized by uncertainty in demand and supply and by other exogenous factors. This framework consists of a set of risk-based capacity planning models and methods that, first, allow for a rigorous quantification of how the financial metrics of an institution depend on its resource provisioning decisions. Second, they provide the capability to make the best decisions to achieve the financial objectives of the institution. Consequently, these models and methods enable interactions between financial and operational planning processes. Decisions including, but not limited to, investments, capacity adjustments, and demand prioritization as well as product or service pricing can all be evaluated quantitatively within this framework, and an overall optimal strategy and plan can be produced with user-specified objectives. These models and methods are based on analysis and optimization algorithms developed for large-scale stochastic loss networks.